Naples
From 2007.igem.org
Contents |
About Us
Students:
- Giovanni Russo
- Lucia Marucci
- Velia Siciliano
- Irene Cantone
- Roberta Bergamasco[1]
- Maria Aurelia Ricci
- Mafalda Graziano
Instructors
- Diego di Bernardo
- Maria Pia Cosma
- Mario di Bernardo
Advisor
Tigem 
The Telethon Institute of Genetics and Medicine (TIGEM)[http://www.tigem.it] was created by the Italian Telethon Foundation in 1994. TIGEM mission is the understanding of the pathogenic mechanisms of genetic diseases with the aim of developing preventive and therapeutic strategies.TIGEM currently hosts 17 research groups, and a total of more than 120 persons, including students, postdoctoral fellows, staff scientists, technicians, and administrators and offers training programs in medical human genetics and Synthetic Biology in cooperation with the University of Naples Federico II.
University of Naples "Federico II" 
University of Naples "Federico II"[http://www.international.unina.it/] was established by the King of "Sacro Romano Impero" Federico II of Svevia. It's considered the most ancient public school of the world. It consists of 13 departments divided in three areas: Sciences and technologies, humanistic and social, medicine.
Our Project - YeSOil: A Yeast Sensor for real extra virgin Olive oil
The aim of our project is to engineer a synthetic biological network in yeast able to detect the quality of olive of oil, one of the most famouse product of Italy [http://en.wikipedia.org/wiki/Italy], now possible only through expensive and not-portable machines. In order to achieve this, we will modify Saccharomyces cerevisiae cells so that they will be able to change colour at different oleate concentrations.
Our model
After some brainstorming we had this idea:
In the picture the genes are represented with their interactions. The transcription factor for PHO4p is activated when there is a low oleic acid concentration, i.e. extra virgin, while PHO80 gene is activated when the oleic acid concentration is high, i.e. not edible oil. When PHO4p is activated it transcripts PHO8 which is integrated with GFP, indicating that the oil is extra virgin. When PHO80 is activated by the not edible oil promoter it creates a complex with PHO85: PHO80-PHO85. PHO80PHO85 phosforilates PHO4p and so it inhibits the trascription of PHO8. RFP is integrated with PHO80, indicating when the oil is not edible. When the level of oleic acid concentration is between extra virgin and not edible, the output will be a mix of green and red fluorescence.
The input to the system will be the level of oleic acid that will drive expression from appropriate promoters responsive to oleic acid cloned upstream of Pho80Pho85 and Pho4.
We recall that, for 100 gr of the oil, it will be:
- extra virgin, if the oleic acid conentration will be less than 0.8 gr
- virgin, if oleic the acid concentration will be less than 2 gr
- not edible, if the oleic acid concentration will be greater than 3-4 gr
Now, we need to convert gr in mol and we found that:
- the oil is extra virgin if the oleic acid concentration is less than 2.8 mM
- the oil is virgin if the oleic acid concentration is less than 7.1 mM
- the oil is not edible if the oleic acid concentration is greater than 7.1 mM
Oleate is the principal olive oil element and acidity indicator. The olive oil is defined "extra vergine" if it has an acidity lower than 0.8 %,"vergine" with an acidity lower than 2% and not edible if has an acidity higher than 3%.[http://en.wikipedia.org/wiki/Olive_oil] Oleate induces the transcription of genes involved in peroxisome biogenesis and stimulates the proliferation of these organelles in Saccharomyces cerevisiae. Fatty acid-mediated induction is based on a dramatic increase in transcription of several genes encoding peroxisomal functions due to the presence of an oleate response element (ORE) in their promoters.This upstream activating sequence is minimally defined by an inverted repeat of CGG triplets separated by a 15-18-nucleotide spacer. It constitutes the binding target for the transcription factors Oaf1p and Pip2p.
Modeling
Introduction
Over the past decades progress in measurement of rates and interactions of molecular and cellular processes has initiated a revolution in understanding of dynamical phenomena in cells. Generally speaking a dynamical phenomenon is a process that changes over time. Living cells are inherently dynamic! Indeed, to sustain the characteristic features of life (growth, cell division...) they need to extract and transform energy from their surroundings. This implies that cells function thermodinamically as open systems. So, we have encountered a new keyword: system. The most general definition for system is the following: a set of functional elements joint together to perform a specific task. Cells are astoundingly complex systems: they contain networks of thousands of biochemical interactions. System-level understanding, the approach of systems biology, requires a shift in the notion of what to look for. An understanding of genes and proteins is very important, but now the focus is on understanding system' structure and dynamics. Biologists use cartoons to capture the complexity of the networks, but because a system is not just an assembly of genes and proteins, its properties cannot be fully understood by drawing these diagrams. They, of course, represent a first step in our modeling, but can be compared to static roadmap, whereas what we really seek to know are the traffic patterns, why they emerge, how to control them. So, we will use a typical approach from systems and control theory.
Basic assumptions
We realized a reaction network as a system of ODEs. Clearly we need to guess working hypothesis:
- Component concentrations don't vary with respect to space
Whether it is a good assumption depends on time and space scales. In a yeast cell molecular diffution is sufficiently fast to mix proteins in less than one minute
- Component concentrations are continuous functions of time
This is true if the number of molecules of each species in the reaction volume is sufficiently large
Other important assumptions are the following:
- Transcription factor timescales are much larger than protein-protein interactions timescales
- Input changes are very rapid
We will use this assumptions to simplify our modeling
ODEs are very useful to represent molecular interaction. Indeed, applying a simple set of rules we can represent arbitrarily complex reaction networks as a set of coupled ODEs!
Mathematical model
We modeled the transcriptions factor interactions as Hill-like functions, while the protein-protein interactions are represented through a typical prey-predator model. All the hypotesis were used to get the ODE model. For the transcription factor level we have the following equations:
In the equations SA and SB indicates the inputs, i.e. if an oleate level is passed. Notice that the second assumption is used to obtain this equations form. [Pho4free]is the Pho4 concentration (the output from protein-protein interaction) that participates to transcription factor interaction. To get this value we write the equation for protein-protein interaction,:
Using the first assumption and the mass conservation law (since [Pho4]=[Pho4free]+[Pho4P] )we can obtain an expression for [Pho4free] in terms of [Pho80Pho85] and [PhO4] using steady state solution. The third equation in the ODEs system will become:
System analysis and simulations
The system was studied analytically and numerically. Both studies showed an unexpected structural stability property in the parameter range of physical interest. Indeed, we get an analytic expression for the Jacobian matrix (a diagonal matrix) whose determinant becomes 0 only if almost one of basical diluition factor becomes 0. After a careful study we found the reason of this behavior in the modeling process, particularly in the use of the second assumption. Matlab, Mathematica and Matcont were used to complete the study with simulations and bifurcation diagrams that confirmed the previous analytic study. Click here to see the bifurcation diagram of [PHO8], an output of the system. Once we get the steady state behavior of the outputs separately, we have to choose some index for the performances of the system. The ratio between the two outputs, i.e. PHO8/PHO8085, appears to be appropriated for our purpose. Click here to see PHO8/PHO8085 at the varying of system parameters.
In our last analysis we want to put oleic acid into the model: notice that the model used upfront has SA and SB as input and they are independent from the oleic acid concentration. Now we want to study the behavior of the system using SA and SB as functions of oleic acid concentration (o.a.), i.e. SA(o.a.), SB(o.a.). Click here to see them. Using these input functions, we can get the steady state behavior when o.a. varies for different parameter values. Click here for the results
To get informations about the transient response of the system and to test the detected parameters we used a new Matlab file which simply simulates the system. The results are presented here.
Yeast Strain
Yeast strain used is W303.
All DNA manipulations and subcloning were done in Escherichia coli.
Materials & Methods
We have adopted a strategy of parallel cloning.
A reporter gene is cloned in parallel into a vector containing one and two tandem copies of the oleate response elements from the FOX3 gene.
Background
Cloning Strategies in E.coli
Cloning Strategies in S.cerevisiae
Cloning Process
- Yeast DNA extraction
- PCR
- Agarose Gel Electrophoresis
- PCR Purification
- Digestion
- Extraction from Gel
- Ligation
- E.coli Transformation
- Mini and Midi prep
- Transformation
Experimental Results
Thanks to...
We are partly funded by the European Union SYNBIOCOMM project
