A cornerstone of statistical inference, the maximum entropy framework is being increasingly
applied to construct descriptive and predictive models of biological systems, especially …

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism
because data on the in vivo biochemistry are too sparse. Predictive large-scale models for …

The development of high-throughput 'omic techniques has sparked a rising interest in
genome-scale metabolic models, with applications ranging from disease diagnostics to crop …

In constraint-based metabolic modelling, physical and biochemical constraints define a
polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an …

Reaction rates (fluxes) in a metabolic network can be analyzed using constraint-based
modeling which imposes a steady state assumption on the system. In a deterministic …

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass
composition in response to diurnal light/dark cycles and nutrient availability. Here, we used …

Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte
Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling …

Which properties of metabolic networks can be derived solely from stoichiometry? Predictive
results have been obtained by flux balance analysis (FBA), by postulating that cells set …

Metabolic modelling has entered a mature phase with dozens of methods and software
implementations available to the practitioner and the theoretician. It is not easy for a …

The solution space of genome-scale models of cellular metabolism provides a map between
physically viable flux configurations and cellular metabolic phenotypes described, at the …