We have divided the entire system into two components: the blue light-inducible system and the lead bioremediation system and modelled them separately. The model is a result of separate studies for every module in these systems. We made deterministic models based on mass-action kinetics for the interactions between the different components and chose approximations for parameters when they were unknown.
Here are the different systems, represented by their genetic circuits:
The overall system has again been divided into two parts. The first part shows light inactivation due to the presence of the fusion protein YF1 and its effector protein FixJ, which in the absence of blue light activates the FixK2 promoter as described in Möglich, Moffat and Ayers[1]. This part is available in the registry as BBa_K3285000.
The second part is a regulatory system with the constitutively active promoter CIlam. The production of RFP is regulated by the cIts2 repressor. The cIts2 repressor is a modified cI repressor which is temperature-sensitive as described here by Jana et al. The cIts2 repressor is present in the repository as BBa_K3285001 and the whole part is present as BBa_K3285002.
The two parts are combined to give us the full system, that is the regulated blue light inducible system, which when put into a plasmid with mutD5 replacing RFP gives us the mutator plasmid.
[1] Möglich A, Ayers RA, Moffat K. Design and signaling mechanism of light‐regulated histidine kinases. J Mol Biol. 2009;385:1433–1444.
[2] Nandan Kumar Jana, Siddhartha Roy, Bhabatarak Bhattacharyya, Nitai Chandra Mandal, Amino acid changes in the repressor of bacteriophage lambda due to temperature-sensitive mutations in its cI gene and the structure of a highly temperature-sensitive mutant repressor, Protein Engineering, Design and Selection, Volume 12, Issue 3, March 1999, Pages 225–233, https://doi.org/10.1093/protein/12.3.225
The Biosensor Module is mainly involved in quantifying Lead in the system using fluorescence as the readout. In the biosensor, Lead ions in the system prevent a repressor protein (pbrR) from binding to an operator site. GFP is present downstream of the operator site. Therefore we get higher GFP intensities for higher lead concentrations. This part is available in the registry as BBa_K1758332.
The bioremediation system is an extension of the above system. The Lead sequestering protein (pbrD) is added downstream of GFP, which results in expression of pbrD only in presence on Lead. This systems also include a transporter protein (pbrT) which allows for Lead ion influx to the cell allowing more efficient sequestration.
We planned to do a Microbial Evolution and Growth Arena (MEGA) plate but with different concentration of heavy metal instead of antibiotics. Due to time constraints, we were not able to do this experiment but tried to simulate a part of it.
We tried to simulate what would happen when bacteria colony will face the transition zone where metal concentration start increasing (i.e. in MEGA plate at the boundary of two different metal concentration zone). Different bacterial colonies with different fitness will grow at a different rate, also their fitness would depend on metal concentration differently based on their ability to survive in higher metal concentration. [Note we didn’t consider metal avoidance or metal sequestering strategy differently, just for shake of simplicity.] We planned to do this simulation using agent-based modelling. Where each bacterium (or agent) would have their own fitness value depending on its mutation type and how much lead it is surrounded by, and its growth rate would be a function of fitness value. We also thought to implement mutation based on a random change in fitness value.
In the beginning, we started with MESA, which is an Agent-based modelling module in Python. For technical reasons (the way it batch activates the agents) we realized it’s not a proper environment for our simulation. So we switched to Gro, the cell programming language.
Using Gro at first we made heavy metal concentration zone (Pink colour), bacteria a sense that and produce GFP proportionally,( this step is just for ease in visualization). Depending on GFP concentration of body their fitness value will change. We have shown for two different types of colonies (which have a different rate of decrease in fitness in high metal concentration), affects their ability to invade in higher metal concentrations. Currently, our model is in a very preliminary stage, much work has to be done.
Here we start with two different types of cells having a different rate of decrease in fitness function in the metal zone.
Metal Gradient is created.
Cell having less decrease in fitness value in the metal zone seems to be able to invade the metal zone.
To access the models for the given systems, please click on the images in the given tabs.
