Flux Design: In silico design of cell factories based on correlation of pathway fluxes to desired properties

Abstract

BackgroundThe identification of genetic target genes is a key step for rational engineering of production strains towards bio-based chemicals, fuels or therapeutics. This is often a difficult task, because superior production performance typically requires a combination of multiple targets, whereby the complex metabolic networks complicate straightforward identification. Recent attempts towards target prediction mainly focus on the prediction of gene deletion targets and therefore can cover only a part of genetic modifications proven valuable in metabolic engineering. Efficient in silico methods for simultaneous genome-scale identification of targets to be amplified or deleted are still lacking.ResultsHere we propose the identification of targets via flux correlation to a chosen objective flux as approach towards improved biotechnological production strains with optimally designed fluxes. The approach, we name Flux Design, computes elementary modes and, by search through the modes, identifies targets to be amplified (positive correlation) or down-regulated (negative correlation). Supported by statistical evaluation, a target potential is attributed to the identified reactions in a quantitative manner. Based on systems-wide models of the industrial microorganisms Corynebacterium glutamicum and Aspergillus niger, up to more than 20,000 modes were obtained for each case, differing strongly in production performance and intracellular fluxes. For lysine production in C. glutamicum the identified targets nicely matched with reported successful metabolic engineering strategies. In addition, simulations revealed insights, e.g. into the flexibility of energy metabolism. For enzyme production in A.niger flux correlation analysis suggested a number of targets, including non-obvious ones. Hereby, the relevance of most targets depended on the metabolic state of the cell and also on the carbon source.ConclusionsObjective flux correlation analysis provided a detailed insight into the metabolic networks of industrially relevant prokaryotic and eukaryotic microorganisms. It was shown that capacity, pathway usage, and relevant genetic targets for optimal production partly depend on the network structure and the metabolic state of the cell which should be considered in future metabolic engineering strategies. The presented strategy can be generally used to identify priority sorted amplification and deletion targets for metabolic engineering purposes under various conditions and thus displays a useful strategy to be incorporated into efficient strain and bioprocess optimization.

Trichoderma reesei RUT-C30 – thirty years of strain improvement

“…The importance of assessing the global effect of mutations is becoming increasingly apparent. The complete sequencing of the T. reesei QM6a genome makes the wild-type strain suitable for targeted metabolic engineering strategies for both improved cellulase production (reviewed by Kubicek et al, 2009) and heterologous protein production (reviewed by Boghigian et al, 2010;Matsuoka & Shimizu, 2010;Melzer et al, 2009). Metabolic modelling and flux analysis, in which the flow of carbon and nitrogen can be tracked through a metabolic network, may provide insight into bottlenecks along metabolic pathways that cause reduced yields.…”

Metabolic Flux Analysis in Systems Biology of Mammalian Cells

“…The stoichiometric models used for flux balancing can also be applied for in silico prediction of network characteristics (e.g. maximal yields, optimal pathways, minimum substrate requirements) [48][49][50] or prediction of optimal genetic modifications using different algorithms [51][52][53][54][55]. The importance of these targeted optimisation approaches is rapidly increasing, which is also caused by an increasing availability of genomic information as well as genome-scale models of different mammalian species [56][57][58].…”

Elementary Mode Analysis: A Useful Metabolic Pathway Analysis Tool for Reprograming Microbial Metabolic Pathways

“…This technique was applied to identify candidate reaction steps for improving lysine production in C. glutamicum and protein production (e.g. fructofuranosidase, glucoamylase, and epoxide hydrolyase) in A. niger (Driouch et al 2012 ;Melzer et al 2009 ) .…”