Comparing and Updating R Packages using MCMC Algorithms for Linear Inverse Modeling of Metabolic Networks - Archive ouverte HAL
Pré-Publication, Document De Travail Année : 2024

Comparing and Updating R Packages using MCMC Algorithms for Linear Inverse Modeling of Metabolic Networks

Résumé

Gathered under the name of metabolic networks, trophic, biochemical, and urban networks are here handled as a single field. In the Linear Inverse Modeling framework, these highly complex objects of research are all mathematically represented by weighted oriented graphs whose vertices are compartments and edges the flows (or flux) of matter or energy. All the flows that satisfy realistic constraints belong to very anisotropic high dimensional polytopes that cannot be analytically determined. Sampling the polytope yields a set of possible scenarios for the metabolic network. Different Monte Carlo Markov Chain (MCMC) algorithms together with their most recent implementations are scrutinized, leading to design an updated R package called samplelim. Comparison of the most recent implementations in terms of both computation time and sampling performances follows a methodology involving acknowledged and new statistical diagnostics and indexes. Application on real data metabolic networks of the three types shows that samplelim gathers the best properties of previous implementations of the MCMC algorithms.
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Dates et versions

hal-04455831 , version 1 (13-02-2024)

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  • HAL Id : hal-04455831 , version 1

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Valerie Girardin, Théo Grente, Nathalie Niquil, Philippe Regnault. Comparing and Updating R Packages using MCMC Algorithms for Linear Inverse Modeling of Metabolic Networks. 2024. ⟨hal-04455831⟩
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