Endy:Chassis engineering/Computational load modeling
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Introduction
Based on my thesis committee meeting and subsequent conversations with Drew, we decided that some aspects of my project would benefit from some modeling work. Firstly, we believe that modeling could better inform the design of feedback control for the dedicated systems of VM2.0. Secondly, we currently lack a clear model of how the demands of an engineered system vary with the system parameters, such as DNA copy number, promoter PoPS, RBS RiPS etc.
Initial questions
- What goals should I set for the modeling work to get some benefit from it without devoting a long period of time?
- What modeling approach should I adopt?
These questions are discussed below:
Modeling Objectives
- Build a simple model of gene expression that considers the finite resources of the cellular chassis and the fraction of those resources consumed by the gene expression process.
- Construct the simple model of gene expresssion in a modular fashion such that it can be used to model a genetic network.
- Use the model to test the network dynamics of a number of possible configurations of feedback control for VM2.0
Modeling Approach
Two main modeling approaches exist - deterministic, continuous models based on differential equations and discrete, stochastic simulation of individual biochemical reactions. Both of these approaches offer some advantages and disadvantages for me.
Continuous and deterministic modeling
- I have more experience with this
- Analytical solutions give greater insight
- Can do this in Matlab, quite quickly
- Computationally efficient
- Hard to model some of the things I'd like to model. For example if I want to explicitly model the numbers of ribosomes on the transcript I need to create species and reactions like the following mRNA.(Ribosome)n+Ribosome -> mRNA.(Ribosome)n+1
- All the usual drawbacks of continuous modeling apply when the molecule numbers are small.
