Giving the cells what they need when they need it: Biosensor‐based feeding control

“Giving the cells exactly what they need, when they need it” is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time‐dependent “physiological fingerprint” of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen‐related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales—from microplate to 20 L bioreactor. We also developed a fully automated bioreactor—FCM interface for on‐line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed‐batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, —based on the specific needs of the cells, —opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.

[1]  F. Delvigne,et al.  Advances in automated and reactive flow cytometry for synthetic biotechnology. , 2023, Current opinion in biotechnology.

[2]  M. Khammash,et al.  Optogenetic closed-loop feedback control of the unfolded protein response optimizes protein production , 2022, bioRxiv.

[3]  M. Khammash,et al.  Platforms for Optogenetic Stimulation and Feedback Control , 2022, Frontiers in Bioengineering and Biotechnology.

[4]  M. Khammash,et al.  Rapid prototyping and design of cybergenetic single-cell controllers , 2021, Nature Communications.

[5]  F. Delvigne,et al.  Reducing phenotypic instabilities of a microbial population during continuous cultivation based on cell switching dynamics , 2021, Biotechnology and bioengineering.

[6]  Virginia W. Cornish,et al.  FRAME-tags: genetically encoded fluorescent markers for multiplexed barcoding and time-resolved tracking of live cells , 2021, bioRxiv.

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

[8]  E. von Lieres,et al.  dMSCC: a microfluidic platform for microbial single-cell cultivation of Corynebacterium glutamicum under dynamic environmental medium conditions. , 2020, Lab on a chip.

[9]  José L Avalos,et al.  Optogenetics and biosensors set the stage for metabolic cybergenetics. , 2020, Current opinion in biotechnology.

[10]  Ciaran L. Kelly,et al.  In situ characterisation and manipulation of biological systems with Chi.Bio , 2020, PLoS biology.

[11]  K. Zengler,et al.  Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes , 2020, npj Systems Biology and Applications.

[12]  Rui M. C. Portela,et al.  Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective , 2020, npj Systems Biology and Applications.

[13]  D. Weuster‐Botz,et al.  Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses , 2020, Microbial Cell Factories.

[14]  Weizhu Zeng,et al.  High-Throughput Screening Technology in Industrial Biotechnology. , 2020, Trends in biotechnology.

[15]  S. Qiang,et al.  Dissolved-oxygen feedback control fermentation for enhancing β-carotene in engineered Yarrowia lipolytica , 2020, Scientific Reports.

[16]  Massimo Morbidelli,et al.  Bioprocessing in the Digital Age: The Role of Process Models , 2019, Biotechnology journal.

[17]  F. Delvigne,et al.  Segregostat: a novel concept to control phenotypic diversification dynamics on the example of Gram‐negative bacteria , 2019, bioRxiv.

[18]  Jarka Glassey,et al.  Toward biotherapeutic product real-time quality monitoring , 2019, Critical reviews in biotechnology.

[19]  Alan G. Ryder,et al.  Cell culture media analysis using rapid spectroscopic methods , 2018, Current Opinion in Chemical Engineering.

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

[21]  D. Weuster‐Botz,et al.  Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives , 2018, Bioprocess and Biosystems Engineering.

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

[23]  P. Neubauer,et al.  Tools for the determination of population heterogeneity caused by inhomogeneous cultivation conditions. , 2017, Journal of biotechnology.

[24]  P. Goffin,et al.  Complete Genome Sequence of Escherichia coli BLR(DE3), a recA-Deficient Derivative of E. coli BL21(DE3) , 2017, Genome Announcements.

[25]  Alan G. Ryder,et al.  Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review , 2017, Applied spectroscopy.

[26]  Ian R. Lewis,et al.  Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing , 2016, Analytical and Bioanalytical Chemistry.

[27]  Robert Gustavsson,et al.  Soft sensor design for bioreactor monitoring and control , 2016 .

[28]  Dandan Zhou,et al.  The role of starvation in biomass harvesting and lipid accumulation: Co‐culture of microalgae–bacteria in synthetic wastewater , 2016 .

[29]  Patrick Fickers,et al.  Phenotypic variability in bioprocessing conditions can be tracked on the basis of on-line flow cytometry and fits to a scaling law. , 2015, Biotechnology journal.

[30]  F. Delvigne,et al.  Dynamic single-cell analysis of Saccharomyces cerevisiae under process perturbation: comparison of different methods for monitoring the intensity of population heterogeneity , 2015 .

[31]  Cleo Kontoravdi,et al.  Genetically-encoded biosensors for monitoring cellular stress in bioprocessing. , 2015, Current opinion in biotechnology.

[32]  Frank Delvigne,et al.  Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity. , 2014, Trends in biotechnology.

[33]  F. Delvigne,et al.  Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations , 2014, Biotechnology journal.

[34]  S. Sørensen,et al.  A low-cost, multiplexable, automated flow cytometry procedure for the characterization of microbial stress dynamics in bioreactors , 2013, Microbial Cell Factories.

[35]  Oliver Spadiut,et al.  On-line multiple component analysis for efficient quantitative bioprocess development. , 2013, Journal of biotechnology.

[36]  B. Ryall,et al.  Culture History and Population Heterogeneity as Determinants of Bacterial Adaptation: the Adaptomics of a Single Environmental Transition , 2012, Microbiology and Molecular Reviews.

[37]  L. Gustafsson,et al.  Starvation Response of Saccharomyces cerevisiae Grown in Anaerobic Nitrogen- or Carbon-Limited Chemostat Cultures , 2005, Applied and Environmental Microbiology.

[38]  P. Neubauer,et al.  Change of extracellular cAMP concentration is a sensitive reporter for bacterial fitness in high‐cell‐density cultures of Escherichia coli , 2004, Biotechnology and bioengineering.

[39]  John W. Eaton,et al.  Gnu Octave Manual , 2002 .

[40]  M. J. Waites,et al.  Industrial Microbiology: An Introduction , 2001 .

[41]  L. Poulsen,et al.  New Unstable Variants of Green Fluorescent Protein for Studies of Transient Gene Expression in Bacteria , 1998, Applied and Environmental Microbiology.

[42]  M. Merrick,et al.  Nitrogen control in bacteria , 1995 .

[43]  R. Edwards,et al.  Nitrogen control in bacteria. , 1995, Microbiological reviews.

[44]  W. Brondyk.  Selecting an appropriate method for expressing a recombinant protein. , 2009, Methods in enzymology.

[45]  S. Shojaosadati,et al.  Over-production of human interferon-γ by HCDC of recombinant Escherichia coli , 2007 .

[46]  W. Hancock,et al.  Proteomic Profiling of Escherichiacoli Proteins under High Cell Density Fed‐Batch Cultivation with Overexpression of Phosphogluconolactonase , 2005, Biotechnology progress.

[47]  S. Bhattacharya,et al.  High‐Level Expression of a Heterologous Gene in Escherichia coli in Response to Carbon‐Nitrogen Source and C/N Ratio in a Batch Bioreactor , 1997, Biotechnology progress.

[48]  B. Chung,et al.  Glucose-limited fed-batch culture of Escherichia coli for production of recombinant human interleukin-2 with the DO-stat method , 1992 .