Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactions. (7th May 2020)

• Record Type: Journal Article
• Authors: Frioux, Clémence; Dittami, Simon M.; Siegel, Anne
• Is Part Of: Biochemical Society transactions. Volume 48:Number 3(2020)
• Page Start: 901
• Page End: 913
• Publication Date: 2020-05-07
• Subjects: community selection -- gap-filling -- metabolic networks -- non-model organisms -- systems biology -- systems ecology; Biochemistry -- Congresses; 572
• DOI: 10.1042/BST20190667
• Languages: English
• ISSNs: 0300-5127

Abstract

Abstract : Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.