title: A probabilistic framework for the exploration of enzymatic capabilities based on feasible kinetics and control analysis.
abstract: BACKGROUND
  Analysis of limiting steps within enzyme-catalyzed reactions is fundamental to understand their behavior and regulation. Methods capable of unravelling control properties and exploring kinetic capabilities of enzymatic reactions would be particularly useful for protein and metabolic engineering. While single-enzyme control analysis formalism has previously been applied to well-studied enzymatic mechanisms, broader application of this formalism is limited in practice by the limited amount of kinetic data and the difficulty of describing complex allosteric mechanisms.
  
  
  METHODS
  To overcome these limitations, we present here a probabilistic framework enabling control analysis of previously unexplored mechanisms under uncertainty. By combining a thermodynamically consistent parameterization with an efficient Sequential Monte Carlo sampler embedded in a Bayesian setting, this framework yields insights into the capabilities of enzyme-catalyzed reactions with modest kinetic information, provided that the catalytic mechanism and a thermodynamic reference point are defined.
  
  
  RESULTS
  The framework was used to unravel the impact of thermodynamic affinity, substrate saturation levels and effector concentrations on the flux control and response coefficients of a diverse set of enzymatic reactions.
  
  
  CONCLUSIONS
  Our results highlight the importance of the metabolic context in the control analysis of isolated enzymes as well as the use of statistically sound methods for their interpretation.
  
  
  CONCLUSIONS
  This framework significantly expands our current capabilities for unravelling the control properties of general reaction kinetics with limited amount of information. This framework will be useful for both theoreticians and experimentalists in the field.
authors.affiliation: Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia, Corner College and Cooper Rds (Bldg 75), The University of Queensland, QLD 4072, Brisbane, Australia.
authors.forename: Pedro A
authors.initials: PA
authors.lastname: Saa
authors.affiliation: Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia, Corner College and Cooper Rds (Bldg 75), The University of Queensland, QLD 4072, Brisbane, Australia. Electronic address: lars.nielsen@uq.edu.au.
authors.forename: Lars K
authors.initials: LK
authors.lastname: Nielsen
mesh_terms: D000255:Adenosine Triphosphate / Q000378:metabolism; D001499:Bayes Theorem; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism*; D007700:Kinetics; D009010:Monte Carlo Method; D009249:NADP / Q000378:metabolism; D010728:Phosphoenolpyruvate / Q000378:metabolism; D011336:Probability; D013816:Thermodynamics
chemical_list: D009249:NADP; D010728:Phosphoenolpyruvate; D000255:Adenosine Triphosphate; D003577:Cytochrome P-450 Enzyme System
keywords: Bayesian inference; Control analysis; Enzyme kinetics; Rate-limiting steps; Sequential Monte Carlo