Optimal bioprocess design through a gene regulatory network - Growth kinetic hybrid model: Towards replacing Monod kinetics.

Currently, design and optimisation of biotechnological bioprocesses is performed either through exhaustive experimentation and/or with the use of empirical, unstructured growth kinetics models. Whereas, elaborate systems biology approaches have been recently explored, mixed-substrate utilisation is predominantly ignored despite its significance in enhancing bioprocess performance. Herein, bioprocess optimisation for an industrially-relevant bioremediation process involving a mixture of highly toxic substrates, m-xylene and toluene, was achieved through application of a novel experimental-modelling gene regulatory network - growth kinetic (GRN-GK) hybrid framework. The GRN model described the TOL and ortho-cleavage pathways in Pseudomonas putida mt-2 and captured the transcriptional kinetics expression patterns of the promoters. The GRN model informed the formulation of the growth kinetics model replacing the empirical and unstructured Monod kinetics. The GRN-GK framework's predictive capability and potential as a systematic optimal bioprocess design tool, was demonstrated by effectively predicting bioprocess performance, which was in agreement with experimental values, when compared to four commonly used models that deviated significantly from the experimental values. Significantly, a fed-batch biodegradation process was designed and optimised through the model-based control of TOL Pr promoter expression resulting in 61% and 60% enhanced pollutant removal and biomass formation, respectively, compared to the batch process. This provides strong evidence of model-based bioprocess optimisation at the gene level, rendering the GRN-GK framework as a novel and applicable approach to optimal bioprocess design. Finally, model analysis using global sensitivity analysis (GSA) suggests an alternative, systematic approach for model-driven strain modification for synthetic biology and metabolic engineering applications.

[1]  Toshimasa Yano,et al.  Dynamic behavior of the chemostat subject to substrate inhibition , 1969 .

[2]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[3]  Marija Cvijovic,et al.  Kinetic models in industrial biotechnology - Improving cell factory performance. , 2014, Metabolic engineering.

[4]  Alison S. Tomlin,et al.  GUI-HDMR - A software tool for global sensitivity analysis of complex models , 2009, Environ. Model. Softw..

[5]  C. Harwood,et al.  BenR, a XylS Homologue, Regulates Three Different Pathways of Aromatic Acid Degradation in Pseudomonas putida , 2000, Journal of bacteriology.

[6]  Efstratios N. Pistikopoulos,et al.  Transcriptional kinetics of the cross-talk between the ortho-cleavage and TOL pathways of toluene biodegradation in Pseudomonas putida mt-2. , 2016, Journal of biotechnology.

[7]  Peter A. Williams,et al.  Biodegradation kinetics of toluene, m-xylene, p-xylene and their intermediates through the upper TOL pathway in Pseudomonas putida (pWWO). , 1998, Microbiology.

[8]  J. Monod.  The Growth of Bacterial Cultures , 1949 .

[9]  K. Timmis,et al.  Transcriptional control of the Pseudomonas TOL plasmid catabolic operons is achieved through an interplay of host factors and plasmid-encoded regulators. , 1997, Annual review of microbiology.

[10]  Eva Balsa-Canto,et al.  Dynamic optimization of bioprocesses: efficient and robust numerical strategies. , 2005, Journal of biotechnology.

[11]  F. Rojo,et al.  The Target for the Pseudomonas putida Crc Global Regulator in the Benzoate Degradation Pathway Is the BenR Transcriptional Regulator , 2007, Journal of bacteriology.

[12]  J. Nielsen,et al.  Structured modeling of a microbial system: III. Growth on mixed substrates , 1991, Biotechnology and bioengineering.

[13]  Kenneth N Timmis,et al.  Pseudomonas putida: a cosmopolitan opportunist par excellence. , 2002, Environmental microbiology.

[14]  J. Sambrook,et al.  Molecular Cloning: A Laboratory Manual , 2001 .

[15]  S. Lee,et al.  Systems strategies for developing industrial microbial strains , 2015, Nature Biotechnology.

[16]  H. Westerhoff,et al.  Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering , 2016, Journal of The Royal Society Interface.

[17]  Jens Nielsen,et al.  The Impact of Systems Biology on Bioprocessing. , 2017, Trends in biotechnology.

[18]  M. Koutinas,et al.  The use of an oil–absorber–bioscrubber system during biodegradation of sequentially alternating loadings of 1,2-dichloroethane and fluorobenzene in a waste gas , 2007 .

[19]  J. Ramos,et al.  Integration of Signals through Crc and PtsN in Catabolite Repression of Pseudomonas putida TOL Plasmid pWW0 , 2005, Applied and Environmental Microbiology.

[20]  Christopher A. Voigt,et al.  Genetic circuit design automation , 2016, Science.

[21]  Athanasios Mantalaris,et al.  The regulatory logic of m-xylene biodegradation by Pseudomonas putida mt-2 exposed by dynamic modelling of the principal node Ps/Pr of the TOL plasmid. , 2009, Environmental microbiology.

[22]  A Kiparissides,et al.  On the model‐based optimization of secreting mammalian cell (GS‐NS0) cultures , 2015, Biotechnology and bioengineering.

[23]  Víctor de Lorenzo,et al.  The differential response of the Pben promoter of Pseudomonas putida mt-2 to BenR and XylS prevents metabolic conflicts in m-xylene biodegradation. , 2015, Environmental microbiology.

[24]  Michael Banf,et al.  Computational inference of gene regulatory networks: Approaches, limitations and opportunities. , 2017, Biochimica et biophysica acta. Gene regulatory mechanisms.

[25]  Costas D. Maranas,et al.  OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions , 2010, PLoS Comput. Biol..

[26]  Athanasios Mantalaris,et al.  Linking genes to microbial growth kinetics: an integrated biochemical systems engineering approach. , 2011, Metabolic engineering.

[27]  G. Klinzing,et al.  Competition for mixed substrates by microbial populations , 1977, Biotechnology and bioengineering.

[28]  Keng C. Soh,et al.  Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. , 2013, Biotechnology journal.

[29]  J. Collins,et al.  DIVERSITY-BASED, MODEL-GUIDED CONSTRUCTION OF SYNTHETIC GENE NETWORKS WITH PREDICTED FUNCTIONS , 2009, Nature Biotechnology.

[30]  Tomasz Arodz,et al.  ENNET: inferring large gene regulatory networks from expression data using gradient boosting , 2013, BMC Systems Biology.

[31]  U. Alon.  An introduction to systems biology : design principles of biological circuits , 2019 .

[32]  K. Timmis,et al.  Activation and Repression of Transcription at the Double Tandem Divergent Promoters for the xylR and xylS Genes of the TOL Plasmid of Pseudomonas putida , 1998, Journal of bacteriology.

[33]  Edward J. O'Brien,et al.  Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction , 2013, Molecular systems biology.

[34]  Eveline Volcke,et al.  A robust fed-batch feeding strategy independent of the carbon source for optimal polyhydroxybutyrate production , 2014 .

[35]  A. Ben-Zvi,et al.  Optimization of microalgal productivity using an adaptive, non-linear model based strategy. , 2012, Bioresource technology.

[36]  B. Palsson,et al.  Formulating genome-scale kinetic models in the post-genome era , 2008, Molecular systems biology.

[37]  G. Klečka,et al.  Kinetics of microbial growth on mixtures of pentachlorophenol and chlorinated aromatic compounds , 1988, Biotechnology and bioengineering.

[38]  K. Timmis,et al.  Transcriptional induction kinetics from the promoters of the catabolic pathways of TOL plasmid pWW0 of Pseudomonas putida for metabolism of aromatics , 1994, Journal of bacteriology.

[39]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[40]  Pietro Liò,et al.  Computational Modeling, Formal Analysis, and Tools for Systems Biology , 2016, PLoS Comput. Biol..

[41]  Wen‐Chien Lee,et al.  Modeling of ethanol fermentation using Zymomonas mobilis ATCC 10988 grown on the media containing glucose and fructose , 2000 .

[42]  Efstratios N. Pistikopoulos,et al.  Global Sensitivity Analysis Challenges in Biological Systems Modeling , 2009 .

[43]  Athanasios Mantalaris,et al.  The impact of succinate trace on pWW0 and ortho-cleavage pathway transcription in Pseudomonas putida mt-2 during toluene biodegradation. , 2017, Bioresource technology.

[44]  T. Egli,et al.  Growth Kinetics of Suspended Microbial Cells: From Single-Substrate-Controlled Growth to Mixed-Substrate Kinetics , 1998, Microbiology and Molecular Biology Reviews.

[45]  L. Serrano,et al.  Engineering stability in gene networks by autoregulation , 2000, Nature.

[46]  Steven P. Asprey,et al.  Toward Global Parametric Estimability of a Large-Scale Kinetic Single-Cell Model for Mammalian Cell Cultures , 2005 .

[47]  S. Lee,et al.  Fed‐batch culture of Escherichia coli for L‐valine production based on in silico flux response analysis , 2011, Biotechnology and bioengineering.

[48]  Riet De Smet,et al.  Advantages and limitations of current network inference methods , 2010, Nature Reviews Microbiology.

[49]  J. Nielsen,et al.  Integration of gene expression data into genome-scale metabolic models. , 2004, Metabolic engineering.

[50]  Microbial growth on substitutable substrates: characterizing the consumer-resource relationship. , 1997, Biotechnology and bioengineering.

[51]  D. Floreano,et al.  Revealing strengths and weaknesses of methods for gene network inference , 2010, Proceedings of the National Academy of Sciences.

[52]  Efstratios N. Pistikopoulos,et al.  'Closing the loop' in biological systems modeling - From the in silico to the in vitro , 2011, Autom..

[53]  Ron Weiss,et al.  Cellular computation and communications using engineered genetic regulatory networks , 2001, Cellular Computing.

[54]  Steffen Klamt,et al.  Exact quantification of cellular robustness in genome-scale metabolic networks , 2015, Bioinform..