Gene Knockout Identification Using an Extension of Bees Hill Flux Balance Analysis

Abstract

Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.

OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

“…There also exist numerous approximate solutions to the OptKnock model, including genetic algorithms 16 and swarm intelligence. 17 Designing strains that couple production to growth has received increasing attention in recent years, mainly due to the great production potential of growth-coupled strains in adaptive laboratory evolution. 18 Consequently, a number of computational tools along this direction have been developed to design strains with various growth-coupled phenotypes.…”

“…Some improvement strategies, such as GDBB and GDLS, have been proposed to improve the efficiency of OptKnock in solving the bilevel problem. There also exist numerous approximate solutions to the OptKnock model, including genetic algorithms and swarm intelligence . Designing strains that couple production to growth has received increasing attention in recent years, mainly due to the great production potential of growth-coupled strains in adaptive laboratory evolution .…”

In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

“…The power of FBA models has been underexploited, with most studies focused on simulating gene knock-outs [11][12][13][14][15][16]. The effects of forcing or constraining flux of metabolic reactions can also be predicted in addition to gene deletion or insertion.…”

NIHBA: A Network Interdiction Approach with Hybrid Benders Algorithm for Strain Design

“…GDLS used local search with multiple search paths to reduce the search space for each local MILP [Lun et al, 2009]. While finding optimal solutions are computationally costly for exact solvers, other studies resorts to inexact methods, such as genetic algorithms [Patil et al, 2005;Rocha et al, 2010] and swarm intelligence [Choon et al, 2015]. These methods, however, still scale poorly with the size of GSMM and are specially ineffective when a large number of genetic manipulations are allowed for high target production.In company with intensive computations, the resulting MILP often has weak LP relaxations due to disjunctive big-M constraints [Codato and Fischetti, 2006], another limitation of the current bilevel optimisation based methods.…”