Modelling

Overview

The goal of the modelling component of our project was to find optimal pathway for maximizing production of PHB in E. coli. Furthermore, the model will be used to simulate the reactions that occur in the bacteria after it is transformed with our construct. After a detailed analysis and discussion with experts, the team decided to pursue flux balance analysis (FBA) and kinetic modelling.

Analysis of various modelling methods

Modelling Method: Flux balance analysis (FBA)

Questions addressed by model

• Can use maximization of PHB production as an objective function and determine the optimal steady-state flux distribution, then compare to what we observe in the lab.
• Can potentially compare PHB production between unmodified E. coli and E. coli after genetic modifications
• Explore the capabilities and limitations of biochemical networks
• Analysis of metabolic network robustness (Raman et al., 2009)
• Analysis of Genome-scale metabolic models

Pros

• Does not require kinetic parameters and can be computed fairly quickly for large networks (Orth et al., 2009).
• Proof of concept that our genetic modifications maximize PHB production from human feces
• Can compare model predictions (maximum theoretical yield) to experimental data and potentially troubleshoot the system.

Cons

• The accuracy of the FBA prediction largely depends on the accurate definition of the metabolic network, the various constraints and definition of biologically relevant objective functions.
• May not always be accurate because does not account for regulatory effects (activation of enzymes by protein kinases or regulation of gene expression) (Orth et al., 2009).

Modelling Method: Reaction kinetics of pathways for synthesis of PHB

Questions addressed by model

• Determine the rate limiting step in the pathway (and implement solutions to improve the rate of that step)
• Can potentially provide insight into the impact of gene order

Pros

• Relatively easy to translate metabolic network to mathematical terms (Schallau et al., 2010)
• Can be integrated with the project’s experimental data and used to reiterate model and lab experiments.

Cons

• Difficult to find parameters for many reactions in the pathway.
• When analyzing multiple pathways that interact with each other, we may have to assume constant reaction rates for simplicity.

Modelling Method: Steady state mass balance to predict amount of PHB

Questions addressed by model

• Helpful for cost analysis of PHB production
• Identify the least efficient parts of the process (in terms of conversion)
• Can be combined with cost analysis to figure out the areas of the process, which can be modified to improve PHB production while adding the least cost.
• Evaluate how much PHB will be produced depending on plant capacity (also combined with cost analysis to suggest the optimal scale at which PHB production is most feasible)

Pros

• Can be integrated with the cost analysis
• Can be integrated with process development.

Cons

• Cannot be integrated with experimental data.

Modelling Method: Translational kinetics to predict effect of codon optimization

Different bacteria have different tRNAs that are abundant in their cells. When genes from other species of bacteria are expressed in E. coli, they may have a very inefficient translation efficiency if the available tRNA pools are very different. The impact of the difference can be calculated using tAI (tRNA adaptation index) (Han et al., 2010). The tAI for a gene is a measure of the availability of the tRNAs that serve each codon in the gene (Han et al., 2010). Synonymous codon substitutions can affect translational kinetics, which subsequently affects the final protein structure and function (RegulonDB).

Pros

• Determine increase in translation efficiency after codon optimization.
• Determine effect of synonymous codon substitutions on protein folding (synthesis enzymes or membrane proteins involved in secretion).
• Further optimize sequence to take into account secondary binding structures etc to optimize mRNA translational efficiency.

Cons

• Cannot be integrated with experimental data.
• Molecular Dynamics Simulations can be used to model protein folding.
• Many unknown parameters involved.