http://2007.igem.org/wiki/index.php?title=Special:Contributions/Rach&feed=atom&limit=50&target=Rach&year=&month=2007.igem.org - User contributions [en]2024-03-28T22:40:13ZFrom 2007.igem.orgMediaWiki 1.16.5http://2007.igem.org/wiki/index.php/Glasgow/ModelingGlasgow/Modeling2007-10-26T12:33:48Z<p>Rach: /* Stochastic Modelling */</p>
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<br />
= Summary =<br />
Synthetic biology has been used to describe an approach to biology<br />
which attempts to design and construct deliberate biological systems<br />
that can be investigated experimentally, which are otherwise very<br />
expensive and practically challenging. One of the central features<br />
of synthetic biology is the appreciation of the knowledge from<br />
science and engineering disciplines for the better design and<br />
understanding of synthetic networks. Here we have engineered a<br />
bacterial biosensor with the involvement of the construction of two<br />
new reporter genes PhzM and PhzS to detect polluting<br />
chemicals, which has the potential to provide an inexpensive and<br />
easy-to-use method of detecting industrial pollution. We explored<br />
a variety of computational approaches to study the behaviour of<br />
three synthetic systems: simple reporter system, positive feedback<br />
reporter system and '''[...]'''. We developed deterministic<br />
and stochastic models that quantitatively describe the graded<br />
signal-response property of the simple reporter system, and also<br />
showed that models can be expanded and used to qualitatively predict<br />
the ''in vivo'' behaviour of the complicated systems. The dynamics<br />
is further studied via the application of qualitative modelling<br />
methods. Simulations reveal that the model with positive feedback<br />
loop has higher output level than that from the intact model '''[...]''' This work shows that by integrating engineering<br />
techniques with scientific methodologies, we can gain a new insights<br />
into the genetic regulation and should become the reference framework<br />
for the design and construction of biochemical networks in synthetic<br />
biology.<br />
<br />
= Framework =<br />
We have used a framework which unifies the qualitative,<br />
stochastic and continuous worlds, as a basis for our overall approach to<br />
modelling<br />
and analysing the biochemical pathways.<br />
Each perspective adds its<br />
contribution to the understanding of the system, thus the three<br />
approaches do not compete, but complement each other. <br />
Qualitative descriptions are abstractions over<br />
stochastic or continuous descriptions, and the stochastic<br />
and continuous models approximate each other. <br />
Note: this framework is based on <br />
(Gilbert et al 2007).<br />
<br />
Our overall framework is illustrated in<br />
Figure 1 that relates the three major ways of<br />
modelling and analysing biochemical networks that we have used:<br />
qualitative, stochastic and continuous.<br />
<br />
The most abstract representation of a biochemical network is <br />
qualitative and is minimally described by its topology. Initial descriptions<br />
can be obtained from biochemists, and are often in some semiformal<br />
representation. These can easily be transformed into<br />
a formal description at this stage which is usually<br />
a bipartite directed graph with nodes representing biochemical<br />
entities or reactions, or in Petri net terminology places<br />
and transitions - see [https://2007.igem.org/Glasgow/Modeling#Petri_Net_Modelling Petri net section]'''. <br />
<center>[[Image: FrameworkSlide.png |frame| Figure 1. Conceptual modelling framework]]</center><br />
The qualitative description can be further enhanced by the abstract<br />
representation of discrete quantities of species, achieved in Petri<br />
nets by the use of tokens at places. These can represent the number<br />
of molecules, or the level of concentration, of a species. A<br />
particular arrangement of tokens over a network is called a <br />
marking. The standard semantics for these qualitative Petri<br />
nets (QPN) does not associate a time with transitions or the sojourn<br />
of tokens at places, and thus these descriptions are time-free.<br />
The qualitative analysis considers however all possible behaviour<br />
of the system under any timing. <br />
The behaviour of such a net forms a discrete state<br />
space. <br />
<br />
Timed information can be added to the qualitative description in<br />
two ways -- stochastic and continuous. <br />
The continuous model replaces the discrete values of species with<br />
continuous values, and hence is not able to describe the behaviour<br />
of species at the level of individual molecules, but only the overall<br />
behaviour via concentrations. We can regard the discrete description<br />
of concentration levels as abstracting over the continuous description<br />
of concentrations. Timed information is introduced by the association<br />
of a particular deterministic rate information with each transition,<br />
permitting the continuous model to be represented as a set of<br />
ordinary differential equations (ODEs) - see [https://2007.igem.org/Glasgow/Modeling#Model_Evolution Model Evolution]. <br />
The concentration of a<br />
particular species in such a model will have the same value at each<br />
point of time for repeated experiments. The state space of such<br />
models is continuous and linear. <br />
It is also possible to linearise ODE descriptions, by for example Laplace transforms, in an attempt to increase modularity in the system description and hence to facilitate model construction - see [[Glasgow/Modeling#BioBrick_library |BioBrick library]]. This approach results in transformations from the time-domain, in which inputs and outputs are functions of time, to the frequency-domain.<br />
<br />
The stochastic Petri net (SPN) description preserves the discrete state<br />
description,<br />
but in addition associates a particular stochastic rate information<br />
with each reaction, permitting the stochastic model to be regarded as a <br />
Markov process with a discrete state space.<br />
The time-evolution of such a process can be described by <br />
the Chemical Master Equation (CME), which is equivalent to Kolmorogov's forward equation<br />
(Wilkinson 2006).<br />
It is quite straightforward to simulate such a system, and this is usually<br />
done with the standard discrete event simulation procedure known as "the<br />
Gillespie algorithm" (Gillespie, 1977) - see [[Glasgow/Modeling#Stochastic_Modelling |Stochastic Modelling]].<br />
The QPN is an abstraction of<br />
the SPN, sharing the same state space and transition relation with<br />
the stochastic model, with the probabilistic information removed.<br />
All qualitative properties valid in the QPN are also valid in the<br />
SPN, and vice versa.<br />
<br />
<br />
In summary, the qualitative<br />
time-free description is the most basic, with discrete values<br />
representing numbers of molecules or levels of concentrations.<br />
The qualitative description abstracts over two timed, quantitative models.<br />
In the stochastic description, discrete values for the amounts of species<br />
are retained, but a stochastic rate is associated with each reaction. The continuous model describes amounts of species using continuous values<br />
and associates a deterministic rate with each reaction.<br />
These two time-dependent models can be mutually approximated by hazard<br />
functions belonging to the stochastic world.<br />
<br />
'''References'''<br />
<br />
D. Gilbert, M. Heiner and S. Lehrack (2007). [[Media:Edinburgh_proceedings.pdf | "A Unifying<br />
Framework for Modelling and Analysing Biochemical Pathways Using Petri Nets"]]. In<br />
proceedings Computational Methods in Systems Biology CMSB 2007 (Computational Methods in Systems Biology),<br />
Springer-Verlag LNCS/LNBI Volume 4695, pp. 200-216.<br />
<br />
D.T. Gillespie (1977). Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry, 1977. 81:25 pp 2340-2361<br />
<br />
D.J. Wilkinson (2006). Stochastic modelling for systems biology. Chapman & Hall / CRC.<br />
<br />
= Model design and analysis: outline=<br />
<br />
[[Image: Glasgow_design_small.png|frame|Figure 2. Design of our system. The intermediate compound is 5-<br />
methylphenazine-1-carboxylic acid betaine.]]<br />
The biochemists constructed an initial diagram to describe the system, using a fairly informal graphical syntax. This used a generic form of the transcription factor ('tf' for the gene, and 'TF' for the protein product) which represented both XylR (BTEX detecting) and DntR (Salicylate detecting). In outline, the steps that we used to develop and refine our model were:<br />
<br />
# Simplification by abstracting away the mRNA, thus combining transcription and translation. <br />
# Combining the PhzM and PhzS components to give one step from PCA to PYO<br />
# We also developed a variant of the model with a positive feedback loop <br />
<br />
<br />
<br />
The descriptions (with and without the positive feedback loop) were then transformed into Qualitative Petri Nets (QPN) - see [https://2007.igem.org/Glasgow/Modeling#Petri_Net_Modelling Petri net section].<br />
We derived the [https://2007.igem.org/Glasgow/Modeling#Parameter_searching_and_refinement rate parameters], and wrote down descriptions and simulated the models in <br />
[https://2007.igem.org/Glasgow/Modeling#Model_design:_detailed Ordinary Differential Equations], as well as in [https://2007.igem.org/Glasgow/Modeling#Petri_Net_Modelling Continuous Petri Nets] (CPN). We performed <br />
model analysis to refine the rate parameters using [https://2007.igem.org/Glasgow/Modeling#Minicap_Sensitivity_Analysis_Program_Package our implementation of the the MPSA algorithm] and [https://2007.igem.org/Glasgow/Modeling#Comparative_Model_Analysis model comparison]. <br />
Simulation of the continuous model was performed in MatLab and [http://www.bionessie.org BioNessie].<br />
Finally we constructed and simulated <br />
[https://2007.igem.org/Glasgow/Modeling#Stochastic_Modelling stochastic models] in MatLab and <br />
[https://2007.igem.org/Glasgow/Modeling#Stochastic_Modelling_vs._ODE_Modelling compared the results] of from the continuous (ODE-based) and stochastic approaches.<br />
<br />
A more detailed description of the model design process is as follows:<br />
<br />
= Model design: detailed=<br />
[[Image: Glasgow_simple_small.png|frame|Figure 3. The simple model]]<br />
The basic design of our system is shown in Figure 2. The sensing protein TF is produced constitutively. TF stands for one of the sensing proteins that were used in the implemented system - DntR or XylR. TF that is always present in the system binds the signal (pollutant) compound. The TF|S complex promotes expression of the PhzM and PhzS protein coding regions. These proteins catalyse transformation of PCA compound that is available in the system into pyocyanin (PYO).<br />
<br />
Two slightly different designs of our system were investigated in the course of the project. The latter is a modification that includes a positive feedback loop in order to enhance system's response to the signal.<br />
<br />
== Simple model ==<br />
<br />
In our modelling effort we have simplified the representation of our design to ease modelling. Firstly, we have omitted the intermediate mRNA production<br />
and represented gene expression in one step instead. The resulting<br />
model contains less parameters, thus is easier to analyse. Also, there are less<br />
parameters that need to be found or estimate. In fact, gene expression rate<br />
is often measured disregarding mRNA production.<br />
<br />
Production of MPCAB (our working name for 5-methylphenazine-1-carboxylic<br />
acid betaine - the intermediate compound) has been dropped as well. A study<br />
which aimed to characterise this part of the pathway [5] revealed that it is<br />
very hard to characterise the PCA -> MPCAB and MPCAB -> PYO<br />
reactions separately. This is probably due to instability of MPCAB. The<br />
composite reaction PCA -> PYO was characterised instead. Therefore, the<br />
MPCAB has been completely removed from the model and the PhzM and<br />
PhzS proteins have been joined together into PhzMS.<br />
<br />
The basic model is shown on the Figure 3. The equations we have developed for it are shown below.<br />
<br />
[[Image: Equations1_wiki.png|center]]<br />
<br />
TF protein is produced with constant rate alpha_TF and degrades at rate delta_TF. It also binds to s (pollutant) compounds with rate beta_TFS and unbinds from it with rate k_d.<br />
<br />
The first two terms in the TFS equation are equivalent to the two last in the TF equation. The complex also degrades with rate delta_TFS.<br />
<br />
We have used Michealis-Mented kinetics to represent production of PhzMS. This protein degrades with rate delta_PhzMS.<br />
<br />
Pyocyanin (PYO) is produced depending on the concentration of PhzMS. It degrades with rate delta_PYO.<br />
<br />
== Positive Feedback Model ==<br />
<br />
[[Image: Glasgow_feedback_small.png|frame| Figure 4. Model with positive feedback]]<br />
<br />
The positive feedback model (Figure 4) includes additional coding region for TF protein placed after PhzMS coding. Once the pollutant is present and expression of reporter proteins is started also more TF is produced. Our assumption was that increased concentration of TF will cause more pollutant molecules to be bound into TF|S complex and, in turn, expression at the TF|S promoter would be enhanced further. The equations we have developed for the positive feedback loop model are as follows.<br />
<br />
[[Image: Equations2_wiki.png|center]]<br />
<br />
The only difference between simple model equations and feedback model equations is the term outlined in orange. It represents the additional production of TF. <br />
<br />
== Simulation ==<br />
[[Image: Glasgow_comparison_plot.png|frame| Figure 5. Comparison of responses of the simple model and feedback loop model (bold line). Pyocyanin curve is light blue, PhzMS curve is red.]]<br />
We have been interested how outputs of the two models would differ. Using the most accurate parameter values we could find (see parameter section) and signal concentration of 5uM the two models have been simulated and compared (Figure 5). The version with positive feedback loop gave a sharper and stronger response.<br />
<br />
To see the full PDF report [[https://static.igem.org/mediawiki/2007/a/ad/Glasgow_modelling.pdf click here]].<br />
<br />
= Petri Net Modelling =<br />
[[Image: F5again1 readArc nofb coloured notable names.png|400px|right]]<br />
[http://en.wikipedia.org/wiki/Petri_nets Petri nets] are used to describe discrete distributed systems that <br />
have concurrent processes, and provide users with an intuitive<br />
and easy-to-understand graphical representation of systems. During<br />
the iGEM project, we explored the use of Petri nets for the construction <br />
and analysis of synthetic biological networks, with a focus on facilitating the experimental design. We started off<br />
with the construction of ''qualitative'' petri-net models using the [http://www-dssz.informatik.tu-cottbus.de/index.html?/software/snoopy.html Snoopy] tool.<br />
A major advantage of the Qualitative Petri net (QPN) approach is that it enables us to perform network<br />
analysis in the absence of prior knowledge of the system parameters.<br />
We achieved this using the Charlie tool (available from [http://www-dssz.informatik.tu-cottbus.de/index.html?/software/tutorial_biopn.html Monika Heiner], where we identified the<br />
subsets of the Petri net models covered by certain properties with<br />
T-invariants (cyclic behaviour) and P-invariants (constant output<br />
in amount). We also studied various suggestions for the system's<br />
possible structures using the ''token game'' with different initial markings. Based on the observation<br />
of possible flows and discussions with biochemists, we examined and<br />
validated the models in terms of the boundedness, for example.<br />
Although Petri Nets were initially designed for qualitative analysis<br />
they have been extended to permit other forms of analysis including<br />
the quantitative form which is known as a Continuous Petri Net (CPN) which has an ODE-based semantics (see [https://2007.igem.org/Glasgow/Modeling#Framework framework]). We<br />
investigated the dynamics of the systems quantitatively using<br />
the parameters retrieved from the literature (for details, please<br />
refer to the section on Parameter Searching), and simulated the behaviour using [http://www-dssz.informatik.tu-cottbus.de/index.html?/software/snoopy.html Snoopy] which can handle continuous Petri nets and has<br />
inbuilt ODE solvers.<br />
These results were consistent with those<br />
generated by the pure [https://2007.igem.org/Glasgow/Modeling#Model_Evolution ODE approach].<br />
<br />
See the full [http://compbio.dcs.gla.ac.uk/iGEM2007/petrinets_report.pdf Petri net report].<br />
<br />
= Parameter searching and refinement =<br />
<br />
<br />
Parameter searching using Google Scholar, PubMed and biomedical databases <br />
was carried out by Christine and Xu under the direction of Dr David Leader. The<br />
task was largely steered by the discussions with, and the suggestions<br />
given by Emma Travis. We applied the [https://2007.igem.org/Glasgow/Modeling#Minicap_Sensitivity_Analysis_Program_Package Minicap program], and [https://2007.igem.org/Glasgow/Modeling#Comparative_Model_Analysis model comparison], both developed as part of the project, in order to help us identify sensible ranges associated with the parameters. A<br />
complete table containing either exact or constrained values for<br />
each of the system parameters was produced, as shown in the [Parameters.png parameter<br />
table], where parameters are annotated with their type,<br />
values and supporting data sources.<br />
<br />
[[Image: Glasgow_Constants.png|center]]<br />
<br />
<br />
== Minicap Sensitivity Analysis Program Package ==<br />
The Multi-Parametric and Initial Concentration Sensitivity analysis package is a Matlab function which executes a chosen Dynamic or Stochastic ''System Function'' for a defined number of different variable values across any desired range. The ''subject'' of analysis can either be constants in the user's system eg. parameters in a biological system (MPSA) or initial values of the variables in the user's system eg. initial substrate concentrations (ISCSA). The program will output a plot for each variable showing a comparison of ''acceptable'' and ''unacceptable'' samples across the subject's range with 3 calculated quantitative comparison figures: the ''Correlation Coefficient'', the ''Area'' between acceptable and unacceptable curves, and the ''Standard Deviation of the gradient'' of the acceptable plot. The comparative and intrinsic sensitivity of each chosen subject is thus highlighted. A plot showing the trend of the ''Substrate of Interest'' over time is also displayed. <br />
<br />
As well as this report, the Minicap package contains a User Manual in html, a number of example codes and all the novel (i.e. not ode15s) function and text files required to run Minicap.<br />
<br />
Downloads:<br />
<br />
The MatLab code is available as [http://compbio.dcs.gla.ac.uk/iGEM2007/Minicap_Sensitivity_Analysis_Program_Package.zip server (Minicap)]. <br />
To see the full PDF report on Minicap [[https://static.igem.org/mediawiki/2007/f/fb/Minicap.pdf click here]].<br />
<br />
== Comparative Model Analysis ==<br />
[[Image: Glasgow_Comparison.png|400px|right]]<br />
In our modelling efforts we were focused on comparing the two models. In order to compare them reliably fine parameter values are needed. In order to drive the process of refining the parameter values we have developed a method which is a simple modification of the Multi-Parametric Sensitivity Analysis.<br />
<br />
In standard MPSA a baseline system output (from experimental data or from simulation of a model for given parameter values) is compared with model outputs for different parameter values drawn from specified parameter space. <br />
<br />
In our modification two different models are compared for each set of parameter values from the parameter space.<br />
<br />
= Stochastic Modelling =<br />
<br />
[[Image: Stochastic1.png|frame|Figure 9. Stochastic simulation. 10 runs for one cell]]<br />
[[Image: Stochastic2.png|frame|Figure 10. Standard deviation of the parameter gamma_PhzMS]]<br />
<br />
A stochastic model for a general biosensor was constructed by students and simulated using Gillespie algorithm in MATLAB. Modelling biological systems in terms of ordinary differential equations is often not enough as cells are intrinsically noisy due to low numbers of molecules that participate in reactions. This can lead to significant statistical fluctuations in reaction rates and, the systems behaviour can differ from that predicted by a deterministic model. In this case, the system of interest was a single cell or bacteria of a bacterial whole cell biosensor.[1]<br />
<br />
In this study the concept of stochastic simulation was introduced using a computational approach called Gillespie algorithm. The Gillespie algorithm allows a discrete and stochastic simulation with few reactants because every reaction is individually simulated. It takes into account a number of parameters that contribute to the model in a random manner, rather than assuming everything can be predicted.<br />
The goal of this study was to construct a stochastic model based on a deterministic one and to investigate the amount of noise (measured by the Fano factor and the coefficient of variation). Simulations were performed for several of cells and repeated for many runs to find representative behaviour. Noise was expected to decrease as the number of cells increased. However, in this study it was found that the coefficient of variation stayed roughly the same as the number of cells increased.[2]<br />
<br />
We also aimed to study whether generic features predicted by other modelling approaches can be repeated by stochastic simulations. The effects of leakiness of the promoter was also observed in the system. Leakiness had a detrimental effect on the reliability of the system. It provides a higher output of the protein which in turn gives a false indication of the level of signal being detected. <br><br />
[[Media:GlasgowStochasticModellingOct07.pdf|Full report on stochastic modelling]]<br><br />
References<br><br />
[1]Intrinsic noise in gene regulatory networks-Thattai and van oudenaarden<br><br />
[2]Wikipedia Gillespie Algorithm-http://en.wikipedia.org/wiki/Gillespie_algorithm<br />
<br />
= Stochastic Modelling vs. ODE Modelling = <br />
<br />
~~ the four pictures here ~~<br />
<br />
= BioBrick library =<br />
[[Image:BioBricks.JPG|right|400px]]<br />
<br />
BioBrickLibrary is an add on Library to Simulink for modelling dynamical biological systems at Brick (Gene) level. Simulink is a program dedicated for dynamical system simulation, however in depth knowledge of dynamics is needed if one is to simulate system mentioned above. The BioBrick library has all the blocks as well as GUI (Graphical User Interface) needed to do the job without understanding how Simulink works. It uses drag and drop system and shares all constants in Matlab’s .m file, so it is easy to store and update them.<br />
<br />
BioBrick library’s main aim is to tell whether and how different topology will influence the output of the system. If actual rate constants are known it can be used instead or as complimentary to ODE modelling. However it must be noted that ODE rate constants ARE NOT TRANSFERABLE to BioBrick library. <br />
<br />
Downloads:<br />
<br />
The software is available as [http://compbio.dcs.gla.ac.uk/iGEM2007/BioBricklibrary.zip BioBrickLibrary.zip].<br />
More information about the package can be found in [[Media:ElectrEcoBluSimulinkManual.pdf|ElectrEcoBluSimulinkManual]] document.<br />
See full [[Media:Report.pdf|Technical Report (''BioBrick Type Modeling'')]] for concept evolution and justification.<br />
<br />
= Microbial Fuel Cell Evaluation =<br />
[[Image:MFC.jpg|right|150px]]<br />
During the course of the project some introductory work has been done with microbial fuel cells in order to prepare us for the envisaged final stage of the project. We have had three fuel cells at our disposition supplied by the UK's NCBE. The experience we have gained and some of the results have been aggregated in this short work. <br />
<br />
= Download Models and Software =<br />
<br />
== Model files ==<br />
<br />
'''Qualitative''':<br />
<br />
Qualitative Petri net : [http://compbio.dcs.gla.ac.uk/iGEM2007/QPN_simple.spped Simple model] and<br />
[http://compbio.dcs.gla.ac.uk/iGEM2007/QPN_feedback.spped Feedback version].<br />
<br />
'''Continuous''': <br />
<br />
Continuous Petri net : [http://compbio.dcs.gla.ac.uk/iGEM2007/CPN_simple.spcontped Simple model] and<br />
[http://compbio.dcs.gla.ac.uk/iGEM2007/CPN_feedback.spcontped Feedback version].<br />
<br />
SBML : [http://compbio.dcs.gla.ac.uk/iGEM2007/iGEM_SimpleReporter.xml Simple model] and [http://compbio.dcs.gla.ac.uk/iGEM2007/iGEM_PositiveFeedbackReporter.xml Feedback version].<br />
<br />
Matlab : [http://compbio.dcs.gla.ac.uk/iGEM2007/ODE_Basic.zip Simple model] and [http://compbio.dcs.gla.ac.uk/iGEM2007/ODE_Feedback Feedback version].<br />
<br />
'''Stochastic''': <br />
<br />
Matlab: [http://compbio.dcs.gla.ac.uk/iGEM2007/stochastic_code.zip stochastic code library]<br />
<br />
== Software ==<br />
<br />
Minicap program: [http://compbio.dcs.gla.ac.uk/iGEM2007/Minicap_Sensitivity_Analysis_Program_Package.zip matLab code], and [https://static.igem.org/mediawiki/2007/f/fb/Minicap.pdf report].<br />
<br />
BioBribrick library: [http://compbio.dcs.gla.ac.uk/iGEM2007/BioBricklibrary.zip software], <br />
[[Media:ElectrEcoBluSimulinkManual.pdf|ElectrEcoBluSimulinkManual]], and <br />
[[Media:Report.pdf|Technical Report (''BioBrick Type Modeling'')]].<br />
<br />
BioNessie: [http://www.bionessie.org package] (a biochemical network editor, simulator and analyser for SBML descriptions, developed at the Bioinformatics Research Centre Glasgow, outwith the iGEM project).<br />
<br />
Snoopy: [http://www-dssz.informatik.tu-cottbus.de/index.html?/software/snoopy.html package] (A Petri net tool, developed at the Brandenburg university of technology Cottbus, outwith the iGEM project).<br />
<br />
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|}</div>Rachhttp://2007.igem.org/wiki/index.php/Glasgow/InterviewsGlasgow/Interviews2007-10-25T20:32:05Z<p>Rach: /* Rachael Fulton */</p>
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== 59 Second Interviews ==<br />
<br />
=== Toby Friend ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
A but achey and sneezy, but I'll get over it.<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
A number of reasons: to take part in cutting edge research (the money); to improve my knowledge of Genetics (my bank balance); do something constructive (earn some money).<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
see 'How and why...'<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
Deleting Spam from my email account.<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
AI baby!<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
hackey-sack in the sun!<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
AI baby!<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Reading some books and tidying my neglected London bedroom.<br />
<li><br />
'''Tell us a secret.'''<br><br />
ok, but this is between everyone in the world with internet access and me!...<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
My name is Toby Friend<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
My name is Toby Friend,<br />
I said MY NAME IS TOBY FRIEND,<br />
Actually it's Louis Sanchez Fernando<br />
</ol><br />
<br />
=== Rachael Fulton ===<br />
#'''How are you today?'''<br><br />
fantastic<br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
I thought it wouid be a great experience and wanted to learn and do something that would be very interesting for the summer<br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
The ability to work with people from different disciplines which i think is a completely unique oppertunity. I also learnt alot about biological modelling which i now would like to study further<br />
#'''Where do you see yourself two years from now?'''<br><br />
Hopefully doing my Phd<br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
AHHHHH decisions decisions actually £50,000 wouldn't go that far in science i should say invest it in curing cancer or something but i would probably put it into some programme to get kids interested in science<br />
#'''What has been your favourite iGEM moment so far?'''<br><br />
The international food night christines irish stew was amazing<br />
#'''If you could be the inventor of anything, what would it be?'''<br><br />
Shinyness i mean lets be honest shiny things are amazing<br />
#'''What are you doing this weekend?'''<br><br />
working which is rubbish and taking my wee cousin to the cinema cos i have no life.<br />
#'''Tell us a secret.'''<br><br />
I see dead people<br />
#'''Describe yourself in five words.'''<br><br />
loud, stressed, small, dizzy, annoying<br />
<br />
#'''Make up a haiku on the spot.'''<br><br />
is that 5,7,5? god i am so culturally inept ok here goes<br><br />
I have a bad cat<br><br />
his name is archimedes<br><br />
he just scratched my leg<br><br />
<br />
=== Christine Harkness ===<br />
#'''How are you today?'''<br><br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
#'''Where do you see yourself two years from now?'''<br><br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
#'''What has been your favourite iGEM moment so far?'''<br><br />
#'''If you could be the inventor of anything, what would it be?'''<br><br />
#'''What are you doing this weekend?'''<br><br />
#'''Tell us a secret.'''<br><br />
#'''Describe yourself in five words.'''<br><br />
#'''Make up a haiku on the spot.'''<br><br />
=== Mai-Britt Jensen ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
I'm alright, you?<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
How? I got asked to come along to a meeting. Why? So my brain wouldn't dry out over the summer.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
Good times. Good banter. I don't know. New techniques.<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
In my third year of my PhD.<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
I would make science more accessible to the general public. I'd put more money into the media's prtrayal of science and help the public's understanding of it because there are so many misconceptions out there.<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
When it works!<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
A robot to pour plates.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Going to a Hallowe'en party and watching some scary movies.<br />
<li><br />
'''Tell us a secret.'''<br><br />
I find multichannel pipettes sexy.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
Curly. Danish. Odd. Green and Red!<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
What is a haiku?<br><br />
I'm not good with syllables.<br><br />
Is this good enough?<br><br />
</ol><br />
<br />
=== Karolis Kidykas ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
Fine Thanks!<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
I found a leaflet advertising iGEM competition in one of my lectures. It was very appealing offer. I was very interested in biology while at school but engineering won my sympathies back then. It was an opportunity to go into field I am interested in but has little to do with aerospace just before I graduate and submerge myself into professional life.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
I always thought about any form of life as of a complex machine which we will be able to control one day. This project proved it to me that I was right. Of course I may not be alive to whiteness it!<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
Airbus? ESA? I love Europe, but if bad luck follows me I will consider Boeing or NASA!<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
Probably not in Biology! Sorry! I have an engineers blood<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
I could easily name the scariest one, but its hard to think of any favorite one. There were quite a few of them. <br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
Antigravity Engine! Though I know it is probably impossible, but you said anything.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Either one of four: Tennis, Travelling, Working on my project, partying <br />
<li><br />
'''Tell us a secret.'''<br><br />
It will no longer be a secret then.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
That would be harder then to push a camel through a needle hole.<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
Sorry, times up!<br />
</ol><br />
<br />
=== Martina Marbà ===<br />
#'''How are you today?'''<br> Fine!! Thanks..<br><br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br> Firstly, because one day I chose Glasgow for my next stop of my way.<br> Secondly, because I found amazing to be involved in this new and attractive mixture of science for the study of synthetic biology. <br> <br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br> Wow...!! Lots of things!! First, how to work in a team (fact that usually maths or stats students we don't know what is). But scientificly speaking... lots of things as well! I'm sure that not all the things that I could learn, because in English sometimes it's more difficult to well understand everything, but I'm sure that for me it has been a lot.<br />
#'''Where do you see yourself two years from now?'''<br> Discovering lots of new functions and effects of these molecules from the JUNK GENOME.. ;) Well, not, '''I'M JOKING!!''' But I would like be in this stage...<br><br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br> Should I invest all it in science??? ..well, I would invert it for study: "The New Ways to introduce information and knowledge in our brain (our neurons), in the shortest time", like if we were keeping hardrives in our head or something like that.<br><br />
#'''What has been your favourite iGEM moment so far?'''<br> My favourite was when we began to work in Stochastic Modelling... Or not!! The following night after bowling..Do you remember, guys?? (Chris, Maciej, Toby, Karolis)<br><br />
#'''If you could be the inventor of anything, what would it be?'''<br> It would be... an umbrella for the bike.<br><br />
#'''What are you doing this weekend?'''<br> Maybe I will go to "The Fire Festival" near Aberdeen.. but it isn't sure yet!<br><br />
#'''Tell us a secret.'''<br> I don't know now... but for example that.. Sometimes I am afraid to every day.<br><br />
#'''Describe yourself in five words.'''<br> Curious, Lazy, (moment's)Lover, Enjoyable &.. Energetic. <br><br />
#'''Make up a haiku on the spot.'''<br> "El Pinxo li va dir al Panxo, punxa'm si, pero a la panxa no!!" ;)<br />
<br />
=== Lynsey McLeay ===<br />
#'''How are you today?'''<br><br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
#'''Where do you see yourself two years from now?'''<br><br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
#'''What has been your favourite iGEM moment so far?'''<br><br />
#'''If you could be the inventor of anything, what would it be?'''<br><br />
#'''What are you doing this weekend?'''<br><br />
#'''Tell us a secret.'''<br><br />
#'''Describe yourself in five words.'''<br><br />
#'''Make up a haiku on the spot.'''<br><br />
<br />
=== Christine Merrick ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
I'm good thanks, how are you?<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
I wanted to get loads of lab experience over the summer and asked Susan Rosser if she would take me on in her lab. She agreed to take me on over the summer and a short time later she informed me about the iGEM competition. I went to a presentation and discovered that this was exactly what I want to do. I think the concept of Synthetic Biology is very exciting and is definately something I want to be involved in while I work towards my degree.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
A whole lot! I have learned so much on a daily basis that when I think back to the start of the project I can't believe how much I've progressed. Three months ago I had never set foot in a research lab and here I am today! I think that's pretty cool.<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
Hopefully two years from now I will have just finished a work placement as part of my degree in a place much sunnier than Glasgow.<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
I think that basic molecular genetics should be taught as a foundation of biology in schools the same way that anatomy is. It will be in a hundred years why not make it so today? If that wasn't an option I would use it to solve the world's problems in some way, perhaps expressing drugs for the third world in plants, making biofuels improving the environment. <br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
Well, I really like it when we get results, and I’ve loved learning so much from the people I work with, but the international food night was my favourite. Sitting and laughing with the team from so many different backgrounds was great, especially while eating such good food. Maija’s fissu is awesome.<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
See Genesis. Only kidding. The wheel, surfing, the colour blue or maybe some way making smoke alarms tell the difference between a real fire and burned toast.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Some home improvements, the cinema, and possibly a trip to London to see my brother -he doesn't know it yet.<br />
<li><br />
'''Tell us a secret.'''<br><br />
I have a Girls Aloud song on my iPod, and I quite like it too.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
You ain’t seen nothing yet.<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
From where I’m sitting<br><br />
I see a computer screen<br><br />
With true reflection<br><br />
</ol><br />
<br />
=== Maija Paakkunainen ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
I'm good, just had strawberries for breakfast so i'm feeling very happy.<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
After my eventful exchange year in Scotland I still wanted more great experiences and decided to ask for summer project possibilities in Glasgow and heard about iGEM from Susan Rosser. The competition sounded challenging but good fun so i decided to apply.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
I've learned how to work with people from very different backgrounds and also discovered a great deal of new techniques and ways of attack.<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
I've graduated from my university back in Finland and hopefully doing some interesting research with a good group of people. Maybe staying abroad again and learning more things about different cultures and lifestyles. <br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
I'd choose a young, growing Finnish company with a great business plan and determined scientists. Possibly in cancer research because I've always found cancer an interesting and challenging thing to study.<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
Whenever we get the results we're expecting or when we realise something important which gets us one step forward in our study.<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
The structure of DNA, an efficient cure for cancer, an endless cup of coffee, or maybe a self chargeable mobile phone? Nokia of course.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
I'm going to Spain to enjoy some sun before going back to cold cold Finland.<br />
<li><br />
'''Tell us a secret.'''<br><br />
I collect fancy paperbags and I'd get upset if someone would fold or wrinkle them.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
Happy I came to Glasgow.<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
Haiku, what is it?<br><br />
Is it a weird poem?<br><br />
Or a tasty food?<br><br />
Google please help me,<br><br />
Wikipedia<br><br />
knows it all, always.<br><br />
</ol><br />
<br />
=== Scott Ramsay ===<br />
#'''How are you today?'''<br> Exhausted. We just spent the day clearing out our lab now that the project is almost finished.<br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br> I spoke to a lecturer after class who told me about a summer project we'd get to design ourselves and maybe win some prizes for. I thought it'd be a good opportunity to get to know what life in a lab is like before I start my PhD next year.<br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br> An understanding of how many times experiments go wrong before they go right!<br />
#'''Where do you see yourself two years from now?'''<br> Hopefully still halfway through a PhD.<br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br> Set up a scheme to take laboratory science to schools so students can see how much fun and hands-on it is.<br />
#'''What has been your favourite iGEM moment so far?'''<br> Making friends with the team from Edinburgh, and realising they're having setbacks too.<br />
#'''If you could be the inventor of anything, what would it be?'''<br> Already existing? The radio. Imagine how much money you could have made from all the technologies that rely on some sort of radio transmitters. In the future? I'd co-invent a machine that auto-thaws molecular biology reagents with [[User:L.McLeay|Lynsey]]...<br />
#'''What are you doing this weekend?'''<br> How forward!<br />
#'''Tell us a secret.'''<br> I love cheese and jam sandwiches.<br />
#'''Describe yourself in five words.'''<br> Tall, friendly, self-doubting, caffeine loving.<br />
#'''Make up a haiku on the spot.'''<br> Haiku I must write <br> But inventive I am not <br> This will have to do.<br />
<br />
=== Maciej Trybilo ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
Super Terrific!<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
Oh, I had the opportunity to learn new things every day and meet some brilliant people!<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
<li><br />
'''What are you doing this weekend?'''<br><br />
<li><br />
'''Tell us a secret.'''<br><br />
<li><br />
'''Describe yourself in five words.'''<br><br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
</ol></div>Rachhttp://2007.igem.org/wiki/index.php/Glasgow/InterviewsGlasgow/Interviews2007-10-25T20:29:26Z<p>Rach: /* Rachael Fulton */</p>
<hr />
<div>{| valign=top cellpadding=3<br />
|-<br />
!align=center|[[Image:Uog.jpg]] || [[Glasgow|<font face=georgia color=#3366CC size=4>Back To <br> Glasgow's <br> Main Page</font>]] || [[Glasgow/Meet the team|<font face=georgia color=#3366CC size=4>Back To <br> The Team <br> Page</font>]]<br />
|}<br />
----<br />
== 59 Second Interviews ==<br />
<br />
=== Toby Friend ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
A but achey and sneezy, but I'll get over it.<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
A number of reasons: to take part in cutting edge research (the money); to improve my knowledge of Genetics (my bank balance); do something constructive (earn some money).<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
see 'How and why...'<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
Deleting Spam from my email account.<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
AI baby!<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
hackey-sack in the sun!<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
AI baby!<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Reading some books and tidying my neglected London bedroom.<br />
<li><br />
'''Tell us a secret.'''<br><br />
ok, but this is between everyone in the world with internet access and me!...<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
My name is Toby Friend<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
My name is Toby Friend,<br />
I said MY NAME IS TOBY FRIEND,<br />
Actually it's Louis Sanchez Fernando<br />
</ol><br />
<br />
=== Rachael Fulton ===<br />
#'''How are you today?'''<br><br />
fantastic<br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
I thought it wouid be a great experience and wanted to learn and do something that would be very interesting for the summer<br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
The ability to work with people from different disciplines which i think is a completely unique oppertunity. I also learnt alot about biological modelling which i now would like to study further<br />
#'''Where do you see yourself two years from now?'''<br><br />
Hopefully doing my Phd<br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
AHHHHH decisions decisions actually £50,000 wouldn't go that far in science i should say invest it in curing cancer or something but i would probably put it into some programme to get kids interested in science<br />
#'''What has been your favourite iGEM moment so far?'''<br><br />
The international food night christines irish stew was amazing<br />
#'''If you could be the inventor of anything, what would it be?'''<br><br />
Shinyness i mean lets be honest shiny things are amazing<br />
#'''What are you doing this weekend?'''<br><br />
working which is rubbish and taking my wee cousin to the cinema cos i have no life.<br />
#'''Tell us a secret.'''<br><br />
I see dead people<br />
#'''Describe yourself in five words.'''<br><br />
loud, stressed, small, dizzy, annoying<br />
<br />
#'''Make up a haiku on the spot.'''<br><br />
is that 5,7,5? god i am so culturally inept<br />
<br />
=== Christine Harkness ===<br />
#'''How are you today?'''<br><br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
#'''Where do you see yourself two years from now?'''<br><br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
#'''What has been your favourite iGEM moment so far?'''<br><br />
#'''If you could be the inventor of anything, what would it be?'''<br><br />
#'''What are you doing this weekend?'''<br><br />
#'''Tell us a secret.'''<br><br />
#'''Describe yourself in five words.'''<br><br />
#'''Make up a haiku on the spot.'''<br><br />
=== Mai-Britt Jensen ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
I'm alright, you?<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
How? I got asked to come along to a meeting. Why? So my brain wouldn't dry out over the summer.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
Good times. Good banter. I don't know. New techniques.<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
In my third year of my PhD.<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
I would make science more accessible to the general public. I'd put more money into the media's prtrayal of science and help the public's understanding of it because there are so many misconceptions out there.<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
When it works!<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
A robot to pour plates.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Going to a Hallowe'en party and watching some scary movies.<br />
<li><br />
'''Tell us a secret.'''<br><br />
I find multichannel pipettes sexy.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
Curly. Danish. Odd. Green and Red!<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
What is a haiku?<br><br />
I'm not good with syllables.<br><br />
Is this good enough?<br><br />
</ol><br />
<br />
=== Karolis Kidykas ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
Fine Thanks!<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
I found a leaflet advertising iGEM competition in one of my lectures. It was very appealing offer. I was very interested in biology while at school but engineering won my sympathies back then. It was an opportunity to go into field I am interested in but has little to do with aerospace just before I graduate and submerge myself into professional life.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
I always thought about any form of life as of a complex machine which we will be able to control one day. This project proved it to me that I was right. Of course I may not be alive to whiteness it!<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
Airbus? ESA? I love Europe, but if bad luck follows me I will consider Boeing or NASA!<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
Probably not in Biology! Sorry! I have an engineers blood<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
I could easily name the scariest one, but its hard to think of any favorite one. There were quite a few of them. <br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
Antigravity Engine! Though I know it is probably impossible, but you said anything.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Either one of four: Tennis, Travelling, Working on my project, partying <br />
<li><br />
'''Tell us a secret.'''<br><br />
It will no longer be a secret then.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
That would be harder then to push a camel through a needle hole.<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
Sorry, times up!<br />
</ol><br />
<br />
=== Martina Marbà ===<br />
#'''How are you today?'''<br> Fine!! Thanks..<br><br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br> Firstly, because one day I chose Glasgow for my next stop of my way.<br> Secondly, because I found amazing to be involved in this new and attractive mixture of science for the study of synthetic biology. <br> <br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br> Wow...!! Lots of things!! First, how to work in a team (fact that usually maths or stats students we don't know what is). But scientificly speaking... lots of things as well! I'm sure that not all the things that I could learn, because in English sometimes it's more difficult to well understand everything, but I'm sure that for me it has been a lot.<br />
#'''Where do you see yourself two years from now?'''<br> Discovering lots of new functions and effects of these molecules from the JUNK GENOME.. ;) Well, not, '''I'M JOKING!!''' But I would like be in this stage...<br><br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br> Should I invest all it in science??? ..well, I would invert it for study: "The New Ways to introduce information and knowledge in our brain (our neurons), in the shortest time", like if we were keeping hardrives in our head or something like that.<br><br />
#'''What has been your favourite iGEM moment so far?'''<br> My favourite was when we began to work in Stochastic Modelling... Or not!! The following night after bowling..Do you remember, guys?? (Chris, Maciej, Toby, Karolis)<br><br />
#'''If you could be the inventor of anything, what would it be?'''<br> It would be... an umbrella for the bike.<br><br />
#'''What are you doing this weekend?'''<br> Maybe I will go to "The Fire Festival" near Aberdeen.. but it isn't sure yet!<br><br />
#'''Tell us a secret.'''<br> I don't know now... but for example that.. Sometimes I am afraid to every day.<br><br />
#'''Describe yourself in five words.'''<br> Curious, Lazy, (moment's)Lover, Enjoyable &.. Energetic. <br><br />
#'''Make up a haiku on the spot.'''<br> "El Pinxo li va dir al Panxo, punxa'm si, pero a la panxa no!!" ;)<br />
<br />
=== Lynsey McLeay ===<br />
#'''How are you today?'''<br><br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
#'''Where do you see yourself two years from now?'''<br><br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
#'''What has been your favourite iGEM moment so far?'''<br><br />
#'''If you could be the inventor of anything, what would it be?'''<br><br />
#'''What are you doing this weekend?'''<br><br />
#'''Tell us a secret.'''<br><br />
#'''Describe yourself in five words.'''<br><br />
#'''Make up a haiku on the spot.'''<br><br />
<br />
=== Christine Merrick ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
I'm good thanks, how are you?<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
I wanted to get loads of lab experience over the summer and asked Susan Rosser if she would take me on in her lab. She agreed to take me on over the summer and a short time later she informed me about the iGEM competition. I went to a presentation and discovered that this was exactly what I want to do. I think the concept of Synthetic Biology is very exciting and is definately something I want to be involved in while I work towards my degree.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
A whole lot! I have learned so much on a daily basis that when I think back to the start of the project I can't believe how much I've progressed. Three months ago I had never set foot in a research lab and here I am today! I think that's pretty cool.<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
Hopefully two years from now I will have just finished a work placement as part of my degree in a place much sunnier than Glasgow.<br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
I think that basic molecular genetics should be taught as a foundation of biology in schools the same way that anatomy is. It will be in a hundred years why not make it so today? If that wasn't an option I would use it to solve the world's problems in some way, perhaps expressing drugs for the third world in plants, making biofuels improving the environment. <br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
Well, I really like it when we get results, and I’ve loved learning so much from the people I work with, but the international food night was my favourite. Sitting and laughing with the team from so many different backgrounds was great, especially while eating such good food. Maija’s fissu is awesome.<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
See Genesis. Only kidding. The wheel, surfing, the colour blue or maybe some way making smoke alarms tell the difference between a real fire and burned toast.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
Some home improvements, the cinema, and possibly a trip to London to see my brother -he doesn't know it yet.<br />
<li><br />
'''Tell us a secret.'''<br><br />
I have a Girls Aloud song on my iPod, and I quite like it too.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
You ain’t seen nothing yet.<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
From where I’m sitting<br><br />
I see a computer screen<br><br />
With true reflection<br><br />
</ol><br />
<br />
=== Maija Paakkunainen ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
I'm good, just had strawberries for breakfast so i'm feeling very happy.<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
After my eventful exchange year in Scotland I still wanted more great experiences and decided to ask for summer project possibilities in Glasgow and heard about iGEM from Susan Rosser. The competition sounded challenging but good fun so i decided to apply.<br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
I've learned how to work with people from very different backgrounds and also discovered a great deal of new techniques and ways of attack.<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
I've graduated from my university back in Finland and hopefully doing some interesting research with a good group of people. Maybe staying abroad again and learning more things about different cultures and lifestyles. <br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
I'd choose a young, growing Finnish company with a great business plan and determined scientists. Possibly in cancer research because I've always found cancer an interesting and challenging thing to study.<br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
Whenever we get the results we're expecting or when we realise something important which gets us one step forward in our study.<br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
The structure of DNA, an efficient cure for cancer, an endless cup of coffee, or maybe a self chargeable mobile phone? Nokia of course.<br />
<li><br />
'''What are you doing this weekend?'''<br><br />
I'm going to Spain to enjoy some sun before going back to cold cold Finland.<br />
<li><br />
'''Tell us a secret.'''<br><br />
I collect fancy paperbags and I'd get upset if someone would fold or wrinkle them.<br />
<li><br />
'''Describe yourself in five words.'''<br><br />
Happy I came to Glasgow.<br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
Haiku, what is it?<br><br />
Is it a weird poem?<br><br />
Or a tasty food?<br><br />
Google please help me,<br><br />
Wikipedia<br><br />
knows it all, always.<br><br />
</ol><br />
<br />
=== Scott Ramsay ===<br />
#'''How are you today?'''<br> Exhausted. We just spent the day clearing out our lab now that the project is almost finished.<br />
#'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br> I spoke to a lecturer after class who told me about a summer project we'd get to design ourselves and maybe win some prizes for. I thought it'd be a good opportunity to get to know what life in a lab is like before I start my PhD next year.<br />
#'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br> An understanding of how many times experiments go wrong before they go right!<br />
#'''Where do you see yourself two years from now?'''<br> Hopefully still halfway through a PhD.<br />
#'''If you had £50,000 to invest in science, what would you do with it?'''<br> Set up a scheme to take laboratory science to schools so students can see how much fun and hands-on it is.<br />
#'''What has been your favourite iGEM moment so far?'''<br> Making friends with the team from Edinburgh, and realising they're having setbacks too.<br />
#'''If you could be the inventor of anything, what would it be?'''<br> Already existing? The radio. Imagine how much money you could have made from all the technologies that rely on some sort of radio transmitters. In the future? I'd co-invent a machine that auto-thaws molecular biology reagents with [[User:L.McLeay|Lynsey]]...<br />
#'''What are you doing this weekend?'''<br> How forward!<br />
#'''Tell us a secret.'''<br> I love cheese and jam sandwiches.<br />
#'''Describe yourself in five words.'''<br> Tall, friendly, self-doubting, caffeine loving.<br />
#'''Make up a haiku on the spot.'''<br> Haiku I must write <br> But inventive I am not <br> This will have to do.<br />
<br />
=== Maciej Trybilo ===<br />
<ol><br />
<li><br />
'''How are you today?'''<br><br />
Super Terrific!<br />
<li><br />
'''How and why did you get involved in the University of Glasgow iGEM Team 2007?'''<br><br />
<li><br />
'''What do you feel you have gained from working in the Glasgow iGEM Team?'''<br><br />
Oh, I had the opportunity to learn new things every day and meet some brilliant people!<br />
<li><br />
'''Where do you see yourself two years from now?'''<br><br />
<li><br />
'''If you had £50,000 to invest in science, what would you do with it?'''<br><br />
<li><br />
'''What has been your favourite iGEM moment so far?'''<br><br />
<li><br />
'''If you could be the inventor of anything, what would it be?'''<br><br />
<li><br />
'''What are you doing this weekend?'''<br><br />
<li><br />
'''Tell us a secret.'''<br><br />
<li><br />
'''Describe yourself in five words.'''<br><br />
<li><br />
'''Make up a haiku on the spot.'''<br><br />
</ol></div>Rachhttp://2007.igem.org/wiki/index.php/Glasgow/ModelingGlasgow/Modeling2007-10-25T14:22:01Z<p>Rach: </p>
<hr />
<div>{| valign=top cellpadding=3<br />
|-<br />
!align=center|[[Image:Uog.jpg]] || [[Glasgow|<font face=georgia color=#3366CC size=4>Back To <br> Glasgow's <br> Main Page</font>]]|| [[Glasgow/Wetlab|<font face=georgia color=#3366CC size=4>Go To <br> Glasgow's <br> Wetlab Log</font>]]<br />
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{|cellspacing="6px" cellpadding="16" border="0" width="100%"<br />
|- align=center<br />
|[https://2007.igem.org/Glasgow/Modeling <font face=georgia color=#3366CC size=5><b>Modelling</b></font>]<br />
|[https://2007.igem.org/Glasgow/DryLog <font face=georgia color=#3366CC size=5><b>Log</b></font>]<br />
|[https://2007.igem.org/Glasgow/DryTutorials <font face=georgia color=#3366CC size=5><b>Tutorials</b></font>]<br />
|[https://2007.igem.org/Glasgow/DryReferences <font face=georgia color=#3366CC size=5><b>References</b></font>]<br />
<br />
|}<br />
----<br />
<br />
= Summary =<br />
Synthetic biology has been used to describe an approach to biology<br />
which attempts to design and construct deliberate biological systems<br />
that can be investigated experimentally, which are otherwise very<br />
expensive and practically challenging. One of the central features<br />
of synthetic biology is the appreciation of the knowledge from<br />
science and engineering disciplines for the better design and<br />
understanding of synthetic networks. Here we have engineered a<br />
bacterial biosensor with the involvement of the construction of two<br />
new reporter genes PhzM and PhzS to detect polluting<br />
chemicals, which has the potential to provide an inexpensive and<br />
easy-to-use method of detecting industrial pollution. We explored<br />
a variety of computational approaches to study the behaviour of<br />
three synthetic systems: simple reporter system, positive feedback<br />
reporter system and '''[...]'''. We developed deterministic<br />
and stochastic models that quantitatively describe the graded<br />
signal-response property of the simple reporter system, and also<br />
showed that models can be expanded and used to qualitatively predict<br />
the ''in vivo'' behaviour of the complicated systems. The dynamics<br />
is further studied via the application of qualitative modelling<br />
methods. Simulations reveal that the model with positive feedback<br />
loop has higher output level than that from the intact model '''[...]''' This work shows that by integrating engineering<br />
techniques with scientific methodologies, we can gain a new insights<br />
into the genetic regulation and should become the reference framework<br />
for the design and construction of biochemical networks in synthetic<br />
biology.<br />
<br />
= Framework =<br />
We have used a framework which unifies the qualitative,<br />
stochastic and continuous worlds, as a basis for our overall approach to<br />
modelling<br />
and analysing the biochemical pathways.<br />
Each perspective adds its<br />
contribution to the understanding of the system, thus the three<br />
approaches do not compete, but complement each other. <br />
Qualitative descriptions are abstractions over<br />
stochastic or continuous descriptions, and the stochastic<br />
and continuous models approximate each other. <br />
Note: this framework is based on <br />
(Gilbert et al 2007).<br />
<br />
Our overall framework is illustrated in<br />
Figure 1 that relates the three major ways of<br />
modelling and analysing biochemical networks that we have used:<br />
qualitative, stochastic and continuous.<br />
<br />
The most abstract representation of a biochemical network is <br />
qualitative and is minimally described by its topology. Initial descriptions<br />
can be obtained from biochemists, and are often in some semiformal<br />
representation. These can easily be transformed into<br />
a formal description at this stage which is usually<br />
a bipartite directed graph with nodes representing biochemical<br />
entities or reactions, or in Petri net terminology places<br />
and transitions - see [https://2007.igem.org/Glasgow/Modeling#Petri_Net_Modelling Petri net section]'''. <br />
<center>[[Image: FrameworkSlide.png |frame| Figure 1. Conceptual modelling framework]]</center><br />
The qualitative description can be further enhanced by the abstract<br />
representation of discrete quantities of species, achieved in Petri<br />
nets by the use of tokens at places. These can represent the number<br />
of molecules, or the level of concentration, of a species. A<br />
particular arrangement of tokens over a network is called a <br />
marking. The standard semantics for these qualitative Petri<br />
nets (QPN) does not associate a time with transitions or the sojourn<br />
of tokens at places, and thus these descriptions are time-free.<br />
The qualitative analysis considers however all possible behaviour<br />
of the system under any timing. <br />
The behaviour of such a net forms a discrete state<br />
space. <br />
<br />
Timed information can be added to the qualitative description in<br />
two ways -- stochastic and continuous. <br />
The continuous model replaces the discrete values of species with<br />
continuous values, and hence is not able to describe the behaviour<br />
of species at the level of individual molecules, but only the overall<br />
behaviour via concentrations. We can regard the discrete description<br />
of concentration levels as abstracting over the continuous description<br />
of concentrations. Timed information is introduced by the association<br />
of a particular deterministic rate information with each transition,<br />
permitting the continuous model to be represented as a set of<br />
ordinary differential equations (ODEs) - see [https://2007.igem.org/Glasgow/Modeling#Model_Evolution Model Evolution]. <br />
The concentration of a<br />
particular species in such a model will have the same value at each<br />
point of time for repeated experiments. The state space of such<br />
models is continuous and linear. <br />
It is also possible to linearise ODE descriptions, by for example Laplace transforms, in an attempt to increase modularity in the system description and hence to facilitate model construction - see [[Glasgow/Modeling#BioBrick_library |BioBrick library]]. This approach results in transformations from the time-domain, in which inputs and outputs are functions of time, to the frequency-domain.<br />
<br />
The stochastic Petri net (SPN) description preserves the discrete state<br />
description,<br />
but in addition associates a particular stochastic rate information<br />
with each reaction, permitting the stochastic model to be regarded as a <br />
Markov process with a discrete state space.<br />
The time-evolution of such a process can be described by <br />
the Chemical Master Equation (CME), which is equivalent to Kolmorogov's forward equation<br />
(Wilkinson 2006).<br />
It is quite straightforward to simulate such a system, and this is usually<br />
done with the standard discrete event simulation procedure known as "the<br />
Gillespie algorithm" (Gillespie, 1977) - see [[Glasgow/Modeling#Stochastic_Modelling |Stochastic Modelling]].<br />
The QPN is an abstraction of<br />
the SPN, sharing the same state space and transition relation with<br />
the stochastic model, with the probabilistic information removed.<br />
All qualitative properties valid in the QPN are also valid in the<br />
SPN, and vice versa.<br />
<br />
<br />
In summary, the qualitative<br />
time-free description is the most basic, with discrete values<br />
representing numbers of molecules or levels of concentrations.<br />
The qualitative description abstracts over two timed, quantitative models.<br />
In the stochastic description, discrete values for the amounts of species<br />
are retained, but a stochastic rate is associated with each reaction. The continuous model describes amounts of species using continuous values<br />
and associates a deterministic rate with each reaction.<br />
These two time-dependent models can be mutually approximated by hazard<br />
functions belonging to the stochastic world.<br />
<br />
'''References'''<br />
<br />
D. Gilbert, M. Heiner and S. Lehrack (2007). [[Media:Edinburgh_proceedings.pdf | "A Unifying<br />
Framework for Modelling and Analysing Biochemical Pathways Using Petri Nets"]]. In<br />
proceedings Computational Methods in Systems Biology CMSB 2007 (Computational Methods in Systems Biology),<br />
Springer-Verlag LNCS/LNBI Volume 4695, pp. 200-216.<br />
<br />
D.T. Gillespie (1977). Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry, 1977. 81:25 pp 2340-2361<br />
<br />
D.J. Wilkinson (2006). Stochastic modelling for systems biology. Chapman & Hall / CRC.<br />
<br />
= Model design and analysis: outline=<br />
<br />
[[Image: Glasgow_model_feedback.png |400px|right]].<br />
The biochemists constructed an initial diagram to describe the system, using a fairly informal graphical syntax. This used a generic form of the transcription factor ('tf' for the gene, and 'TF' for the protein product) which represented both XylR (BTEX detecting) and DntR (Salicylate detecting). In outline, the steps that we used to develop and refine our model were:<br />
<br />
# Simplification by abstracting away the mRNA, thus combining transcription and translation. <br />
# Combining the PhzM and PhzS components to give one step from PCA to PYO<br />
# We also developed a variant of the model with a positive feedback loop <br />
<br />
<br />
<br />
The descriptions (with and without the positive feedback loop) were then transformed into Qualitative Petri Nets (QPN) - see [https://2007.igem.org/Glasgow/Modeling#Petri_Net_Modelling Petri net section].<br />
We derived the [https://2007.igem.org/Glasgow/Modeling#Parameter_searching_and_refinement rate parameters], and wrote down descriptions and simulated the models in <br />
[https://2007.igem.org/Glasgow/Modeling#Model_design:_detailed Ordinary Differential Equations], as well as in [https://2007.igem.org/Glasgow/Modeling#Petri_Net_Modelling Continuous Petri Nets] (CPN). We performed <br />
model analysis to refine the rate parameters using [https://2007.igem.org/Glasgow/Modeling#Minicap_Sensitivity_Analysis_Program_Package our implementation of the the MPSA algorithm] and [https://2007.igem.org/Glasgow/Modeling#Comparative_Model_Analysis model comparison]. <br />
Simulation of the continuous model was performed in MatLab and [http://www.bionessie.org BioNessie].<br />
Finally we constructed and simulated <br />
[https://2007.igem.org/Glasgow/Modeling#Stochastic_Modelling stochastic models] in MatLab and <br />
[https://2007.igem.org/Glasgow/Modeling#Stochastic_Modelling_vs._ODE_Modelling compared the results] of from the continuous (ODE-based) and stochastic approaches.<br />
<br />
A more detailed description of the model design process is as follows:<br />
<br />
= Model design: detailed=<br />
<br />
[[Image: Glasgow_ODE_plot.png |400px|right]]<br />
Two slightly different designs of our system were investigated in the course of the project. The latter is a modification that includes a positive feedback loop in order to enhance system's response to the signal.<br />
<br />
To see the full PDF report [[https://static.igem.org/mediawiki/2007/a/ad/Glasgow_modelling.pdf click here]].<br />
<br />
= Petri Net Modelling =<br />
[[Image: Glasgow_petri.png|400px|right]]<br />
[http://en.wikipedia.org/wiki/Petri_nets Petri nets] are used to describe discrete distributed systems that <br />
have concurrent processes, and provide users with an intuitive<br />
and easy-to-understand graphical representation of systems. During<br />
the iGEM project, we explored the use of Petri nets for the construction <br />
and analysis of synthetic biological networks, with a focus on facilitating the experimental design. We started off<br />
with the construction of ''qualitative'' petri-net models using the [http://www-dssz.informatik.tu-cottbus.de/index.html?/software/snoopy.html Snoopy] tool.<br />
A major advantage of the Qualitative Petri net (QPN) approach is that it enables us to perform network<br />
analysis in the absence of prior knowledge of the system parameters.<br />
We achieved this using the Charlie tool (available from [http://www-dssz.informatik.tu-cottbus.de/index.html?/software/tutorial_biopn.html Monika Heiner], where we identified the<br />
subsets of the Petri net models covered by certain properties with<br />
T-invariants (cyclic behaviour) and P-invariants (constant output<br />
in amount). We also studied various suggestions for the system's<br />
possible structures using the ''token game'' with different initial markings. Based on the observation<br />
of possible flows and discussions with biochemists, we examined and<br />
validated the models in terms of the boundedness, for example.<br />
Although Petri Nets were initially designed for qualitative analysis<br />
they have been extended to permit other forms of analysis including<br />
the quantitative form which is known as a Continuous Petri Net (CPN) which has an ODE-based semantics (see [https://2007.igem.org/Glasgow/Modeling#Framework framework]). We<br />
investigated the dynamics of the systems quantitatively using<br />
the parameters retrieved from the literature (for details, please<br />
refer to the section on Parameter Searching), and simulated the behaviour using Snoopy's<br />
inbuilt ODE solvers.<br />
These results were consistent with those<br />
generated by the pure [https://2007.igem.org/Glasgow/Modeling#Model_Evolution ODE approach].<br />
<br />
<br />
= Parameter searching and refinement =<br />
<br />
<br />
Parameter searching using Google Scholar, PubMed and biomedical databases <br />
was carried out by Christine and Xu under the direction of Dr David Leader. The<br />
task was largely steered by the discussions with, and the suggestions<br />
given by Emma Travis. We applied the [https://2007.igem.org/Glasgow/Modeling#Minicap_Sensitivity_Analysis_Program_Package Minicap program], and [https://2007.igem.org/Glasgow/Modeling#Comparative_Model_Analysis model comparison], both developed as part of the project, in order to help us identify sensible ranges associated with the parameters. A<br />
complete table containing either exact or constrained values for<br />
each of the system parameters was produced, as shown in the [Parameters.png parameter<br />
table], where parameters are annotated with their type,<br />
values and supporting data sources.<br />
<br />
[[Image: parameters_glasgow.png |right|600px| Figure System parameters]]<br />
<br />
References<br />
<br />
[1] A. Smirnova, C. Dian, G. A. Leonard, S. McSweeney, D. Birse, and P. Brzezinski. Development<br />
of a bacterial biosensor for nitrotoluenes: the crystal structure of the transcriptional<br />
regulator dntr. J. Mol. Biol, 340:405–418, 2004.<br />
<br />
[2] Jun Zhu and Stephen C. Winans. Autoinducer binding by the quorum-sensing regulator<br />
trar increases affinity for target promoters vitro and decreases trar turnover rates in whole<br />
cells. In Proc. Natl. Acad. Sci, pages 4832–4837, 1999.<br />
<br />
[3] F. Lacroute and GS. Stent. Peptide chain growth of -galactosidase in escherichia coli. J<br />
Mol Biol., 35:165–173, 1968.<br />
<br />
[4] Kyung Chae Jung, Ho Sung Rhee, Chi Hoon Park, and Yang Chul-Hak. Determination<br />
of the dissocation constants for recombinant c-myc. max. dna complexes: The inhibitory<br />
effect of linoleic acid on the dna-bindingstep. Biochem. Biophys. Res. Commun., 334:269–<br />
275, 2005.<br />
<br />
[5] Kamalendu Nath and Arthur L. Koch. Protein degradation in escherichia coli. i. measurement<br />
of rapidly and slowly decaying components. J. Biol. Chem., 245:2889–2900, 1970.<br />
<br />
[6] J. Parsons, B. Greenhagen, K. Shi, K. Calabrese, H. Robinson, and J. Ladner. Structural<br />
and functional analysis of the pyocyanin biosynthetic protein phzm from pseudomonas<br />
aeruginosa. Biochemistry, 2007.<br />
<br />
[7] Yunxia Q. O’Malley, Maher Y. Abdalla, Michael L. McCormick, Krzysztof J. Reszka,<br />
Gerene M. Denning, and Bradley E. Britigan. Subcellular localization of Pseudomonas<br />
pyocyanin cytotoxicity in human lung epithelial cells. Am J Physiol Lung Cell Mol Physiol,<br />
284(2):L420–430, 2003.<br />
<br />
== Minicap Sensitivity Analysis Program Package ==<br />
The Multi-Parametric and Initial Concentration Sensitivity analysis package is a Matlab function which executes a chosen Dynamic or Stochastic ''System Function'' for a defined number of different variable values across any desired range. The ''subject'' of analysis can either be constants in the user's system eg. parameters in a biological system (MPSA) or initial values of the variables in the user's system eg. initial substrate concentrations (ISCSA). The program will output a plot for each variable showing a comparison of ''acceptable'' and ''unacceptable'' samples across the subject's range with 3 calculated quantitative comparison figures: the ''Correlation Coefficient'', the ''Area'' between acceptable and unacceptable curves, and the ''Standard Deviation of the gradient'' of the acceptable plot. The comparative and intrinsic sensitivity of each chosen subject is thus highlighted. A plot showing the trend of the ''Substrate of Interest'' over time is also displayed. <br />
<br />
As well as this report, the Minicap package contains a User Manual in html, a number of example codes and all the novel (i.e. not ode15s) function and text files required to run Minicap.<br />
<br />
Downloads:<br />
<br />
The MatLab code is available as [http://compbio.dcs.gla.ac.uk/iGEM2007/Minicap_Sensitivity_Analysis_Program_Package.zip server (Minicap)]. <br />
To see the full PDF report on Minicap [[https://static.igem.org/mediawiki/2007/f/fb/Minicap.pdf click here]].<br />
<br />
== Comparative Model Analysis ==<br />
[[Image: Glasgow_Comparison.png|400px|right]]<br />
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?<br />
<br />
= Stochastic Modelling =<br />
[[Image:Glasgow_stochastic1.png|400px|right]] <br />
[[Image:Glasgow_stochastic2.png|400px|right]] <br />
<br />
A stochastic model for a general biosensor was constructed by students and simulated using Gillespie algorithm in MATLAB. Modelling biological systems in terms of ordinary differential equations is often not enough as cells are intrinsically noisy due to low numbers of molecules that participate in reactions. This can lead to significant statistical fluctuations in reaction rates and, the systems behaviour can differ from that predicted by a deterministic model. In this case, the system of interest was a single cell or bacteria of a bacterial whole cell biosensor.<br />
<br />
In this study the concept of stochastic simulation was introduced using a computational approach called Gillespie algorithm. The Gillespie algorithm allows a discrete and stochastic simulation with few reactants because every reaction is individually simulated. It takes into account a number of parameters that contribute to the model in a random manner, rather than assuming everything can be predicted.<br />
The goal of this study was to construct a stochastic model based on a deterministic one and to investigate the amount of noise (measured by the Fano factor and the coefficient of variation). Simulations were performed for several of cells and repeated for many runs to find representative behaviour. Noise was expected to decrease as the number of cells increased. However, in this study it was found that the coefficient of variation stayed roughly the same as the number of cells increased.<br />
<br />
We also aimed to study whether generic features predicted by other modelling approaches can be repeated by stochastic simulations. The effects of leakiness of the promoter was also observed in the system. Leakiness had a detrimental effect on the reliability of the system. It provides a higher output of the protein which in turn gives a false indication of the level of signal being detected. <br />
[[Media:GlasgowStochasticModelling.pdf|Full report on stochastic modelling]]<br />
<br />
= Stochastic Modelling vs. ODE Modelling = <br />
<br />
~~ the four pictures here ~~<br />
<br />
= BioBrick library =<br />
[[Image:BioBricks.JPG|right|400px]]<br />
<br />
BioBrickLibrary is an add on Library to Simulink for modelling dynamical biological systems at Brick (Gene) level. Simulink is a program dedicated for dynamical system simulation, however in depth knowledge of dynamics is needed if one is to simulate system mentioned above. The BioBrick library has all the blocks as well as GUI (Graphical User Interface) needed to do the job without understanding how Simulink works. It uses drag and drop system and shares all constants in Matlab’s .m file, so it is easy to store and update them.<br />
<br />
BioBrick library’s main aim is to tell whether and how different topology will influence the output of the system. If actual rate constants are known it can be used instead or as complimentary to ODE modelling. However it must be noted that ODE rate constants ARE NOT TRANSFERABLE to BioBrick library. <br />
<br />
Downloads:<br />
<br />
The software is available as [http://compbio.dcs.gla.ac.uk/iGEM2007/BioBricklibrary.zip BioBrickLibrary.zip].<br />
More information about the package can be found in [[Media:ElectrEcoBluSimulinkManual.pdf|ElectrEcoBluSimulinkManual]] document.<br />
See full [[Media:Report.pdf|Technical Report (''BioBrick Type Modeling'')]] for concept evolution and justification.<br />
<br />
= Microbial Fuel Cell Evaluation =<br />
[[Image:MFC.jpg|right|150px]]<br />
During the course of the project some introductory work has been done with microbial fuel cells in order to prepare us for the envisaged final stage of the project. We have had three fuel cells at our disposition supplied by the UK's NCBE. The experience we have gained and some of the results have been aggregated in this short work. <br />
<br />
= Download Models and Software =<br />
<br />
== Model files ==<br />
<br />
'''Qualitative''':<br />
<br />
Qualitative Petri net : Simple model and Feedback version.<br />
<br />
'''Continuous''': <br />
<br />
Continuous Petri net : Simple model and Feedback version.<br />
<br />
SBML : [http://compbio.dcs.gla.ac.uk/iGEM2007/iGEM_SimpleReporter.xml Simple model] and [http://compbio.dcs.gla.ac.uk/iGEM2007/iGEM_PositiveFeedbackReporter.xml Feedback version].<br />
<br />
Matlab : Simple model and Feedback version.<br />
<br />
'''Stochastic''': <br />
<br />
Matlab : Simple model.<br />
<br />
== Software ==<br />
<br />
Minicap program: [http://compbio.dcs.gla.ac.uk/iGEM2007/Minicap_Sensitivity_Analysis_Program_Package.zip matLab code], and [https://static.igem.org/mediawiki/2007/f/fb/Minicap.pdf report].<br />
<br />
BioBribrick library: [http://compbio.dcs.gla.ac.uk/iGEM2007/BioBricklibrary.zip software], <br />
[[Media:ElectrEcoBluSimulinkManual.pdf|ElectrEcoBluSimulinkManual]], and <br />
[[Media:Report.pdf|Technical Report (''BioBrick Type Modeling'')]].<br />
<br />
BioNessie: [http://www.bionessie.org package] (a biochemical network editor, simulator and analyser for SBML descriptions, developed at the Bioinformatics Research Centre Glasgow, outwith the iGEM project).<br />
<br />
----<br />
{|cellspacing="6px" cellpadding="16" border="0" width="100%"<br />
|- align=center<br />
|[https://2007.igem.org/Glasgow/Modeling <font face=georgia color=#3366CC size=5><b>Modelling</b></font>]<br />
|[https://2007.igem.org/Glasgow/DryLog <font face=georgia color=#3366CC size=5><b>Log</b></font>]<br />
|[https://2007.igem.org/Glasgow/DryTutorials <font face=georgia color=#3366CC size=5><b>Tutorials</b></font>]<br />
|[https://2007.igem.org/Glasgow/DryReferences <font face=georgia color=#3366CC size=5><b>References</b></font>]<br />
<br />
|}</div>Rachhttp://2007.igem.org/wiki/index.php/Glasgow/LecturesGlasgow/Lectures2007-07-13T13:09:48Z<p>Rach: </p>
<hr />
<div>The aim of these informal lectures was to give each side of the team an insight into the other as we are not 'modellers' and 'biologists' we are a team. The dry lab people also had a day of working in the lab with Emma and were promised that their experiments were going to be used in the project (that was possibly just to make us feel better though).<br><br />
<br />
[[Wet to Dry | '''Lecture given by Wetlab to Drylab''']]<br><br />
[[Dry to Wet | '''Lecture given by Drylab to Wetlab''']]</div>Rachhttp://2007.igem.org/wiki/index.php/GlasgowGlasgow2007-07-13T13:07:17Z<p>Rach: /* Links */</p>
<hr />
<div>[[Image:Glasgow header.png]] <br />
<br />
__NOTOC__<br />
{| cellspacing="2px" cellpadding="20" border="0" <br />
|valign="top" width=320px style="padding: 5px|<br />
== Team Members ==<br />
=== Instructors === <br />
[[User:dforehand|David Forehand]]<br><br />
<!--[[User:DavidGilbert|David Gilbert]]<br> --><br />
[http://www.brc.dcs.gla.ac.uk/~drg David Gilbert]<br><br />
[[User:GaryGray|Gary Gray]]<br><br />
[[User:gux|Xu Gu]] [[Image:Glasgow_flags_cn.png|China]]<br><br />
[[User:ghamilton1|Graham Hamilton]]<br><br />
[[User:raya|Raya Khanin]] <br> <!-- Raya must register --><br />
[[User:corriecas|David Leader]]<br><br />
[[User:Susanrosser|Susan Rosser]]<br><br />
[[User:EmmaTravis|Emma Travis]]<br><br />
<br />
=== Students ===<br />
[[User:toby|Toby Friend]] [[Image:Glasgow_flags_en.png|England]]<br><br />
[[User:Rach|Rachael Fulton]] [[Image:Glasgow_flags_sc.png|Scotland]]<br><br />
[[User:mojs|Mai-Britt Jensen]] [[Image:Glasgow_flags_dk.png|Denmark]]<br><br />
[[User:0602359k |Karolis Kidykas]] [[Image:Glasgow_flags_lt.png|Lithuania]]<br><br />
[[User:freestym|Martina Marbà]] [[Image:Glasgow_flag_es.png|Spain]]<br><br />
[[User:charkness|Christine Harkness]]<br><br />
[[User:christinemerrick|Christine Merrick]] [[Image:Glasgow_flags_sc.png|Scotland]]<br><br />
[[User:MaijaP|Maija Paakkunainen]] [[Image:Glasgow_flags_fi.png|Finland]]<br><br />
[[User:scott.w.ramsay|Scott Ramsay]] [[Image:Glasgow_flags_sc.png|Scotland]]<br><br />
[[User:mcek|Maciej Trybiło]] [[Image:Glasgow_flags_pl.png|Poland]]<br><br />
[[User:peterz|Petr Znamenskiy]] [[Image:Glasgow_flags_ru.png|Russia]]<br><br />
<br />
| valign="top" width=320px style="padding: 5px|<br />
<br />
== Links ==<br />
[http://www.gla.ac.uk Glasgow University] <br><br />
[http://en.wikipedia.org/wiki/Help:Wikitext_examples wiki text examples] <br><br />
[[Glasgow/Diary | Diary]]<br><br />
[[Glasgow/Lectures | Tutorials (understanding each other)]]<br />
<br />
== Goals Of this Project ==<br />
Save the world from lack of energy<br><br />
Detect some bad stuff<br><br />
Have some fun</div>Rachhttp://2007.igem.org/wiki/index.php/GlasgowGlasgow2007-07-13T13:06:11Z<p>Rach: /* Links */</p>
<hr />
<div>[[Image:Glasgow header.png]] <br />
<br />
__NOTOC__<br />
{| cellspacing="2px" cellpadding="20" border="0" <br />
|valign="top" width=320px style="padding: 5px|<br />
== Team Members ==<br />
=== Instructors === <br />
[[User:dforehand|David Forehand]]<br><br />
<!--[[User:DavidGilbert|David Gilbert]]<br> --><br />
[http://www.brc.dcs.gla.ac.uk/~drg David Gilbert]<br><br />
[[User:GaryGray|Gary Gray]]<br><br />
[[User:gux|Xu Gu]] [[Image:Glasgow_flags_cn.png|China]]<br><br />
[[User:ghamilton1|Graham Hamilton]]<br><br />
[[User:raya|Raya Khanin]] <br> <!-- Raya must register --><br />
[[User:corriecas|David Leader]]<br><br />
[[User:Susanrosser|Susan Rosser]]<br><br />
[[User:EmmaTravis|Emma Travis]]<br><br />
<br />
=== Students ===<br />
[[User:toby|Toby Friend]] [[Image:Glasgow_flags_en.png|England]]<br><br />
[[User:Rach|Rachael Fulton]] [[Image:Glasgow_flags_sc.png|Scotland]]<br><br />
[[User:mojs|Mai-Britt Jensen]] [[Image:Glasgow_flags_dk.png|Denmark]]<br><br />
[[User:0602359k |Karolis Kidykas]] [[Image:Glasgow_flags_lt.png|Lithuania]]<br><br />
[[User:freestym|Martina Marbà]] [[Image:Glasgow_flag_es.png|Spain]]<br><br />
[[User:charkness|Christine Harkness]]<br><br />
[[User:christinemerrick|Christine Merrick]] [[Image:Glasgow_flags_sc.png|Scotland]]<br><br />
[[User:MaijaP|Maija Paakkunainen]] [[Image:Glasgow_flags_fi.png|Finland]]<br><br />
[[User:scott.w.ramsay|Scott Ramsay]] [[Image:Glasgow_flags_sc.png|Scotland]]<br><br />
[[User:mcek|Maciej Trybiło]] [[Image:Glasgow_flags_pl.png|Poland]]<br><br />
[[User:peterz|Petr Znamenskiy]] [[Image:Glasgow_flags_ru.png|Russia]]<br><br />
<br />
| valign="top" width=320px style="padding: 5px|<br />
<br />
== Links ==<br />
[http://www.gla.ac.uk Glasgow University] <br><br />
[http://en.wikipedia.org/wiki/Help:Wikitext_examples wiki text examples] <br><br />
[[Glasgow/Diary | Diary]]<br><br />
[[Glasgow/Tutorials (understanding each other) | Tutorials (understanding each other)]]<br />
<br />
== Goals Of this Project ==<br />
Save the world from lack of energy<br><br />
Detect some bad stuff<br><br />
Have some fun</div>Rachhttp://2007.igem.org/wiki/index.php/Dry_to_WetDry to Wet2007-07-13T13:00:07Z<p>Rach: /* Michaelis-Menten */</p>
<hr />
<div>=== Mass-Action Reaction Modelling ===<br />
There are many advantages of modeling a biological system in differential equations. Predictions can be made for experiments before they are carried out in the wetlab which can prove to be very useful.<br />
<br />
The simplest reaction which is shown is simple decay where substance A decays to substance B. This can be modeled by two differential equations. The quantities which must be known to model these equations are the initial concentrations of both A and B and also the rate constant k1. By using Matlab a graph can be produced which shows that as A is used up B increases.<br />
<br />
[[Image:simpledecayequation.jpg|400px]]<br />
[[Image:simpledecay.jpg|400px]]<br />
<br />
<br />
The decay reaction can also take the form of becoming a reversible reaction where A turns into B via a rate constant of k1 while at the same time B turns into A via a rate constant of k2. This again is modeled by two differential equations<br />
<br />
[[Image:reversibleequation.jpg|400px]]<br />
[[Image:reversible.jpg|400px]]<br />
<br />
<br />
Another type of reaction which can be shown is an addition reaction where both A and B must be present to react to form C via a rate constant. Three differential equations are formed to model this reaction.<br />
<br />
[[Image:additionequation.jpg|400px]]<br />
[[Image:addition.jpg|400px]]<br />
<br />
An enzyme reaction can be modeled as shown below. The enzyme complex is modeled by using both the addition reaction and the reversible reaction. <br />
<br />
[[Image:enzymeequation.jpg|400px]]<br />
[[Image:enzyme.jpg|400px]]<br />
<br />
=== RKIP network ===<br />
After gaining a thorough understanding of methods involved with modeling simple mass-action reactions, we can move on to more complex systems such as the RKIP network.<br><br />
[[Image:RKIP network.JPG | 700px]]<br><br />
In the above diagram, substrates, enzymes and substrate/enzyme complexes are represented by numbered circles, rate constants are represented by numbered squares. By isolating individual species and their direct peripheral species (those being formed from or forming the isolated species) we are able to treat the group as a simple mass-action reaction. A differential equation is then found for each species based on the rate constants and code can be written and a graph plotted showing the trend of all the species’ concentration over time giving the following graph:<br><br />
[[Image:RKIP network graph.jpg]]<br><br />
<br />
=== Sensitivity ===<br />
''An insight into a system's sensitivity will show how the variation of a model can be apportioned qualitatively or quantitatively to different sources of variation''<br><br />
<br />
One method of exposing the variation of a model is to program a loop exposing a modelled reaction to increasing values of a chosen constant. This process was followed with the metabolic pathway showing in [[Mass-Action Reaction Modelling]] and ploted on a graph showing the response of all 4 species for a set range of varying K2 values from 1 to 10 where 10 is highlighted red.<br><br />
[[Image: metabolic sensetivity response.jpg | 800px]]<br />
<br />
=== Michaelis-Menten ===<br />
''This was taken from 'Biochemistry' by 'Stryer'''<br><br />
''I apologise for the format of the equations i did not have them in a pdf document and the mathematical formula toolbox will not work''<br><br><br />
Anybody who has done any sort of biological study will know Michaelis-Menten what i am trying to acheive here is to take it from the basics so as to equate it to the equations we will be using to model the system and to give the biologists an idea of what values and models we need. In this case all k values are rate constants and [] means concentration and E is enzyme, S is substrate and [E]t is total enzyme concentration. <br><br />
The Michaelis-Menten equation describes the kinetic properties of many enzymes. Consider the simple system A -> P<br />
The rate of V is the quantity of A that disappears over a specified unit of time which is equal to the rate of appearance of P. For this system V=k[A] where k is the rate constant. <br />
The simplest model that accounts for the kinetic properties of many enzymes is (i will add it in when i have figured out how to)<br><br />
what we want is an expression that relates the rate of catalysis to the concentrations of substrate and enzyme and the rates of the individual steps.<br><br />
Our starting point is that the catalytic rate is equal to the product of the ES complex and k3.<br />
'''Vo=K2[ES]''' ''call this '''eqn(1)''' as i will be referring to it again''<br />
Now expressing [ES]in terms of known quantities the rates of '''formation''' and '''breakdown''' of [ES] are given by:<br />
'''formation [ES] = k1*[E]*[S]'''<br />
'''breakdown [ES] = (k2+k3)*[ES]'''<br />
A steady state occurs when the rates of formation and breakdown of the ES complex are equal, this gives the formula<br />
'''k1*[E]*[S]=(k2+k3)*[ES]'''<br />
which then gives <br />
'''[E][S] / [ES]=(k2+k3) / k1''' <br> <br />
This can be simplified by defining '''Km''' called the Michaelis constant<br><br />
'''Km = (k2+k3) / k1'''<br />
from this we get<br />
'''[ES] = [E][S] / Km''' ''call this '''eqn(2)''' as i will be referring to it again'' <br><br />
Now examining the numerator of this equation: because substrate is usually present at much higher concentrations than the enzyme, the concentration of uncombined substrate [S]is very nearly equal to the total substrate concentration. The concentration of enzyme [E] is equal to '''[E]t - [ES]''' now substituting this into ''eqn(2)'' and after some simplification we get<br />
'''[ES]=([E]t*[S]) / ([S]+Km)'''<br />
by substituting this expression into ''eqn(1)'' we get<br />
'''Vo = (k2[E]t*[S]) / ([s]+Km)'''<br />
The maximised rate Vmax is obtained when the catalytic sites on the enzyme are saturated with substrate i.e '''[ES] = [E]t''' thus<br />
'''Vmax = k2*[E]t<br />
this gives the Michaelis-Menten equation <br />
'''Vo = Vmax*([S] / ([S]+km)) ''' ''call this '''eqn(3)''' as i will refer to it again'' <br />
when '''[S]=Km''' then '''Vo = Vmax / 2'''. Thus Km is equal to the substrate concentration at which the reaction rate is half its maximal value.<br><br />
<br />
''The following part is from slides Raya showed us''<br><br />
Now relating this to gene transcription which we will be doing in this project. As any biologist will know<br><br />
'''Gene expression = production - degradation'''<br><br />
so '''μ’(t) = p(t) - δμ(t) ''' ''call this '''eqn(4)''' i will refer back to it''<br><br />
μ’(t) could be the production of mRNA for example.<br />
'''where P(t) = production rate and δ(t) = linear degredation rate''' <br><br />
We have the Michaeilis Menton model for the production (or transcription) rate. There is one for the '''activator''' and one for the '''repressor'''.<br><br />
'''Activator'''- The transcription factor '''TF''' ''increases'' the transcription rate of the gene:<br />
'''P(t)a =( β*[TF] / (γ + [TF]) ) + α''' ''call this '''eqn(5)'''''<br><br />
Where<br>β >0 is the maximum transcription rate which is equivalent to say '''Vmax''' in '''''eqn(3)'''''<br><br />
γ >0 is the half saturation constant which is equivalent to say '''Km''' in '''''eqn(3)'''''<br><br />
α >=0 is the basal rate of transcription (As far as i am aware this can be referred to as leakiness) which is taken into consideration to make the model more accurate.<br><br />
Now the equation for gene transcription for the '''activator''' is given by substituting '''''eqn(5)''''' into '''''eqn(4)''''' and assuming α=0.<br />
'''μ’(t)a = ( β*[TF] / (γ + [TF]) ) - δμ(t) '''<br><br />
'''Repressor''- The transcription factor '''TF''' ''decreases''the transcription rate of the gene:<br />
'''P(t)r =( β / (γ + [TF]) ) + α''' ''call this '''eqn(6)'''''<br><br />
Again the equation for gene transcription for the '''repressor''' is given by sunstituting '''''eqn(6)''''' into '''''eqn(5)''''' and assumingα=0. We get:<br />
'''μ’(t)r = ( β / (γ + [TF]) ) - δμ(t) '''<br> <br />
When considering more than one binding site we brinh '''h=number of binding sites into the equation''' so in both the '''activator''' and '''repressor''' equations for gene transcription '''whenever TF is in the equation you multiply it to the power of h'''.<br />
<br />
=== <u> Multiple Transcription Factors </u> ===<br />
<br />
There is an extension of the formulas from Michaelis-Menten, for '''multiple transcription factors'''. <br />
<br />
''Regulation of gene expression is controlled by the binding of transcription factors to specific DNA sequences in gene promoter regions. Multiple transcription factor binding events are involved in the regulation of cellular processes.'' <br />
<br />
<br />
When we have more than one '''transcription factor''' (TF) involved we can find two situations:<br><br />
(In this page we will study the scenario with only 2 transcription factors involved) <br />
<br />
- SUM Gate.<br><br />
- ADD Gate.<br />
<br />
<br />
<u>'''SUM Gate'''</u>: As the word refers in this situation the effect from multiple TFs is additive. That means that the transcription could be induced for one '''OR''' other factor (or both together). But we have to note in this point that it’s not necessary the presence of both of them.<br />
<br />
<u>'''ADD Gate'''</u>: Implies the situation where the effect from multiple TFs is multiplicative. That means that the transcription will be induced for one '''AND''' the other factor at the same time (both actuate together). Nottice that if one of them is not active the transcription will not be done.<br />
<br />
<center>[[Image:formulas.jpg|400px]]</center></div>Rachhttp://2007.igem.org/wiki/index.php/Dry_to_WetDry to Wet2007-07-13T12:59:37Z<p>Rach: /* Michaelis-Menten */</p>
<hr />
<div>=== Mass-Action Reaction Modelling ===<br />
There are many advantages of modeling a biological system in differential equations. Predictions can be made for experiments before they are carried out in the wetlab which can prove to be very useful.<br />
<br />
The simplest reaction which is shown is simple decay where substance A decays to substance B. This can be modeled by two differential equations. The quantities which must be known to model these equations are the initial concentrations of both A and B and also the rate constant k1. By using Matlab a graph can be produced which shows that as A is used up B increases.<br />
<br />
[[Image:simpledecayequation.jpg|400px]]<br />
[[Image:simpledecay.jpg|400px]]<br />
<br />
<br />
The decay reaction can also take the form of becoming a reversible reaction where A turns into B via a rate constant of k1 while at the same time B turns into A via a rate constant of k2. This again is modeled by two differential equations<br />
<br />
[[Image:reversibleequation.jpg|400px]]<br />
[[Image:reversible.jpg|400px]]<br />
<br />
<br />
Another type of reaction which can be shown is an addition reaction where both A and B must be present to react to form C via a rate constant. Three differential equations are formed to model this reaction.<br />
<br />
[[Image:additionequation.jpg|400px]]<br />
[[Image:addition.jpg|400px]]<br />
<br />
An enzyme reaction can be modeled as shown below. The enzyme complex is modeled by using both the addition reaction and the reversible reaction. <br />
<br />
[[Image:enzymeequation.jpg|400px]]<br />
[[Image:enzyme.jpg|400px]]<br />
<br />
=== RKIP network ===<br />
After gaining a thorough understanding of methods involved with modeling simple mass-action reactions, we can move on to more complex systems such as the RKIP network.<br><br />
[[Image:RKIP network.JPG | 700px]]<br><br />
In the above diagram, substrates, enzymes and substrate/enzyme complexes are represented by numbered circles, rate constants are represented by numbered squares. By isolating individual species and their direct peripheral species (those being formed from or forming the isolated species) we are able to treat the group as a simple mass-action reaction. A differential equation is then found for each species based on the rate constants and code can be written and a graph plotted showing the trend of all the species’ concentration over time giving the following graph:<br><br />
[[Image:RKIP network graph.jpg]]<br><br />
<br />
=== Sensitivity ===<br />
''An insight into a system's sensitivity will show how the variation of a model can be apportioned qualitatively or quantitatively to different sources of variation''<br><br />
<br />
One method of exposing the variation of a model is to program a loop exposing a modelled reaction to increasing values of a chosen constant. This process was followed with the metabolic pathway showing in [[Mass-Action Reaction Modelling]] and ploted on a graph showing the response of all 4 species for a set range of varying K2 values from 1 to 10 where 10 is highlighted red.<br><br />
[[Image: metabolic sensetivity response.jpg | 800px]]<br />
<br />
=== Michaelis-Menten ===<br />
''This was taken from 'Biochemistry' by 'Stryer'''<br><br />
''I apologise for the format of the equations i did not have them in a pdf document and the mathematical formula toolbox will not work''<br />
Anybody who has done any sort of biological study will know Michaelis-Menten what i am trying to acheive here is to take it from the basics so as to equate it to the equations we will be using to model the system and to give the biologists an idea of what values and models we need. In this case all k values are rate constants and [] means concentration and E is enzyme, S is substrate and [E]t is total enzyme concentration. <br><br />
The Michaelis-Menten equation describes the kinetic properties of many enzymes. Consider the simple system A -> P<br />
The rate of V is the quantity of A that disappears over a specified unit of time which is equal to the rate of appearance of P. For this system V=k[A] where k is the rate constant. <br />
The simplest model that accounts for the kinetic properties of many enzymes is (i will add it in when i have figured out how to)<br><br />
what we want is an expression that relates the rate of catalysis to the concentrations of substrate and enzyme and the rates of the individual steps.<br><br />
Our starting point is that the catalytic rate is equal to the product of the ES complex and k3.<br />
'''Vo=K2[ES]''' ''call this '''eqn(1)''' as i will be referring to it again''<br />
Now expressing [ES]in terms of known quantities the rates of '''formation''' and '''breakdown''' of [ES] are given by:<br />
'''formation [ES] = k1*[E]*[S]'''<br />
'''breakdown [ES] = (k2+k3)*[ES]'''<br />
A steady state occurs when the rates of formation and breakdown of the ES complex are equal, this gives the formula<br />
'''k1*[E]*[S]=(k2+k3)*[ES]'''<br />
which then gives <br />
'''[E][S] / [ES]=(k2+k3) / k1''' <br> <br />
This can be simplified by defining '''Km''' called the Michaelis constant<br><br />
'''Km = (k2+k3) / k1'''<br />
from this we get<br />
'''[ES] = [E][S] / Km''' ''call this '''eqn(2)''' as i will be referring to it again'' <br><br />
Now examining the numerator of this equation: because substrate is usually present at much higher concentrations than the enzyme, the concentration of uncombined substrate [S]is very nearly equal to the total substrate concentration. The concentration of enzyme [E] is equal to '''[E]t - [ES]''' now substituting this into ''eqn(2)'' and after some simplification we get<br />
'''[ES]=([E]t*[S]) / ([S]+Km)'''<br />
by substituting this expression into ''eqn(1)'' we get<br />
'''Vo = (k2[E]t*[S]) / ([s]+Km)'''<br />
The maximised rate Vmax is obtained when the catalytic sites on the enzyme are saturated with substrate i.e '''[ES] = [E]t''' thus<br />
'''Vmax = k2*[E]t<br />
this gives the Michaelis-Menten equation <br />
'''Vo = Vmax*([S] / ([S]+km)) ''' ''call this '''eqn(3)''' as i will refer to it again'' <br />
when '''[S]=Km''' then '''Vo = Vmax / 2'''. Thus Km is equal to the substrate concentration at which the reaction rate is half its maximal value.<br><br />
<br />
''The following part is from slides Raya showed us''<br><br />
Now relating this to gene transcription which we will be doing in this project. As any biologist will know<br><br />
'''Gene expression = production - degradation'''<br><br />
so '''μ’(t) = p(t) - δμ(t) ''' ''call this '''eqn(4)''' i will refer back to it''<br><br />
μ’(t) could be the production of mRNA for example.<br />
'''where P(t) = production rate and δ(t) = linear degredation rate''' <br><br />
We have the Michaeilis Menton model for the production (or transcription) rate. There is one for the '''activator''' and one for the '''repressor'''.<br><br />
'''Activator'''- The transcription factor '''TF''' ''increases'' the transcription rate of the gene:<br />
'''P(t)a =( β*[TF] / (γ + [TF]) ) + α''' ''call this '''eqn(5)'''''<br><br />
Where<br>β >0 is the maximum transcription rate which is equivalent to say '''Vmax''' in '''''eqn(3)'''''<br><br />
γ >0 is the half saturation constant which is equivalent to say '''Km''' in '''''eqn(3)'''''<br><br />
α >=0 is the basal rate of transcription (As far as i am aware this can be referred to as leakiness) which is taken into consideration to make the model more accurate.<br><br />
Now the equation for gene transcription for the '''activator''' is given by substituting '''''eqn(5)''''' into '''''eqn(4)''''' and assuming α=0.<br />
'''μ’(t)a = ( β*[TF] / (γ + [TF]) ) - δμ(t) '''<br><br />
'''Repressor''- The transcription factor '''TF''' ''decreases''the transcription rate of the gene:<br />
'''P(t)r =( β / (γ + [TF]) ) + α''' ''call this '''eqn(6)'''''<br><br />
Again the equation for gene transcription for the '''repressor''' is given by sunstituting '''''eqn(6)''''' into '''''eqn(5)''''' and assumingα=0. We get:<br />
'''μ’(t)r = ( β / (γ + [TF]) ) - δμ(t) '''<br> <br />
When considering more than one binding site we brinh '''h=number of binding sites into the equation''' so in both the '''activator''' and '''repressor''' equations for gene transcription '''whenever TF is in the equation you multiply it to the power of h'''.<br />
<br />
=== <u> Multiple Transcription Factors </u> ===<br />
<br />
There is an extension of the formulas from Michaelis-Menten, for '''multiple transcription factors'''. <br />
<br />
''Regulation of gene expression is controlled by the binding of transcription factors to specific DNA sequences in gene promoter regions. Multiple transcription factor binding events are involved in the regulation of cellular processes.'' <br />
<br />
<br />
When we have more than one '''transcription factor''' (TF) involved we can find two situations:<br><br />
(In this page we will study the scenario with only 2 transcription factors involved) <br />
<br />
- SUM Gate.<br><br />
- ADD Gate.<br />
<br />
<br />
<u>'''SUM Gate'''</u>: As the word refers in this situation the effect from multiple TFs is additive. That means that the transcription could be induced for one '''OR''' other factor (or both together). But we have to note in this point that it’s not necessary the presence of both of them.<br />
<br />
<u>'''ADD Gate'''</u>: Implies the situation where the effect from multiple TFs is multiplicative. That means that the transcription will be induced for one '''AND''' the other factor at the same time (both actuate together). Nottice that if one of them is not active the transcription will not be done.<br />
<br />
<center>[[Image:formulas.jpg|400px]]</center></div>Rachhttp://2007.igem.org/wiki/index.php/Dry_to_WetDry to Wet2007-07-13T09:08:15Z<p>Rach: /* Michaelis-Menten */</p>
<hr />
<div>=== Mass-Action Reaction Modelling ===<br />
There are many advantages of modeling a biological system in differential equations. Predictions can be made for experiments before they are carried out in the wetlab which can prove to be very useful.<br />
<br />
The simplest reaction which is shown is simple decay where substance A decays to substance B. This can be modeled by two differential equations. The quantities which must be known to model these equations are the initial concentrations of both A and B and also the rate constant k1. By using Matlab a graph can be produced which shows that as A is used up B increases.<br />
<br />
[[Image:simpledecayequation.jpg|400px]]<br />
[[Image:simpledecay.jpg|400px]]<br />
<br />
<br />
The decay reaction can also take the form of becoming a reversible reaction where A turns into B via a rate constant of k1 while at the same time B turns into A via a rate constant of k2. This again is modeled by two differential equations<br />
<br />
[[Image:reversibleequation.jpg|400px]]<br />
[[Image:reversible.jpg|400px]]<br />
<br />
<br />
Another type of reaction which can be shown is an addition reaction where both A and B must be present to react to form C via a rate constant. Three differential equations are formed to model this reaction.<br />
<br />
[[Image:additionequation.jpg|400px]]<br />
[[Image:addition.jpg|400px]]<br />
<br />
An enzyme reaction can be modeled as shown below. The enzyme complex is modeled by using both the addition reaction and the reversible reaction. <br />
<br />
[[Image:enzymeequation.jpg|400px]]<br />
[[Image:enzyme.jpg|400px]]<br />
<br />
=== RKIP network ===<br />
After gaining a thorough understanding of methods involved with modeling simple mass-action reactions, we can move on to more complex systems such as the RKIP network.<br><br />
[[Image:RKIP network.JPG | 700px]]<br><br />
In the above diagram, substrates, enzymes and substrate/enzyme complexes are represented by numbered circles, rate constants are represented by numbered squares. By isolating individual species and their direct peripheral species (those being formed from or forming the isolated species) we are able to treat the group as a simple mass-action reaction. A differential equation is then found for each species based on the rate constants and code can be written and a graph plotted showing the trend of all the species’ concentration over time giving the following graph:<br><br />
[[Image:RKIP network graph.jpg]]<br><br />
<br />
=== Sensitivity ===<br />
''An insight into a system's sensitivity will show how the variation of a model can be apportioned qualitatively or quantitatively to different sources of variation''<br><br />
<br />
One method of exposing the variation of a model is to program a loop exposing a modelled reaction to increasing values of a chosen constant. This process was followed with the metabolic pathway showing in [[Mass-Action Reaction Modelling]] and ploted on a graph showing the response of all 4 species for a set range of varying K2 values from 1 to 10 where 10 is highlighted red.<br><br />
[[Image: metabolic sensetivity response.jpg | 800px]]<br />
<br />
=== Michaelis-Menten ===<br />
''This was taken from 'Biochemistry' by 'Stryer'''<br><br />
Anybody who has done any sort of biological study will know Michaelis-Menten what i am trying to acheive here is to take it from the basics so as to equate it to the equations we will be using to model the system and to give the biologists an idea of what values and models we need. In this case all k values are rate constants and [] means concentration and E is enzyme, S is substrate and [E]t is total enzyme concentration. <br><br />
The Michaelis-Menten equation describes the kinetic properties of many enzymes. Consider the simple system A -> P<br />
The rate of V is the quantity of A that disappears over a specified unit of time which is equal to the rate of appearance of P. For this system V=k[A] where k is the rate constant. <br />
The simplest model that accounts for the kinetic properties of many enzymes is (i will add it in when i have figured out how to)<br><br />
what we want is an expression that relates the rate of catalysis to the concentrations of substrate and enzyme and the rates of the individual steps.<br><br />
Our starting point is that the catalytic rate is equal to the product of the ES complex and k3.<br />
'''Vo=K2[ES]''' ''call this '''eqn(1)''' as i will be referring to it again''<br />
Now expressing [ES]in terms of known quantities the rates of '''formation''' and '''breakdown''' of [ES] are given by:<br />
'''formation [ES] = k1*[E]*[S]'''<br />
'''breakdown [ES] = (k2+k3)*[ES]'''<br />
A steady state occurs when the rates of formation and breakdown of the ES complex are equal, this gives the formula<br />
'''k1*[E]*[S]=(k2+k3)*[ES]'''<br />
which then gives <br />
'''[E][S] / [ES]=(k2+k3) / k1''' <br> <br />
This can be simplified by defining '''Km''' called the Michaelis constant<br><br />
'''Km = (k2+k3) / k1'''<br />
from this we get<br />
'''[ES] = [E][S] / Km''' ''call this '''eqn(2)''' as i will be referring to it again'' <br><br />
Now examining the numerator of this equation: because substrate is usually present at much higher concentrations than the enzyme, the concentration of uncombined substrate [S]is very nearly equal to the total substrate concentration. The concentration of enzyme [E] is equal to '''[E]t - [ES]''' now substituting this into ''eqn(2)'' and after some simplification we get<br />
'''[ES]=([E]t*[S]) / ([S]+Km)'''<br />
by substituting this expression into ''eqn(1)'' we get<br />
'''Vo = (k2[E]t*[S]) / ([s]+Km)'''<br />
The maximised rate Vmax is obtained when the catalytic sites on the enzyme are saturated with substrate i.e '''[ES] = [E]t''' thus<br />
'''Vmax = k2*[E]t<br />
this gives the Michaelis-Menten equation <br />
'''Vo = Vmax*([S] / ([S]+km)) ''' ''call this '''eqn(3)''' as i will refer to it again'' <br />
when '''[S]=Km''' then '''Vo = Vmax / 2'''. Thus Km is equal to the substrate concentration at which the reaction rate is half its maximal value.<br><br />
<br />
''The following part is from slides Raya showed us''<br><br />
Now relating this to gene transcription which we will be doing in this project. As any biologist will know<br><br />
'''Gene expression = production - degradation'''<br><br />
<br />
=== <u> Multiple Transcription Factors </u> ===<br />
<br />
There is an extension of the formulas from Michaelis-Menten, for '''multiple transcription factors'''. <br />
<br />
''Regulation of gene expression is controlled by the binding of transcription factors to specific DNA sequences in gene promoter regions. Multiple transcription factor binding events are involved in the regulation of cellular processes.'' <br />
<br />
<br />
When we have more than one '''transcription factor''' (TF) involved we can find two situations:<br><br />
(In this page we will study the scenario with only 2 transcription factors involved) <br />
<br />
- SUM Gate.<br><br />
- ADD Gate.<br />
<br />
<br />
<u>'''SUM Gate'''</u>: As the word refers in this situation the effect from multiple TFs is additive. That means that the transcription could be induced for one '''OR''' other factor (or both together). But we have to note in this point that it’s not necessary the presence of both of them.<br />
<br />
<u>'''ADD Gate'''</u>: Implies the situation where the effect from multiple TFs is multiplicative. That means that the transcription will be induced for one '''AND''' the other factor at the same time (both actuate together). Nottice that if one of them is not active the transcription will not be done.<br />
<br />
<center>[[Image:formulas.jpg|400px]]</center></div>Rachhttp://2007.igem.org/wiki/index.php/User:RachUser:Rach2007-07-11T17:31:41Z<p>Rach: </p>
<hr />
<div>[[Image:IMG_0292.JPG]]<br />
'''A bit about me'''<br />
<br />
Well i'm Rachael or Rach to my friends. I am currently going into fourth year of joint honouyrs in maths and statistics which i enjoy (although i will deny it during any stressful period). I'm a complete stress monkey and should be avoided during exams unless you have lucozade and chocolate. In the future i'd love to do my phd in some kind of modelling and biological systems but i'm not sure exactly of the path i would like to follow and of course i need to get the grades first. If i do do this i would love to be a research assistant at glasgow uni and then go on to possibly be a lecturer but again thats all in the future i may run away and become a secret agent before that.i love reading, rock climbing, hiking, I was learning to read and write mandarin chinese which i loved studying until my teacher moved to Malaysia and now i'm a bit swamped with the whole IGEM thing but i can still probably hold a small conversation as long as it was centered around food, hotels airports or other things that i learned countless times. Anyway i applied for the Glasgow team actually because one of my lecturers Raya pointed it out to me as i am interested in this feild for postgraduate study. I think it will be a great oppertunity to learn more about the biological side of things as well as learning a new programming language (Matlab) and new modelling skills. Its also a great way to learn how you need to work as a team with different people from different feilds of study and how to utalise different peoples skills apart from anything its also a great way to meet new people. <br />
I am a total geek i read new scientist i love computer games and my favouritre website is http://www.why-is-the-sky-blue.tv/why-is-the-sky-blue.htm look at it it's awesome. I am currently learning to drive and for cheap fuel consumption my car (george the blue corsa) eats nedss so that it can run we decided it would benefit everyone, the emmisions would be cleaner and glasgow would be safer and alot more peaceful. I have no pictures but i have added a very accurate painting i did of george [[Image:George.JPG]]</div>Rachhttp://2007.igem.org/wiki/index.php/File:George.JPGFile:George.JPG2007-07-11T17:28:22Z<p>Rach: </p>
<hr />
<div></div>Rachhttp://2007.igem.org/wiki/index.php/User:RachUser:Rach2007-07-11T17:25:03Z<p>Rach: </p>
<hr />
<div>[[Image:IMG_0292.JPG]]<br />
'''A bit about me'''<br />
<br />
Well i'm Rachael or Rach to my friends. I am currently going into fourth year of joint honouyrs in maths and statistics which i enjoy (although i will deny it during any stressful period). I'm a complete stress monkey and should be avoided during exams unless you have lucozade and chocolate. In the future i'd love to do my phd in some kind of modelling and biological systems but i'm not sure exactly of the path i would like to follow and of course i need to get the grades first. If i do do this i would love to be a research assistant at glasgow uni and then go on to possibly be a lecturer but again thats all in the future i may run away and become a secret agent before that.i love reading, rock climbing, hiking, I was learning to read and write mandarin chinese which i loved studying until my teacher moved to Malaysia and now i'm a bit swamped with the whole IGEM thing but i can still probably hold a small conversation as long as it was centered around food, hotels airports or other things that i learned countless times. Anyway i applied for the Glasgow team actually because one of my lecturers Raya pointed it out to me as i am interested in this feild for postgraduate study. I think it will be a great oppertunity to learn more about the biological side of things as well as learning a new programming language (Matlab) and new modelling skills. Its also a great way to learn how you need to work as a team with different people from different feilds of study and how to utalise different peoples skills apart from anything its also a great way to meet new people. <br />
I am a total geek i read new scientist i love computer games and my favouritre website is http://www.why-is-the-sky-blue.tv/why-is-the-sky-blue.htm look at it it's awesome</div>Rachhttp://2007.igem.org/wiki/index.php/File:IMG_0292.JPGFile:IMG 0292.JPG2007-07-11T17:20:16Z<p>Rach: </p>
<hr />
<div></div>Rachhttp://2007.igem.org/wiki/index.php/Dry_to_WetDry to Wet2007-07-11T15:02:48Z<p>Rach: /* Michaelis-Menton */</p>
<hr />
<div>=== Mass-Action Reaction Modelling ===<br />
<br />
[[Image:simpledecay.jpg|400px]]<br />
<br />
[[Image:reversible.jpg|400px]]<br />
<br />
[[Image:addition.jpg|400px]]<br />
<br />
[[Image:enzyme.jpg|400px]]<br />
<br />
=== RKIP network ===<br />
After gaining a thorough understanding of methods involved with modeling simple mass-action reactions, we can move on to more complex systems such as the RKIP network.<br><br />
[[Image:RKIP network.JPG | 700px]]<br><br />
In the above diagram, substrates, enzymes and substrate/enzyme complexes are represented by numbered circles, rate constants are represented by numbered squares. By isolating individual species and their direct peripheral species (those being formed from or forming the isolated species) we are able to treat the group as a simple mass-action reaction. A differential equation is then found for each species based on the rate constants and code can be written and a graph plotted showing the trend of all the species’ concentration over time giving the following graph:<br><br />
[[Image:RKIP network graph.jpg]]<br><br />
<br />
=== Sensitivity ===<br />
''An insight into a system's sensitivity will show how the variation of a model can be apportioned qualitatively or quantitatively to different sources of variation''<br><br />
<br />
One method of exposing the variation of a model is to program a loop exposing a modelled reaction to increasing values of a chosen constant. This process was followed with the metabolic pathway showing in [[Mass-Action Reaction Modelling]] and ploted on a graph showing the response of all 4 species for a set range of varying K2 values from 1 to 10 where 10 is highlighted red.<br><br />
[[Image: metabolic sensetivity response.jpg | 800px]]<br />
<br />
=== Michaelis-Menton ===<br />
''This was taken from 'Biochemistry' by 'Straya''<br><br />
Anybody who has done any sort of biological study will know michaelis menton what i am trying to acheive here is to take it from the basics so as to equate it to the equations we will be using to model the system and to give the biologists an idea of what values and models we need. In this case all k values are rate constants and [] means concentration and E is enzyme, S is substrate and [E]t is total enzyme concentration. <br><br />
The michaelis-Menton equation describes the kinetic properties of many enzymes. Consider the simple system A -> P<br />
The rate of V is the quantity of A that disappears over a specified unit of time which is equal to the rate of appearance of P. For this system V=k[A] where k is the rate constant. <br />
The simplest model that accounts for the kinetic properties of many enzymes is (i will add it in when i have figured out how to)<br><br />
what we want is an expression that relates the rate of catalysis to the concentrations of substrate and enzyme and the rates of the individual steps.<br><br />
Our starting point is that the catalytic rate is equal to the product of the ES complex and k3.<br />
'''Vo=K2[ES]''' ''call this '''eqn(1)''' as i will be referring to it again''<br />
Now expressing [ES]in terms of known quantities the rates of '''formation''' and '''breakdown''' of [ES] are given by:<br />
'''formation [ES] = k1*[E]*[S]'''<br />
'''breakdown [ES] = (k2+k3)*[ES]'''<br />
A steady state occurs when the rates of formation and breakdown of the ES complex are equal, this gives the formula<br />
'''k1*[E]*[S]=(k2+k3)*[ES]'''<br />
which then gives <br />
'''[E][S] / [ES]=(k2+k3) / k1''' <br> <br />
This can be simplified by defining '''Km''' called the michaelis constant<br><br />
'''Km = (k2+k3) / k1'''<br />
from this we get<br />
'''[ES] = [E][S] / Km''' ''call this '''eqn(2)''' as i will be referring to it again'' <br><br />
Now examining the numerator of this equation: because substrate is usually present at much higher concentrations than the enzyme, the concentration of uncombines substrate [S]is very nearly equal to the total substrate concentration. The concentration of enzyme [E] is equal to '''[E]t - [ES]''' now substituting this into ''eqn(2)'' and after some simplification we get<br />
'''[ES]=([E]t*[S]) / ([S]+Km)'''<br />
by substituting this expression into ''eqn(1)'' we get<br />
'''Vo = (k2[E]t*[S]) / ([s]+Km)'''<br />
The maximised rate Vmax is obtained when the catalytic sites on the enzyme are saturated with substrate i.e '''[ES] = [E]t''' thus<br />
'''Vmax = k2*[E]t<br />
this gives the Michaelis Menton equation <br />
'''Vo = Vmax*([S] / ([S]+km)) ''' ''call this '''eqn(3)''' as i will refer to it again'' <br />
when '''[S]=Km''' then '''Vo = Vmax / 2'''. Thus Km is equal to the substrate concentration at which the reaction rate is half its maximal value.<br><br />
<br />
=== Sum & And Promoters ===<br />
<br />
=== Application ===<br />
<br />
<math>x</math></div>Rachhttp://2007.igem.org/wiki/index.php/Dry_to_WetDry to Wet2007-07-11T13:59:17Z<p>Rach: /* Michaelis-Menton */</p>
<hr />
<div>=== Mass-Action Reaction Modelling ===<br />
<br />
[[Image:simpledecay.jpg]]<br />
<br />
[[Image:reversible.jpg]]<br />
<br />
[[Image:addition.jpg]]<br />
<br />
[[Image:enzyme.jpg]]<br />
<br />
=== RKIP network ===<br />
After gaining a thorough understanding of methods involved with modeling simple mass-action reactions, we can move on to more complex systems such as the RKIP network.<br><br />
[[Image:RKIP network.JPG]]<br><br />
In the above diagram, substrates, enzymes and substrate/enzyme complexes are represented by numbered circles, rate constants are represented by numbered squares. By isolating individual species and their direct peripheral species (those being formed from or forming the isolated species) we are able to treat the group as a simple mass-action reaction. A differential equation is then found for each species based on the rate constants and code can be written and a graph plotted showing the trend of all the species’ concentration over time giving the following graph:<br><br />
[[Image:RKIP network graph.jpg]]<br><br />
<br />
=== Sensitivity ===<br />
''An insight into a system's sensitivity will show how the variation of a model can be apportioned qualitatively or quantitatively to different sources of variation''<br><br />
<br />
One method of exposing the variation of a model is to program a loop exposing a modelled reaction to increasing values of a chosen constant. This process was followed with the metabolic pathway showing in..... and ploted on a graph showing the response of all 4 species for a set range of varying K2 values from 1 to 10 where 10 is highlighted red.<br><br />
[[Image: metabolic sensetivity response.jpg | 800px]]<br />
<br />
=== Michaelis-Menton ===<br />
Anybody who has done any sort of biological study will know michaelis menton what i am trying to acheive here is to take it from the basics so as to equate it to the equations we will be using to model the system and to give the biologists an idea of what values and models we need.<br />
The michaelis-Menton equation describes the kinetic properties of many enzymes. Consider the simple system A -> P<br />
The rate of V is the quantity of A that disappears over a specified unit of time which is equal to the rate of appearance of P. For this system V=k[A] where k is the rate constant. <br />
The simplest model that accounts for the kinetic properties of many enzymes is<br />
<br />
=== Sum & And Promoters ===<br />
<br />
=== Application ===</div>Rachhttp://2007.igem.org/wiki/index.php/User:RachUser:Rach2007-07-11T13:31:43Z<p>Rach: </p>
<hr />
<div>'''A bit about me'''<br />
<br />
Well i'm Rachael or Rach to my friends. I am currently going into fourth year of joint honouyrs in maths and statistics which i enjoy (although i will deny it during any stressful period). I'm a complete stress monkey and should be avoided during exams unless you have lucozade and chocolate. In the future i'd love to do my phd in some kind of modelling and biological systems but i'm not sure exactly of the path i would like to follow and of course i need to get the grades first. If i do do this i would love to be a research assistant at glasgow uni and then go on to possibly be a lecturer but again thats all in the future i may run away and become a secret agent before that.i love reading, rock climbing, hiking, I was learning to read and write mandarin chinese which i loved studying until my teacher moved to Malaysia and now i'm a bit swamped with the whole IGEM thing but i can still probably hold a small conversation as long as it was centered around food, hotels airports or other things that i learned countless times. Anyway i applied for the Glasgow team actually because one of my lecturers Raya pointed it out to me as i am interested in this feild for postgraduate study. I think it will be a great oppertunity to learn more about the biological side of things as well as learning a new programming language (Matlab) and new modelling skills. Its also a great way to learn how you need to work as a team with different people from different feilds of study and how to utalise different peoples skills apart from anything its also a great way to meet new people. <br />
I am a total geek i read new scientist i love computer games and my favouritre website is http://www.why-is-the-sky-blue.tv/why-is-the-sky-blue.htm look at it it's awesome</div>Rach