Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints

Abstract

BackgroundOptimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation.MethodsIn this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock.ResultsOur new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies.

Escherichia colimetabolism under short-term repetitive substrate dynamics: Adaptation and trade-offs

“…minimization of metabolic adjustment – MOMA) [72]. It has been suggested that mutant E.coli strains redistribute their metabolic fluxes in such way to minimally divert from the wild-type metabolic network [72–74]. Our work is a comparable case, where the perturbation of the cells was not genetic, but kinetic, as a result of an intermittent substrate feeding.…”

A gene knockout strategy for succinate production using a hybrid algorithm of bees algorithm and minimization of metabolic adjustment

“…Table 1 also shows the comparison of experimental results for BAMOMA, OptKnock and MOMAKnock OptKnock had achieved succinate production of 6.21 mmol gDW -1 hr -1 which was the lower compared to BAMOMA with 8.1943 mmol gDW -1 hr -1 . Ren et al (2013) [15] proposed MOMAKnock as framework that identifies candidate knockout genes which leads to maximizing the production of targeted metabolite under MOMA assumption. MOMAKnock had obtained the production of 5.02 mmol gDW -1 hr -1 which was also lower compared to BAMOMA results.…”

Easier than We Thought - A Practical Scheme to Compute Pessimistic Bilevel Optimization Problem

“…Let l i be the dimension of h i . The CP model defined in (25)(26)(27) is equivalent to the following single-level formulation. min cx…”

“…To address such challenge, many computational strategies and algorithms have been proposed, developed and investigated (see [16,4,13,30] and references therein), the vast majority of which, however, is devoted to solving the optimistic formulation. As a result, those advanced and capable computing tools strongly support the real applications of optimistic bilevel models in transportation planning and capacity expansion [7,25], government policy making [5,12], revenue management [8,14], electricity market [20,19] and computational biology [26,9].On the contrary, the pessimistic model is thought to be much more difficult than the optimistic one, and we generally believe that the solution methodologies developed for the latter one cannot be directly applied. Up to now, the pessimistic bilevel problem only receives a very limited amount of attention.…”