# ETHZ/Engineering

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.:: EducatETH E. coli - Engineering Perspective ::.

##### .:: Introduction ::.

Having decided to work on an engineered biological system which exhibits learning, we elaborated on its design. Discussing with the biologists of the team, we realized that what we knew from the field of logic design as JK flip-flop with a latch may be implemented with biological parts using a modified toggle switch. Initial simulations showed us that it was possible to reach the desired behaviour. Therefore, a complete framework of differential equations describing the system was constructed and parameters were searched in the literature. Simulations performed with our new detailed model are very encouraging. In this page, the equations that model our system are and explained. The values that were chosen for the system parameters are presented and the results of our simulations are analyzed. References are provided at the end of the page. For an introduction to system modeling in synthetic biology, please read our modeling tutorial here.

##### .:: System Model ::.

Following the guidelines presented in our modeling guide, we divided the biological system into subsystems, each of which was modelled with a system of differential equations. According to what presented in the Biology Perspective, our system is composed the following three subsystems:

1. A subsystem of constitutively produced proteins (see Fig. 1),
2. The learning subsystem(see Fig. 2), and
3. The reporting subsystem (see Fig. 3).
The first two subsystems interact, and thus, they should be considered together. The third subsystem has no feedback with the other two, as it is only used for producing the appropriate fluorescent proteins. The subsystem of the constitutively produced proteins serves as a regulatory system, and can be modeled with the following three decoupled partial differential equations (see Fig. 1):
Subsystem 1: Constitutively produced proteins (Fig. 1)

The second subsystem is the main part of our model. It stores the information concerning the learned information for the chemical of interest, and drives the production of the appropriate reporter during the recognition phase. It is actually a toggle switch that reaches its steady state depending on the chemical that it has been previously exposed to (see Fig. 2):

Subsystem 2: Basic learning subsystem (toggle) (Fig. 2)

The third subsystem reports the state that our system is, during the different phases of learning and recognition. During the learning phase, this subsystem reports which chemical the cells are exposed to, and during the recognition phase, it reports if the cells recognize the chemicals that they are currently exposed to (see Fig. 3):

Subsystem 3: Reporting subsystem (Fig. 3)

Note that the three constitutively produced proteins LacI, TetR and LuxR exist in two different forms; as free proteins and in complexes they build with IPTG, aTc and AHL, respectively. We need to model this complex-forming procedure, with another set of differential equations (Fig. 4):

Allosteric regulation (see Fig. 4)

In order to have meaningful results from our simulations, we browsed through the literature in order to find appropriate values for our parameters. We reduced our parameter space by joining parameters together, and we gave reasonable estimates, for the values that could not be extracted from available publications. Since this is a hard part that every team has to face, we present the table with the chosen parameters below:

Parameters of the model (Fig. 5)
Parameter Value Description References
c1max 0.01 [mM/h] max. transcription rate of
constitutive promoter
(per gene)
Estimate
c2max 0.01 [mM/h] max. transcription rate of
luxR-activated promoter
(per gene)
Estimate
lhi 25 high-copy plasmid number Estimate
llo 5 low-copy plasmid number Estimate
ap22cII,LacI 0.1 - 0.2 basic production of
p22cII/LacI-inhibited genes
Discussion
ap22cII 0.1 - 0.2 basic production of
p22cII-inhibited genes
Discussion
acI,TetR 0.1 - 0.2 basic production of
cI/TetR-inhibited genes
Discussion
acI 0.1 - 0.2 basic production of
cI-inhibited genes
Discussion
ap22cII,TetR 0.1 - 0.2 basic production of
p22cII/TetR-inhibited genes
Discussion
acI,LacI 0.1 - 0.2 basic production of
cI/TetR-inhibited genes
Discussion
dLacI 2.31e-3 [pro sec] degradation of lacI [10]
dTetR 1e-5 [pro sec]/2.31e-3 [pro sec] degradation of TetR [9], [10]
dLuxR 1e-3 - 1e-4 [per sec] degradation of LuxR [6]
dcI 7e-4 [per sec] degradation of cI [7]
dYFP 6.3e-3 [per min] degradation of YFP suppl. mat. to Ref. [8]
dGFP 6.3e-3 [per min] degradation of GFP in analogy to YFP
dRFP 6.3e-3 [per min] degradation of RFP in analogy to YFP
dCFP 6.3e-3 [per min] degradation of CFP in analogy to YFP
KLacI 1.3e-3 - 2e-3 [mM/h] lacI repressor
dissociation constant
[2], [5]
KIPTG 1.5e-10 [mM/h] IPTG-lacI repressor
dissociation constant
[5]
KTetR 5.6 (+-2) [nM-1] tetR repressor
dissociation constant
[1]
KaTc 1120 (+-400) [nM-1] aTc-tetR repressor
dissociation constant
[1], [3]
KLuxR
• 0.003 [mM/s]
• 55 - 520 [nM]
luxR activator
dissociation constant
[6]
KAHL
• 0.009 [mM/s] - 0.1 [mM/s]
• 0.09 - 1 [µM]
AHL-luxR activator
dissociation constant
[6]
KcI 2e-3 [mM/h] cI repressor
dissociation constant
[5]
Kp22cII p22cII repressor
dissociation constant
nLacI 1 lacI repressor
Hill cooperativity
[5]
nIPTG 2 IPTG-lacI repressor
Hill cooperativity
[5]
nTetR 3 tetR repressor
Hill cooperativity
[3]
naTc 2 (1.5-2.5) aTc-tetR repressor
Hill cooperativity
[3]
nLuxR 2 luxR activator
Hill cooperativity
[6]
nAHL 1 AHL-luxR activator
Hill cooperativity
[3]
ncI 1.9 cI repressor
Hill cooperativity
[5]
np22cII p22cII repressor
Hill cooperativity

##### .:: References ::.

[1] Weber W., Stelling J., Rimann M., Keller B., Daoud-El Baba M., Weber C.C., Aubel D., and Fussenegger M., "A synthetic time-delay circuit in mammalian cells and mice", Proceedings of the National Academy of Sciences, vol. 104, no. 8, pp. 2643, 2007.
[2] Setty Y., Mayo AE, Surette MG, and Alon U., "Detailed map of a cis-regulatory input function", Proceeding of the National Academy of Sciences, vol. 100, no. 13, pp. 7702--7707, 2003.
[3] Braun D., Basu S., and Weiss R., "Parameter Estimation for Two Synthetic Gene Networks: A Case Study", IEEE Int'l Conf. Acoustics, Speech, and Signal Processing 2005, vol. 5, 2005.
[4] Fung E., Wong W.W., Suen J.K., Bulter T., Lee S., and Liao J.C., "A synthetic gene--metabolic oscillator", Nature, vol. 435, no. 7038, pp. 118--122, 2005, supplementary material.
[5] Iadevaia S., and Mantzaris N.V., "Genetic network driven control of PHBV copolymer composition", Journal of Biotechnology, vol. 122, no. 1, pp. 99--121, 2006.
[6] Goryachev AB, Toh DJ, and Lee T., "Systems analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constants", Biosystems, vol. 83, no. 2-3, pp. 178--187, 2006.
[7] Arkin A., Ross J., and McAdams H.H., "Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-Infected Escherichia coli cells", Genetics, vol. 149, no. 4, pp. 1633--1648, 1998.
[8] Colman-Lerner A., Chin T.E., and Brent R., "Yeast Cbk1 and Mob2 Activate Daughter-Specific Genetic Programs to Induce Asymmetric Cell Fates", Cell, vol. 107, no. 6, pp. 739--750, 2001.
[9] Becskei A., and Serrano L., "Engineering stability in gene networks by autoregulation", Nature, vol. 405, no.6786, pp.590--593, 2000.
[10] Tuttle L.M., Salis H., Tomshine J., and Kaznessis Y.N., "Model-Driven Designs of an Oscillating Gene Network", Biophysical Journal, vol. 89, no. 6, pp. 3873--3883, 2005.

##### .:: To Do ::.
• Christos: 1. Add gifs concerning the simulations
• Christos: 2. Remove the table with the parameters, once we have satisfying values.
• Katerina: 1. "The third subsystem has no feedback with the other two, as it is only used for producing the appropriate fluorescent proteins." Is this really true? gfp and rfp are situated in bigger parts with functionality. I think this needs to be rewritten more clearly.