OptStrain: A computational framework for redesign of microbial production systems

Abstract

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.[Supplemental material is available online at www.genome.org. The Universal database can be found at http://fenske.che.psu.edu/Faculty/CMaranas/pubs.html.]A fundamental goal in systems biology is to elucidate the complete "palette" of biotransformations accessible to nature in living systems. This goal parallels the continuing quest in biotechnology to construct microbial strains capable of accomplishing an ever-expanding array of desired biotransformations. These biotransformations are aimed at products that range from simple precursor chemicals (Nakamura and Whited 2003;Causey et al. 2004) or complex molecules such as carotenoids (Misawa et al. 1991), to electrons in biofuel cells (Liu et al. 2004) or batteries (Bond et al. 2002;Bond and Lovley 2003), to even microbes capable of precipitating heavy metal complexes in bioremediation applications (Finneran et al. 2002;Lovley 2003;Methe et al. 2003). Recent developments in molecular biology and recombinant DNA technology have ushered in a new era in the ability to shape the gene content and expression levels for microbial production strains in a direct and targeted fashion (Stephanopoulos 2002). The astounding range and diversity of these newly acquired capabili...

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

“…In these studies, the E. coli GEM is 22 . Select exemplary metabolic engineering applications will be described in more detail.…”

“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

“…Through the application of computational methods that incorporate linear, mixed integer linear, and non-linear programming, it has been demonstrated that model-directed strain design can lead to increased metabolite production [20][21][22][23][24][25][26][27][28][29][30] . In these studies, the E. coli GEM is principally used to analyze the metabolite production potential of E. coli and identify metabolic interventions needed to enable the production of the product of interest.…”

“…In these studies, the E. coli GEM is principally used to analyze the metabolite production potential of E. coli and identify metabolic interventions needed to enable the production of the product of interest. Thus, E. coli strains have been systematically designed through in silico analysis to overproduce target metabolites such as lycopene 23,24 , lactic acid 25 , ethanol 26 , succinic acid 27,28 , Lvaline 29 , L-threonine 30 , additional amino acids 21 , as well as diverse products from hydrogen to vanillin 22 . Select exemplary metabolic engineering applications will be described in more detail.…”

“…(A) A drawing of a predicted effect from a loss of function mutation in a simple system is shown. Metabolic engineering studies have investigated in silico strain design using E. coli metabolic reconstructions to overproduce desired products [20][21][22][23][24][25][26][27][28][29][30] . (B) Recent studies utilizing the reconstruction in a prospective manner have aimed to use the current biochemical and genetic information included in the metabolic network along with additional data types to drive biological discovery, such as predicting genes encoding for orphan reactions 32,33,[35][36][37] .…”