Team:TU Delft-Leiden/Modeling

Modeling Overview

We modeled all three different modules our project consists of, namely the landmine module, the Extracellular Electron Transport (EET) module and the conductive curli module. In order to achieve this, we had to use all kinds of different modeling methods.

Landmine module

An important part of our iGEM project is a promoter sensitive to DNT/TNT. We will use two promoters that are sensitive to DNT/TNT, namely ybiJ and ybiFB2A1, in our project. Of these promoters, not much is known other than the fact that they have a DNT/TNT-dependent response curve . Our goal was to find a model which would be able to reproduce the response curves of both promoters. To achieve this, we constructed two different models, both using Deterministic Modeling Methods. One model is based on a simple binding model of DNT to the promoter, the other is based on cooperative binding of DNT to the promoter. When based on the simple binding model, fits of promoter activation with respect to DNT concentration to the experimental data of [1] did not yield good results. However, when the fits were based on the cooperative binding model, we were able to match the experimental data in [1] really well, see figure 1.

EET Module

Flux Balance Analysis

In the wet lab, we integrated the Extracellular Electron Transport (EET) module of S. oneidensis into E. coli [reference]. For the modeling of the EET module, we wanted at first to gain insight in the consequences of the integration of the EET module into E. coli. To achieve this, we simulated the cell metabolism of E. coli including the EET module using the Flux Balance Analysis (FBA) method.
Our goal was to investigate the carbon metabolism providing the electrons for the EET module [reference]. Also, we want the EET pathway used by the cells in order to have a measurable electrical signal for our biosensor [reference].
From the FBA method, we conclude that in aerobic conditions the cell does not use the EET pathway, but oxygen gets reduced instead, as it is a stronger oxidizing agent. However, in anaerobic growth the cell does use the EET pathway to export electrons out of the cell. When the cell is grown on glucose, the growth rate will be higher than when the cell is grown on D-lactate.
We also conclude that in an experimental setting the EET pathway has a higher chance of being used when the cells are grown on D-lactate as the EET pathway is necessary in order for the cells to grow, while when grown on glucose and the EET pathway is turned off (represented by \(0 mmol \ (gDW)^{-1} \ hr^{-1}\) (per gram dry weight per hour) maximum EET flux) growth is still possible, see figure 2. From Flux Variability Analysis (FVA) we conclude that for maximum growth for each specific combination of carbon source uptake flux and maximum EET flux, only one possible EET flux is possible for both growth on glucose and growth on D-lactate, namely the EET flux shown in figure 2.

As performing the FBA method while maximizing the EET flux yielded no growth, we wondered if there are pathways possible that would yield growth. So, we performed FVA, the results can be found in figure 3. From this figure, we conclude that when maximizing the EET flux, there are pathways possible that yield growth, as the figure displays the difference between the optimized maximum value and the optimized minimum value of growth. We see that this value is not equal to zero everywhere in the figure, thus there are multiple pathways possible when maximizing EET flux that all yield different values for growth. Note that the EET flux is 2.8 times higher and 2 times higher in comparison to maximizing for growth rate, for glucose and D-lactate as a carbon source, respectively.

From figures 2 and 3 we conclude that there are different regions in which the cell can operate. In an experimental setting, it can be investigated in which region the cell actually operates and if it maximizes its growth rate or its EET flux. To be able to do this, the experimental observed pathway has to be compared to the possible pathways when maximizing the EET flux and to the pathway when maximizing the growth rate. From these regions, it can be deduced if the experimentally observed EET flux and growth rate are carbon source limited or limited by the maximum possible EET flux.
Finally, we investigated an extended model of E. coli metabolism. This model contains, in contrast to the previously used core model, L-lactate as a metabolite. Using the extended model, we found that for glucose and D-lactate as carbon sources, the maximized growth rate agreed quite well to the previous analysis's using the core model. Using L-lactate as a carbon source, we conclude that a steady state solution in which E. coli can grow on L-lactate and use the EET pathway is not possible. A possible way to obtain information about the EET flux when the cells are not in steady state as observed by Goldbeck et al. [2], would be by the use of dynamic flux balance analysis (dFBA), which can also model the dynamics of a system before it reaches steady state [3].

Deterministic Model of EET Complex Assembly

References

[1] S. Yagur-Kroll, S. Belkin et al., “Escherichia Coli bioreporters for the detection of 2,4-dinitrotoluene and 2,4,6-trinitrotoluene”, Appl. Microbiol. Biotechnol. 98, 885-895, 2014.

[2] C.P. Goldbeck, H.M. Jensen et al., “Tuning Promoter Strengths for Improved Synthesis and Function of Electron Conduits in Escherichia coli”, ACS Synth. Biol. 2, 150-159, 2013.

[3] R. Mahadevan, J.S. Edwards & F.J. Doyle, “Dynamic Flux Balanace Analysis of Diauxic Growth in Escherichia coli”, Biophys. J. 83, 1331-1340, 2002.