Concepts, Challenges and Successes in Modeling Thermodynamics of Metabolism

Abstract

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The modeling of the chemical reactions involved in metabolism is a daunting task. Ideally, the modeling of metabolism would use kinetic simulations, but these simulations require knowledge of the thousands of rate constants involved in the reactions. The measurement of rate constants is very labor intensive, and hence rate constants for most enzymatic reactions are not available. Consequently, flux-based approaches have been the methods of choice because they do not require the use of the rate constants of the law of mass action. However, this convenience also limits the predictive power of flux-based approaches in that the law of mass action is not used directly, making it very difficult to predict metabolite levels or energy requirements of pathways.An alternative to both of these approaches is to model metabolism using simulations of states rather than simulations of reactions, in which the state is defined as the set of all metabolite counts or concentrations. While kinetic simulations model reactions based on the likelihood of the reaction derived from the law of mass action, states are modeled based on likelihood ratios of mass action. Both approaches provide information on the energy requirements of metabolic reactions and pathways. However, modeling states rather than reactions has the advantage that the parameters needed to model states (chemical potentials) are much easier to determine than the parameters needed to model reactions (rate constants). Herein we discuss recent results, assumptions and issues in using simulations of state to model metabolism.

Keywords

• Adaptation, Biological
• Metabolism
• Simulations
• tricarboxylic acid cycle
• molecular motors
• statistical thermodynamics