Across many disciplines, mathematical models of processes are key tools used to understand and improve those processes. Although no model can perfectly capture all the dynamics of a process, a good enough model can capture enough to help a user better understand the behaviors they observe, and make predictions about what changes to implement to improve the function of a process.
Our lab is developing new stoichiometric models of cyanobacterial metabolism. This type of model views the inside of the cell as a tiny factory. The genome sequence provides a parts list of all the molecular machines available inside the cell. These machines transform inorganic and organic nutrients into all of the components required to assemble a cell: amino acids and proteins, pigments, DNA, RNA, lipids, carbohydrates, and various cofactors. The inventory of all these cellular components is measured separately to define the output of the cell factory. A unique feature of microbial cell factories is that one of their main outputs is more factories, i.e. growth – but that’s not their only output.
Assembling all of the chemical reactions that occur inside the cell into a model allows the user to predict the distribution of one reaction vs. another (i.e. how many isoleucines are synthesized per NO3 assimilated) that leads to a maximum of some output, such as biomass or a product such as a biofuel or pharmaceutical. This modeled result serves as a road map for the synthetic biologist to modify an organism or its culture conditions to maximize whatever product is desired.
In conjunction with measurements of cellular metabolism from cultures growing on isotopically labeled precursors, these models can also help to identify metabolic bottlenecks, and to identify new pathways not previously predicted from an organism's genome sequence.