Trans-Dimensional Diffusive Nested Sampling for Metabolic Network Inference

Abstract

Bayesian analysis is particularly useful for inferring models and their parameters given data. This is a common task in metabolic modeling, where models of varying complexity are used to interpret data. Nested sampling is a class of probabilistic inference algorithms that are particularly effective for estimating evidence and sampling the parameter posterior probability distributions. However, the practicality of nested sampling for metabolic network inference has yet to be studied. In this technical report, we explore the amalgamation of nested sampling, specifically diffusive nested sampling, with reversible jump Markov chain Monte Carlo. We apply the algorithm to two synthetic problems from the field of metabolic flux analysis. We present run times and share insights into hyperparameter choices, providing a useful point of reference for future applications of nested sampling to metabolic flux problems.

Authors 3

1. Johann F. Jadebeck Aachen Computational Systems Biotechnology

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Computational Systems Biotechnology (AVT.CBS), RWTH Aachen University, 52062 Aachen, Germany

   Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

2. Wolfgang Wiechert Aachen Computational Systems Biotechnology

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Computational Systems Biotechnology (AVT.CBS), RWTH Aachen University, 52062 Aachen, Germany

   Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

3. Katharina Nöh corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany