External control of microbial populations for bioproduction: A modeling and optimization viewpoint

Abstract When engineering microbes for bioproduction, one is necessarily confronted to the existing tradeoff between efficient bioproduction, and maintenance of the cell physiology and growth. Moreover, because cellular processes at the single-cell level are coupled with population dynamics via selection mechanisms, this question should be investigated at the population level. Identifying the temporal induction profile that maximizes production on the long term is highly challenging. External control allows to dynamically adapt the strength of the induction from the outside based on intracellular readouts. It allows benchmarking various regulation functions and, coupled with modeling approaches, identifying and applying optimal strategies. In this review, we describe recent advances using quantitative approaches, modeling and control theory that pave the way to compute external stimulations maximizing long term production.

[1]  U. Alon,et al.  Optimality and sub-optimality in a bacterial growth law , 2017, Nature Communications.

[2]  Jibin Sun,et al.  Biomanufacturing: history and perspective , 2017, Journal of Industrial Microbiology & Biotechnology.

[3]  Julius von Kügelgen,et al.  A bacterial size law revealed by a coarse-grained model of cell physiology , 2019, PLoS Comput. Biol..

[4]  Morten Otto Alexander Sommer,et al.  Synthetic addiction extends the productive life time of engineered Escherichia coli populations , 2018, Proceedings of the National Academy of Sciences.

[5]  Jens Nielsen,et al.  Construction of mini‐chemostats for high‐throughput strain characterization , 2019, Biotechnology and bioengineering.

[6]  Mary J Dunlop,et al.  Controlling and exploiting cell-to-cell variation in metabolic engineering. , 2019, Current opinion in biotechnology.

[7]  Jason S Crater,et al.  Scale-up of industrial microbial processes , 2018, FEMS microbiology letters.

[8]  Andrea Y Weiße,et al.  Growth Defects and Loss-of-Function in Synthetic Gene Circuits. , 2019, ACS synthetic biology.

[9]  Christopher A. Voigt,et al.  Synthetic biology 2020–2030: six commercially-available products that are changing our world , 2020, Nature Communications.

[10]  Kristala L. J. Prather,et al.  Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit , 2017, Nature Biotechnology.

[11]  R. Kitney,et al.  Developing synthetic biology for industrial biotechnology applications , 2020, Biochemical Society transactions.

[12]  T. Hwa,et al.  Interdependence of Cell Growth and Gene Expression: Origins and Consequences , 2010, Science.

[13]  Jared E. Toettcher,et al.  Optogenetic regulation of engineered cellular metabolism for microbial chemical production , 2018, Nature.

[14]  G. Stan,et al.  Burden-driven feedback control of gene expression , 2017, Nature Methods.

[15]  S. Goyal,et al.  An Automated Tabletop Continuous Culturing System with Multicolor Fluorescence Monitoring for Microbial Gene Expression and Long-Term Population Dynamics. , 2021, ACS synthetic biology.

[16]  Jakob Ruess,et al.  Optimal control of an artificial microbial differentiation system for protein bioproduction , 2019, 2019 18th European Control Conference (ECC).

[17]  Zanmin Hu,et al.  The Potential for Microalgae as Bioreactors to Produce Pharmaceuticals , 2016, International journal of molecular sciences.

[18]  A light tunable differentiation system for the creation and control of consortia in yeast , 2021, Nature communications.

[19]  B. Teusink,et al.  Shifts in growth strategies reflect tradeoffs in cellular economics , 2009, Molecular systems biology.

[20]  F. Bertaux,et al.  Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight , 2020, Nature Communications.

[21]  Philipp Thomas Intrinsic and extrinsic noise of gene expression in lineage trees , 2019, Scientific Reports.

[22]  U. Alon,et al.  Optimality and evolutionary tuning of the expression level of a protein , 2005, Nature.

[23]  Richard M. Murray,et al.  Future systems and control research in synthetic biology , 2018, Annu. Rev. Control..

[24]  Johannes Geiselmann,et al.  A synthetic growth switch based on controlled expression of RNA polymerase , 2015, Molecular systems biology.

[25]  Pascal Hersen,et al.  The Promise of Optogenetics for Bioproduction: Dynamic Control Strategies and Scale-Up Instruments , 2020, Bioengineering.

[26]  Matthew Deaner,et al.  Recent advancements in fungal-derived fuel and chemical production and commercialization. , 2019, Current opinion in biotechnology.

[27]  T. Hwa,et al.  Emergence of robust growth laws from optimal regulation of ribosome synthesis , 2014, Molecular systems biology.

[28]  P. Swain,et al.  Mechanistic links between cellular trade-offs, gene expression, and growth , 2015, Proceedings of the National Academy of Sciences.

[29]  J. Keasling,et al.  Microbial engineering for the production of advanced biofuels , 2012, Nature.

[30]  Jean-Luc Gouzé,et al.  Dynamical Allocation of Cellular Resources as an Optimal Control Problem: Novel Insights into Microbial Growth Strategies , 2016, PLoS Comput. Biol..

[31]  Jean-Luc Gouzé,et al.  Optimal control of bacterial growth for the maximization of metabolite production , 2018, Journal of Mathematical Biology.

[32]  Tom Ellis,et al.  The second decade of synthetic biology: 2010–2020 , 2020, Nature Communications.

[33]  N. Barkai,et al.  Rethinking cell growth models. , 2016, FEMS yeast research.

[34]  Lorenzo Duso,et al.  Stochastic reaction networks in dynamic compartment populations , 2020, Proceedings of the National Academy of Sciences.

[35]  Fuzhong Zhang,et al.  Control strategies to manage trade-offs during microbial production , 2020, Current opinion in biotechnology.

[36]  J. Ruess,et al.  Beyond the chemical master equation: Stochastic chemical kinetics coupled with auxiliary processes , 2021, PLoS Comput. Biol..

[37]  Christina D. Smolke,et al.  Synthetic biology strategies for microbial biosynthesis of plant natural products , 2019, Nature Communications.

[38]  H. Alper,et al.  Applications, challenges, and needs for employing synthetic biology beyond the lab , 2021, Nature Communications.

[39]  Eric Klavins,et al.  A Low Cost, Customizable Turbidostat for Use in Synthetic Circuit Characterization , 2014, ACS synthetic biology.

[40]  Xiaomei Lv,et al.  Sequential control of biosynthetic pathways for balanced utilization of metabolic intermediates in Saccharomyces cerevisiae. , 2015, Metabolic engineering.

[41]  F. Rudolf,et al.  A shuttle vector series for precise genetic engineering of Saccharomyces cerevisiae , 2016, Yeast.

[42]  Brandon G Wong,et al.  Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER , 2018, Nature Biotechnology.