Team:Edinburgh/Modeling/Platform

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Platform

We chose to use a platform created by another student from our university – Dominik Bucher - which is based on a cellular model and simulation policy [1] which were designed by John Karr et al. for the organism Mycoplasmium genitalium. As the organism with the smallest genome, this organism was picked by the authors of the original paper with the goal to create a complete fine-grained model of all the genes, proteins and metabolites inside the cell. Dominik stripped away all of the Mycoplasmium specificities of the model and re-implemented the original simulation platform with some improvements, making it more modular.

The way the simulation platform works is as follows: Many independent processes run on the platform simultaneously without knowing about each other’s existence. There is a central module that serves as a cellular task manager, switching between all processes in small time steps. At each time step each process reads the cell state (e.g. substance levels, resources, etc) , then changes the variables appropriately as a result. The task manager cycles through all the processes, then updates the cell state and takes a small step forward in time.

A major feature of the platform is that the processes which run on it are completely black-box. The simulation platform doesn’t know what they are doing or how they work, and there is no communication between the processes. The only requirement for a process is that it would be able to modify the cell state given the initial cell state when it is run for a given amount of time. This gives great freedom in the programming of processes – they can be programmed in any language or using any algorithm or method. This flexibility is very useful when programming specific circuit models as processes.

References:

1.Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B., Assad-Garcia, N., Glass, J.I., & Covert, M.W. (2012). A whole-cell computational model predicts phenotype from genotype Cell, 150, 389-401 DOI: 10.1016/j.cell.2012.05.044

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 <h3> Platform </h3>
-Our whole-cell model is founded upon a modular platform that serves as a cellular task manager; each process that takes place within the cell becomes simply an independent plugin, a function that is inserted into the platform and reads the various available cellular resources, then changes them appropriately as a result. The task manager cycles through all the processes, then updates the resources and takes a small step forward in time.
+We chose to use a platform created by another student from our university – Dominik Bucher - which is based on a cellular model and simulation policy [1] which were designed by John Karr et al. for the organism Mycoplasmium genitalium. As the organism with the smallest genome, this organism was picked by the authors of the original paper with the goal to create a complete fine-grained model of all the genes, proteins and metabolites inside the cell. Dominik stripped away all of the Mycoplasmium specificities of the model and re-implemented the original simulation platform with some improvements, making it more modular.
-This way, we simulate the interactions between the various cellular processes, be they native or introduced; hopefully we gain insight as to how the artificial circuit operates in the wider context of the cell. This information aids the development of better-informed designs, which have a symbiotic rather than a parasitic relationship with their host.
-Better yet than a living breathing computer cell is a computer cell that is accessible to everyone, despite its turbid programmatic depths. The modular platform at this model's core allows for easy mix-and-match of prepared modules (a.k.a. cellular processes) that are defined by the "black box" principle: the platform doesn't care about the module's internal workings; all it needs to know is the modified cell state (e.g. substance amounts, environment variables, resource availability, etc.) that each module produces in a given amount of time.
+The way the simulation platform works is as follows: Many independent processes run on the platform simultaneously without knowing about each other’s existence. There is a central module that serves as a cellular task manager, switching between all processes in small time steps. At each time step each process reads the cell state (e.g. substance levels, resources, etc) , then changes the variables appropriately as a result. The task manager cycles through all the processes, then updates the cell state and takes a small step forward in time.
-This idea can be extended to make the whole-cell model itself into a module which can run on a superplatform. It would be possible to choose the specific whole cell model that is required from a library of models (each depicting a different cell species or strain), or to create and program your own one. It may also be possible to specify some options, like turning on and off some features of the whole cell model or scaling the accuracy of the simulation, thus customizing it to be more coarse- or fine-grained according to tastes and inner beliefs.
+A major feature of the platform is that the processes which run on it are completely black-box. The simulation platform doesn’t know what they are doing or how they work, and there is no communication between the processes. The only requirement for a process is that it would be able to modify the cell state given the initial cell state when it is run for a given amount of time. This gives great freedom in the programming of processes – they can be programmed in any language or using any algorithm or method. This flexibility is very useful when programming specific circuit models as processes.
+<h2> References: </h2>
+.Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B., Assad-Garcia, N., Glass, J.I., & Covert, M.W. (2012). A whole-cell computational model predicts phenotype from genotype Cell, 150, 389-401 DOI: 10.1016/j.cell.2012.05.044
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