Combining Biological Knowledge with Sampling Know-How: Kinetic Space Modeling & Control Over Growth in Chlamydomonas reinhardtii

Metabolic models aid in the rational genetic engineering and cultivation of valuable microorganisms, and genome-scale dynamic models of metabolism are highly desirable because they can describe time-based perturbations and complex enzymatic and gene interactions. However, kinetic modeling is considered challenging because of informational and computational limitations associated with parameterizing and solving large systems of nonlinear differential equations. We describe a methodology called `kinetic space' modeling, which utilizes the ample amount of available biological knowledge, while recognizing that biological constants required to parameterize dynamic systems are inherently uncertain, especially for less-characterized organisms. In this approach, Monte Carlo sampling is employed to generate ensembles of thousands of kinetic models allowable within known thermodynamic and experimental bounds. These ensembles are screened to identify models that replicate in vivoChlamydomonas reinhardtii<<