Phenotypic design choices for enhanced two-stage microbial production processes

Microbial metabolism can be harnessed to produce a broad range of industrially important chemicals. Many microorganisms naturally produce some important compounds but do so with low efficiency. To target a more diverse range of chemicals, pathways for non-natural products can be designed and implemented. However, in order to improve these microbes toward the target of industrial production, their metabolism must be engineered by controlling metabolic flux through key pathways. The merits of microbial production processes are often measured using three key variables: titer, rate and yield (TRY). Each of these variables has an impact on the economic viability of any microbial production process. Previous research into improving these TRY metrics have examined the efficacy of decoupling microbial growth from chemical production to achieve enhanced production rates. However, there has been limited research into the choice of microbial phenotype for the growth and production stages of two-stage production processes. Moreover, the substrate uptake rates of microbes drop significantly upon reducing the growth rate, adding to the need for intelligent phenotype selection while designing strains for two-stage processes. In this work, we present a two-stage optimization framework that scans the phenotypic space of microbial metabolism to identify the correct choice of phenotypes during growth and production stages, along with the optimal time to switch between these stages to achieve required TRY values. Through this framework and using Escherichia coli as a model organism, we compare the performance of two-stage fermentation processes where dynamic pathway regulation is involved with one-stage fermentation processes that have static intervention strategies implemented for a range of naturally produced chemicals. Our results indicate that while one stage processes are better at achieving optimal yields, two-stage processes outperform them in achieving optimal production rates even after incorporating the effects of reduced substrate uptake rates during the production stage. We anticipate that this optimization framework would be invaluable in designing microbial strains and fermentation processes for industrial chemical productio

Multiobjective optimization of gene circuits for metabolic engineering

8th IFAC Conference on Foundations of Systems Biology in Engineering, Valencia, 15-18 October 2019Metabolic engineering has enabled the production of a wealth of chemicals with microorganisms. Classic strategies for pathway engineering rely on the expression of heterologous enzymes in a host that convert native intermediates into target products. Although traditional implementations are based on open-loop control, recent advances in gene circuit engineering offer opportunities for building feedback systems that dynamically control pathway activity. Here we present a framework for the design of metabolic control circuits based on multiobjective optimization. We show that positive and negative feedback loops produce a range of optimal dynamics along a Pareto front. Such regulatory loops define connectivities between pathway intermediates and enzymatic genes that trade-off metabolic production against the burden to the host. Our results lay the groundwork for the automated design of gene circuitry in applications at the interface of synthetic biology and metabolic engineeringPeer reviewe

Comprehensive review of models and methods for inferences in bio-chemical reaction networks

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed

Dynamically controlling metabolic valves to decouple and switch between phenotypic states.

Microorganisms are well positioned to address many societal concerns in areas such as health, sustainability, and energy. Rapid progress in systems biology and synthetic biology has drastically improved our ability to understand and engineer complex biological systems. Sustainable biochemical production has received significant attention over the past 30 years; however, these processes must often reach near theoretical maximum performance to become competitive with petroleum alternatives. Here, we implement synthetic biological circuits in order to gain finer control over microorganisms and surpass the theoretical limits of organisms lacking such control. To do so, we aim to identify a set of important reactions which can be dynamically controlled to redirect flux between two phenotypes: growth and production. Then, cells can rapidly accumulate in a growth stage, before a phenotypic switch to production. We have shown that this strategy can significantly improve process rates in two-stage production processes. Since the process of metabolic engineering is iterative, we tackle diverse challenges through the design-built-test-learn cycle. First, we implement and characterize a bistable transcriptional controller as a genetic memory element to drive gene expression. We show that an optimized controller can effectively decouple growth from lactic acid production and can be implemented for two-stage production. Second, we develop a strain design algorithm (MoVE) which can use a stoichiometric metabolic model to predict genetic engineering strategies using two distinct interventions: static knockouts and dynamically controlled metabolic valves. Using this algorithm, we have identified promising candidates for metabolic valves which can be used for a range of products. Finally, we develop liquid handling workflows and a data analysis framework (Impact) to allow for the rapid and thorough characterization of microbial physiology. We anticipate that these tools and techniques will expedite the implementation of efficient dynamic control strategies. Furthermore, we expect studies which improve the accuracy of metabolic models will reduce the development time to commercialization of metabolic engineering technologies.Ph.D.2019-11-19 00:00:0

MoVE identifies metabolic valves to switch between phenotypic states

Interventions in metabolic networks can improve microbes for chemical production. Here, the authors develop a tool to identify metabolic valves that can decouple growth and production to systematically improve the rate and yield of biochemical production processes

An Optimized Bistable Metabolic Switch To Decouple Phenotypic States during Anaerobic Fermentation

This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Synthetic Biology, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/pdf/10.1021/acssynbio.8b00284#.Metabolic engineers aim to genetically modify microorganisms to improve their ability to produce valuable compounds. Despite the prevalence of growth-coupled production processes, these strategies can significantly limit production rates. Instead, rates can be improved by decoupling and optimizing growth and production independently, and operating with a growth stage followed by a production stage. Here, we implement a bistable transcriptional controller to decouple and switch between these two states. We optimize the controller in anaerobic conditions, typical of industrial fermentations, to ensure stability and tight expression control, while improving switching dynamics. The stability of this controller can be maintained through a simulated seed train scale-up from 5 mL to 500 000 L, indicating industrial feasibility. Finally, we demonstrate a two-stage production process using our optimal construct to improve the instantaneous rate of lactate production by over 50%, motivating the use of these systems in broad metabolic engineering applications.Natural Sciences and Engineering Research Council, Ontario Ministry of Research and Innovatio

Automation assisted anaerobic phenotyping for metabolic engineering

Abstract Background Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. Results In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies—an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. Conclusions Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the ‘test’ phase of the design-build-test-learn cycle of metabolic engineering