Authors: Meltem Apaydin; Liang Xu; Bo Zeng; Xiaoning Qian
Addresses: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA ' Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA ' Department of Industrial Engineering and Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA ' Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA
Abstract: Optimisation-based mathematical models provide ways to analyse and obtain predictions on microbial communities who play critical roles in the ecological system, human health and diseases. However, there are inherent model and data uncertainties from the existing knowledge and experiments so that the imposed models may not exactly reflect the reality in nature. Here, we aim to have a flexible framework to model microbial communities with uncertainty, and introduce P-OptCom, an extension of an existing method OptCom, based on pessimistic bilevel optimisation. This framework relies on the coordinated decision making between the single upper-level and multiple lower-level decision makers to better approximate community steady states even when the individual microorganisms' behavior deviate from the optimum in terms of their cellular fitness criteria. Our study demonstrates that without experimental knowledge in advance, we are able to analyse the trade-offs among the members of microbial communities and closely approximate the actual experimental measurements.
Keywords: microbial communities; pessimistic bilevel optimisation; stoichiometric-based genome-scale metabolic modelling.
International Journal of Computational Biology and Drug Design, 2020 Vol.13 No.1, pp.82 - 97
Received: 25 May 2018
Accepted: 24 Aug 2018
Published online: 13 Feb 2020 *