Analysis of Bioprocesses. Dynamic Modeling is a Must

Abstract Bioprocesses are contingent on the metabolic activity of microorganisms which is subject to dynamic variations as environmental changes occur concurrently. It is well known that such changes in the environment evoke regulatory response from the organismsto drive metabolism in specific ways but mathematical models have either neglected this feature altogether or incorporated them inadequately by ad hoc means.Metabolic regulation is accomplished by control of the levels and activities of enzyme catalyzing various reactions in the metabolic network. The facility for such control lies in the organism’s genetic infrastructure which enables selective synthesis of enzymes and allosteric regulation of their activities. The heavy demands ofa description of regulatory processes from this viewpoint have led researchers away towards simpler mathematical frameworks in which key experimental measurements are combined with heuristic concepts of metabolic function to make predictions of metabolic fluxes. An example is the flux balance approach (FBA) of Palsson and coworkers, which has had considerable impact in this regard as it combines the simple measurement of substrate uptake with the postulated regulatory choice of maximizing the yield of biomass. This steady state approach would clearly be strained to account for dynamic behavior and, as this talk would establish, other (nonlinear) aspects of metabolic dynamics. This presentation is predicated on the need for predicting dynamic metabolic behavior which alone can address engineering goals such as maximizing productivity, optimization and control of bioprocesses. It will report on the progress made by the speaker’s research group on a dynamic theory of metabolism based on viewing regulation as a strategy for maximizing the survival rate of the organism. This is accomplished by optimally investing its constrained enzyme synthesis resources on enzymes that catalyze reactions contributing the most to its survival. The resulting models have been termed cyberneticas they view the regulatory processes in a living cell as being “goal” oriented.While this cybernetic theory began (in the early nineteen eighties) at the level of describing mixed substrate utilization in various settings using only a coarse view of metabolism, the incorporation of detailed networks has occurred in the last decade leading to a dynamic description of metabolic processes without limit on the network size. Broadly, the network is viewed as a collection of metabolic options calledelementarymodes (EM), each representing a pathway through which substrate can beuptaken by the cell and engaged in a set of reactions resulting in an external product. The cybernetic models are based on the optimal uptake distributions of substrate through the different EMs so that the goal of survival is satisfied at every instant. This presentation will discuss how the foregoing idea has led to the creation of dynamic models with the capacity to be highly predictive of not only wild-type behavior but of specific mutant strains. Clearly, securing this predictive capacity on a wider scale would call for a comprehensive set of case studies. Towards this end, a software called AUMIC is under continuous development to enable the application of cybernetic models.