Development of an algorithm for adaptive and dynamic modelling of multiple metabolic models

Abstract

One of the most relevant topics in the field of systems biology is the study of metabolic networks, as they offer a wide range of information on organisms in general, playing special importance to those used in Health and the Biotechnology industry. One of the most widely used methods to explore metabolism computationally is flux balance analysis (FBA), which can calculate the flow of metabolites through the metabolic network, seeking to optimise a given objective function, typically organism growth. A number of extensions to FBA allow for additional applications, such as DFBA (dynamic FBA), used to explore time-dependence. Curiously, most existing implementations still use a relatively inflexible deterministic approach. Work on optimisation of heterologous protein production at CNB revealed several limitations of DFBA and led to the development of a novel, flexible algorithm permitting full steered control of dynamic simulations (ADFBA, adaptive DFBA). Like DFBA, ADFBA is still limited to simulations with a single model. Recent work has extended deterministic DFBA for application in multi-model systems (MDFBA), but there is not yet a flexible method for dynamic modelling of multi-model systems (such as complex organisms or communities). In this work, we extend ADFBA to multi-model systems maintaining all its flexibility so that any change not inherent to the metabolic models can be simulated in an adaptive way, allowing for derived, separate, and independent responses by each of the component metabolic models of the system. Our algorithm, dubbed Multiple Adaptive DFBA (MADFBA) enables complex simulations of multi-tissue organisms, heterogeneous organism populations and their interactions, with adaptive response to external or internal changes to the system. We provide proof of concept examples that are necessarily limited due to the current lack of experimental reference data