Constraint-based models of microbial physiology: Surprisingly versatile
| Autor: | Teusink, B., Robert Planque, Molenaar, D., Bruggeman, F. |
|---|---|
| Přispěvatelé: | Systems Bioinformatics, AIMMS |
| Předmět: | |
| Zdroj: | Scopus-Elsevier STARTPAGE=5;ENDPAGE=10;TITLE=10th International Conference on Simulation and Modelling in the Food and Bio-Industry 2018, FOODSIM 2018 |
| Popis: | Microbial cells evolved a remarkable ability to adapt to environmental conditions, or to withstand otherwise detrimental mutations, which makes them often very resilient to man-made interventions. These properties arise from the integrative functioning of biological networks. Functional genomics has allowed the cost-effective measurement of the network components; however, we still mostly fail to understand how their interactions lead to cellular function and adaptation. For this, modeling is required. Current mainstream constraint-based metabolic modeling efforts largely focus on the metabolic network only, albeit at genome-scale. They are based on reaction stoichiometry only, but nonetheless can be extremely valuable for mostly exploratory analysis of the metabolic potential of an organism. However, because these “Flux Balance Analysis” (FBA) methods lack important parts of the cell –with their associated constraints- they often fail to predict changes in common regulatory strategies. One view that is becoming dominant in cellular physiology, is that physical and (bio)chemical constraints limits protein content and synthesis, impacting on how resources are partitioned over growth and stress processes to optimize fitness (“cellular economics”). Such constraints can lead to (evolutionary) trade-offs and can explain a large number of microbial physiological phenomena, such as overflow metabolism or catabolite repression. Current efforts in the modeling field aim to include such resource constraints into the constraint-based modeling format. We have developed theory to understand what the solution is to a flux maximization problem under resource constraints. We found that Elementary Flux Modes (EFMs), mathematical definitions of minimal patways, are the flux maximisers. Although the number of EFMs is enormous (in the millions), the number of active constraints determines the maximum number of active EFMs at optimum. So complexity of the flux space seems to be determined by the constraints, not by the seemingly infinite possibilities. Still, the resource allocation perspective is developed for steady-state growth under constant environments. What happens during dynamic growth conditions is largely unexplored. Such analysis requires dynamic models, one of which is dynamic FBA. We conclude that the constraint-based modeling approach is a powerful and versatile approach to explain the physiology of a cell through the interactions of its molecular components and the governing physico-chemical and biochemical constraints. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|