The LASER database: Formalizing design rules for metabolic engineering.
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ABSTRACT:
SUBMITTER: Winkler JD
PROVIDER: S-EPMC8193242 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MOTIVATION:Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. RESULTS:Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ?1?h, e.g. studied in this article). AVAILABILITY AND IMPLEMENTATION:Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. CONTACT:math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
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Project description:Metabolic engineering efforts require enzymes that are both highly active and specific toward the synthesis of a desired output product to be commercially feasible. The 3-hydroxyacid (3HA) pathway, also known as the reverse ?-oxidation or coenzyme-A-dependent chain-elongation pathway, can allow for the synthesis of dozens of useful compounds of various chain lengths and functionalities. However, this pathway suffers from byproduct formation, which lowers the yields of the desired longer chain products, as well as increases downstream separation costs. The thiolase enzyme catalyzes the first reaction in this pathway, and its substrate specificity at each of its two catalytic steps sets the chain length and composition of the chemical scaffold upon which the other downstream enzymes act. However, there have been few attempts reported in the literature to rationally engineer thiolase substrate specificity. In this study, we present a model-guided, rational design study of ordered substrate binding applied to two biosynthetic thiolases, with the goal of increasing the ratio of C6/C4 products formed by the 3HA pathway, 3-hydroxy-hexanoic acid and 3-hydroxybutyric acid. We identify thiolase mutants that result in nearly 10-fold increases in C6/C4 selectivity. Our findings can extend to other pathways that employ the thiolase for chain elongation, as well as expand our knowledge of sequence-structure-function relationship for this important class of enzymes.
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Project description:Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools).
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