Robust mutant strain design by pessimistic optimization.

BACKGROUND: Flux Balance Analysis (FBA) based mathematical modeling enables in silico prediction of systems behavior for genome-scale metabolic networks. Computational methods have been derived in the FBA framework to solve bi-level optimization for deriving "optimal" mutant microbial strains with targeted biochemical overproduction. The common inherent assumption of these methods is that the surviving mutants will always cooperate with the engineering objective by overproducing the maximum desired biochemicals. However, it has been shown that this optimistic assumption may not be valid in practice. METHODS: We study the validity and robustness of existing bi-level methods for strain optimization under uncertainty and non-cooperative environment. More importantly, we propose new pessimistic optimization formulations: P-ROOM and P-OptKnock, aiming to derive robust mutants with the desired overproduction under two different mutant cell survival models: (1) ROOM assuming mutants have the minimum changes in reaction fluxes from wild-type flux values, and (2) the one considered by OptKnock maximizing the biomass production yield. When optimizing for desired overproduction, our pessimistic formulations derive more robust mutant strains by considering the uncertainty of the cell survival models at the inner level and the cooperation between the outer- and inner-level decision makers. For both P-ROOM and P-OptKnock, by converting multi-level formulations into single-level Mixed Integer Programming (MIP) problems based on the strong duality theorem, we can derive exact optimal solutions that are highly scalable with large networks. RESULTS: Our robust formulations P-ROOM and P-OptKnock are tested with a small E. coli core metabolic network and a large-scale E. coli iAF1260 network. We demonstrate that the original bi-level formulations (ROOM and OptKnock) derive mutants that may not achieve the predicted overproduction under uncertainty and non-cooperative environment. The knockouts obtained by the proposed pessimistic formulations yield higher chemical production rates than those by the optimistic formulations. Moreover, with higher uncertainty levels, both cellular models under pessimistic approaches produce the same mutant strains. CONCLUSIONS: In this paper, we propose a new pessimistic optimization framework for mutant strain design. Our pessimistic strain optimization methods produce more robust solutions regardless of the inner-level mutant survival models, which is desired as the models for cell survival are often approximate to real-world systems. Such robust and reliable knockout strategies obtained by the pessimistic formulations would provide confidence for in-vivo experimental design of microbial mutants of interest.

An integrated in vivo and in silico analysis of the metabolism disrupting effects of CPI-613 on embryo-larval zebrafish (Danio rerio).

CPI-613 is a mitochondrial metabolism disrupter that inhibits tricarboxylic acid (TCA) cycle activity. The consequences of TCA cycle disruption on various metabolic pathways and overall organismal physiology are not fully known. The present study integrates in vivo experimental data with an in silico stoichiometric metabolism model of zebrafish to study the metabolic pathways perturbed under CPI-613 exposure. Embryo-larval life stages of zebrafish (Danio rerio) were exposed to 1 µM CPI-613 for 20 days. Whole-organism respirometry measurements showed an initial suppression of O2 consumption at Day 5 of exposure, followed by recovery comparable to the solvent control (0.01% DMSO) by Day 20. Comparison of whole-transcriptome RNA-sequencing at Day 5 vs. 20 of exposure showed functional categories related to O2 binding and transport, antioxidant activity, FAD binding, and hemoglobin complexes, to be commonly represented. Metabolic enzyme gene expression changes and O2 consumption rate was used to parametrize two in silico stoichiometric metabolic models representative of Day 5 or 20 of exposure. Computational simulations predicted impaired ATP synthesis, α-ketoglutarate dehydrogenase (KGDH) activity, and fatty acid ß-oxidation at Day 5 vs. 20 of exposure. These results show that the targeted disruption of KGDH may also impact oxidative phosphorylation (ATP synthesis) and fatty acid metabolism (ß-oxidation), in turn influencing cellular bioenergetics and the observed reduction in whole-organism O2 consumption rate. The results of this study provide an integrated in vivo and in silico framework to study the impacts of metabolic disruption on organismal physiology.