title: Stochastic flux analysis of chemical reaction networks.
abstract: BACKGROUND
  Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes.
  
  
  RESULTS
  We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology.
  
  
  CONCLUSIONS
  We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.
authors: The Microsoft Research - University of Trento, Centre for Computational and Systems Biology, Trento, Italy. ozan@cosbi.eu.
authors: Ozan
authors: O
authors: Kahramanoğulları
authors: James F
authors: JF
authors: Lynch
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D008954:Models, Biological; D011381:Programming Languages; D013269:Stochastic Processes; D013997:Time Factors