Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle

Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems

A Logic for Checking the Probabilistic Steady-State Properties of Reaction Networks

International audienceDesigning probabilistic reaction models and determining their stochastic kinetic parameters are major issues in systems biology. In order to assist in the construction of reaction network models, we introduce a logic that allows one to express asymp-totic properties about the steady-state stochastic dynamics of a reaction network. Basically, the formulas can express properties on expectancies, variances and co-variances. If a formula encoding for experimental observations on the system is not sat-isfiable then the reaction network model can be rejected. We demonstrate that deciding the satisfi-ability of a formula is NP-hard but we provide a decision method based on solving systems of polynomial constraints. We illustrate our method on a toy example

A Logic for Checking the Probabilistic Steady-State Properties of Reaction Networks

International audienceDesigning probabilistic reaction models and determining their stochastic kinetic parameters are major issues in systems biology. To assist in the construction of reaction network models, we introduce a logic that allows one to express asymptotic properties about the steady-state stochastic dynamics of a reaction network. Basically, the formulas can express properties on expectancies, variances, and covariances. If a formula encoding for experimental observa- tions on the system is not satisfiable, then the reaction network model can be rejected. We demonstrate that deciding the satisfiability of a formula is NP-hard, but we provide a decision method based on solving systems of polynomial constraints. We illustrate our method on a toy example