Advances in de novo strain design using integrated systems and synthetic biology tools

Abstract

Recent efforts in expanding the range of biofuel and biorenewable molecules using microbial production hosts have focused on the introduction of non-native pathways in model organisms and the bio-prospecting of non-model organisms with desirable features. Current challenges lie in the assembly and coordinated expression of the (non-)native pathways and the elimination of competing pathways and undesirable regulation. Several systems and synthetic biology approaches providing contrasting top-down and bottom-up strategies, respectively, have been developed. In this review, we discuss recent advances in both in silico and experimental approaches for metabolic pathway design and engineering, with a critical assessment of their merits and remaining challenges.

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

“…The application of computational strain design methods in this field has not been reported so far. A host of strain design methods have been thoroughly reviewed by Ng et al Applying a pathway prediction method such as GEM‐Path, for instance, could decrease the time required to create a suitable design for the production of commodity chemicals from methane. The algorithm provided 1271 growth‐coupled designs for the production of 20 commodity chemicals in E. coli .…”

“…Methanol, which is the first product of aerobic methane oxidation, presents a similarly suitable feedstock for biotechnological applications. Strategies involving native methylotrophs have been reviewed by Clomburg et al, while achievements in synthetic implementations of methylotrophy have been expanded upon by Bennett et al To rationally improve strain designs of native and synthetic methanotrophs, in silico systems biology tools can be employed . The fundament of many in silico approaches is a formalized representation of an organism's metabolic network in the form of a genome‐scale metabolic model (GEM).…”

Transcription factor-based biosensors in biotechnology: current state and future prospects

“…While rational strain engineering is limited by the high physiological complexity of microbes, traditional random mutagenesis strategies are restricted by the selection and screening capacity, which requires a readily accessible phenotype linked to product formation (Dietrich et al 2010 ; Schallmey et al 2014 ). During the past decade, advances in synthetic biology significantly contributed to the establishment of novel metabolic engineering tools (Ng et al 2015 ; Wendisch 2014 ). For example, genetically encoded biosensors have proven to be of high value for various applications in strain engineering, dynamic pathway control and single-cell analysis.…”

Design, Optimization and Application of Small Molecule Biosensor in Metabolic Engineering

“…In order to commercialize these processes, effective biosynthetic pathways must be built in suitable hosts, followed by extensive optimization to achieve economically feasible yields, titers and productivities ( Liu D. et al, 2015 ). During the past decade, metabolite biosensors arise as one of the most powerful tools for metabolic engineering ( Ng et al, 2015 ). In every living cell, a wide variety of metabolites are sensed by a broad range of natural sensors/actuators such as riboswitches, transcription factors (TF) or enzymes, and proper responses are carefully exerted by cell to maintain its function.…”