Quantifying complexity in metabolic engineering using the LASER database

Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes

In metabolic engineering, predicting gene overexpression targets remains challenging because both endogenous and heterologous genes in a large metabolic space can be candidates, in contrast to gene knockout targets that are confined to endogenous genes. We report the development of iBridge that identifies positive and negative metabolites exerting positive and negative impacts on product formation, respectively, based on the sum of covariances of their outgoing (consuming) reaction fluxes for a target chemical. Then, "bridge" reactions converting negative metabolites to positive metabolites are identified as overexpression targets, while the opposites as downregulation targets. Using iBridge, overexpression and downregulation targets are suggested for the production of 298 chemicals and validated for 36 chemicals experimentally demonstrated in previous studies. Finally, iBridge is employed to engineer Escherichia coli strains capable of producing 10.3 g/L of D-panthenol, a compound not previously produced, as well as putrescine and 4-hydroxyphenyllactate at enhanced titers, 63.7 and 8.3 g/L, respectively.

History-Driven Genetic Modification Design Technique Using a Domain-Specific Lexical Model for the Acceleration of DBTL Cycles for Microbial Cell Factories

The development of microbes for conducting bioprocessing via synthetic biology involves design-build-test-learn (DBTL) cycles. To aid the designing step, we developed a computational technique that suggests next genetic modifications on the basis of relatedness to the user's design history of genetic modifications accumulated through former DBTL cycles conducted by the user. This technique, which comprehensively retrieves well-known designs related to the history, involves searching text for previous literature and then mining genes that frequently co-occur in the literature with those modified genes. We further developed a domain-specific lexical model that weights literature that is more related to the domain of metabolic engineering to emphasize genes modified for bioprocessing. Our technique made a suggestion by using a history of creating a Corynebacterium glutamicum strain producing shikimic acid that had 18 genetic modifications. Inspired by the suggestion, eight genes were considered by biologists for further modification, and modifying four of these genes proved experimentally efficient in increasing the production of shikimic acid. These results indicated that our proposed technique successfully utilized the former cycles to suggest relevant designs that biologists considered worth testing. Comprehensive retrieval of well-tested designs will help less-experienced researchers overcome the entry barrier as well as inspire experienced researchers to formulate design concepts that have been overlooked or suspended. This technique will aid DBTL cycles by feeding histories back to the next genetic design, thereby complementing the designing step.

Reinforcement Learning for Bioretrosynthesis

Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

Exploiting transcriptomic data for metabolic engineering: toward a systematic strain design

Transcriptomics is now recognized as a primary tool for metabolic engineering as it can be used for identifying new strain designs by diagnosing current states of microbial cells. This review summarizes current application of transcriptomic data for strain design. Along with a few successful examples, limitations of conventionally used differentially expressed gene-based strain design approaches have been discussed, which have been major reasons why transcriptomic data are considerably underutilized. Recently, integrative network-based approaches interpreting transcriptomic data in the context of biological networks were invented to provide complimentary solutions for metabolic engineering by overcoming the limitations of conventional approaches. Here, we highlight recent pioneering studies in which integrative network-based methods have been used for providing novel strain designs.