D. K. Agrawal, E. Franco, and R. Schulman, A self-regulating biomolecular comparator for processing oscillatory signals, Journal of The Royal Society Interface, vol.12, issue.111, p.20150586, 2015.

B. D. Anderson and J. B. Moore, Optimal Control: Linear Quadratic Methods, 1990.

F. Annunziata, A. Matyjaszkiewicz, G. Fiore, C. S. Grierson, L. Marucci et al., An orthogonal multi-input integration system to control gene expression in Escherichia coli, ACS Synthetic Biology, vol.6, issue.10, pp.1816-1824, 2017.

M. Ashyraliyev, Y. Nanfack, J. A. Kaandorp, and J. G. Blom, Systems biology: Parameter estimation for biochemical models, FEBS J, vol.276, issue.4, pp.886-902, 2009.

K. J. Astrom, Introduction to Stochastic Control Theory, 1970.

S. ,

R. Bailey and M. V. Maus, Gene editing for immune cell therapies, Nature Biotechnology, 2019.

H. C. Bernstein, S. D. Paulson, and R. P. Carlson, Synthetic Escherichia coli consortia engineered for syntrophy demonstrate enhanced biomass productivity, Journal of Biotechnology, vol.157, pp.159-166, 2012.

M. Bertero, Linear inverse and ill-posed problems, Advances in Electronics and Electron Physics, vol.75, pp.1-120, 1989.

S. Berthoumieux, M. Brilli, H. Jong, D. Kahn, and E. Cinquemani, Identification of linlog models of metabolic networks from incomplete high-throughput datasets, Proceedings of the ISMB conference 2011), vol.27, pp.186-195, 2011.

S. Berthoumieux, M. Brilli, D. Kahn, H. Jong, and E. Cinquemani, On the identifiability of metabolic network models, Journal of Mathematical Biology, vol.67, issue.6-7, pp.1795-832, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00762620

S. Berthoumieux, H. Jong, G. Baptist, C. Pinel, C. Ranquet et al., Shared control of gene expression in bacteria by transcription factors and global physiology of the cell, Molecular Systems Biology, vol.9, p.634, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00793352

, Inria Sophia-Antipolis -Méditerranée. url: team. inria.fr/biocore

, Equipe-BIOP-presentation

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

C. Briat, A. Gupta, and M. H. Khammash, Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks, Journal of the Royal Society Interface, vol.15, issue.143, p.20180079, 2018.

M. Campi and S. Garatti, Introduction to the Scenario Approach, 2019.

Y. Cao, W. Yu, W. Ren, and G. Chen, An overview of recent progress in the study of distributed multi-agent coordination, IEEE Transactions on Industrial Informatics, vol.9, issue.1, pp.427-438, 2013.

R. Chait, J. Ruess, T. Bergmiller, G. Tkacik, and C. C. Guet, Shaping bacterial population behavior through computer-interfaced control of individual cells, Nature Communications, vol.8, issue.1, p.1535, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01669883

O. T. Chis, J. R. Banga, and E. Balsa-canto, Structural identifiability of systems biology models: a critical comparison of methods, PLoS One, vol.6, issue.11, p.27755, 2011.

K. H. Cho, S. M. Choo, S. H. Jung, J. R. Kim, H. S. Choi et al., Reverse engineering of gene regulatory networks, IET Systems Biology, vol.1, issue.3, pp.149-63, 2007.

E. Cinquemani, Identifiability and reconstruction of biochemical reaction networks from population snapshot data, Processes (Special Issue on Computational Synthetic Biology), vol.6, issue.9, p.136, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01909765

E. Cinquemani, Stochastic reaction networks with input processes: Analysis and application to gene expression inference, Automatica, vol.101, pp.150-156, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01925923

E. Cinquemani, M. Agarwal, D. Chatterjee, and J. Lygeros, Convexity and convex approximations of discrete-time stochastic control problems with constraints, Automatica, vol.47, pp.2082-87, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00793041

E. Cinquemani, V. Laroute, M. Cocaign-bousquet, H. Jong, and D. Ropers, Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data, Proceedings of ISMB/ECCB conference 2017), vol.33, pp.301-310, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01607919

E. Cinquemani, F. Mairet, I. Yegorov, H. Jong, and J. Gouzé, Optimal control of bacterial growth for metabolite production: The role of timing and costs of control, Proceedings of the 17th European Control Conference, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02384034

A. Colman-lerner, A. Gordon, E. Serra, T. Chin, O. Resnekov et al., Regulated cell-to-cell variation in a cell-fate decision system, Nature, vol.437, pp.699-706, 2005.

, COSOFT -Control software for a system of mini-bioreactors. Inria Action de Développement Technologique, 2017.

H. Jong, Modeling and simulation of genetic regulatory systems: A literature review, Journal of Computational Biology, vol.9, issue.1, pp.67-103, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00072606

H. Jong, S. Casagranda, N. Giordano, E. Cinquemani, D. Ropers et al., Mathematical modeling of microbes: Metabolism, gene expression, and growth, Journal of the Royal Society Interface, vol.14, 2017.

H. Jong, C. Ranquet, D. Ropers, C. Pinel, and J. Geiselmann, Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria, BMC Systems Biology, vol.4, issue.1, p.55, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00784438

G. De-nicolao, G. Sparacino, and C. Cobelli, Nonparametric input estimation in physiological systems: Problems, methods, and case studies, Automatica, vol.33, issue.5, pp.851-70, 1997.

B. Delyon, M. Lavielle, and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm, Ann. Statist, vol.27, issue.1, pp.94-128, 1999.

A. Doucet, A. Smith, N. Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, 2001.

M. B. Elowitz, A. J. Levine, E. D. Siggia, and P. S. Swain, Stochastic gene expression in a single cell, Science, vol.297, issue.5584, pp.1183-1189, 2002.

S. Estrela, C. H. Trisos, and S. P. Brown, From metabolism to ecology: Crossfeeding interactions shape the balance between polymicrobial conflict and mutualism, The American Naturalist, vol.180, issue.5, pp.566-76, 2012.

K. Faust, F. Bauchinger, B. Laroche, S. Buyl, L. Lahti et al., Signatures of ecological processes in microbial community time series, p.120, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01891496

B. Finkenstädt, E. A. Heron, M. Komorowski, K. Edwards, S. Tang et al., Reconstruction of transcriptional dynamics from gene reporter data using differential equations, Bioinformatics, vol.24, issue.24, pp.2901-2907, 2008.

F. Fröhlich, A. Reiser, L. Fink, D. Woschée, T. Ligon et al., Multi-experiment nonlinear mixed effect modeling of singlecell translation kinetics after transfection, NPJ Systems Biology and Applications, vol.5, issue.1, 2018.

D. T. Gillespie, The chemical langevin equation, Journal of Chemical Physics, vol.113, pp.297-306, 2000.

D. T. Gillespie, A rigorous derivation of the chemical master equation, Physica A, vol.188, pp.404-425, 1992.

N. Giordano, F. Mairet, J. L. Gouzé, J. Geiselmann, and H. De-jong, Dynamical allocation of cellular resources as an optimal control problem: Novel insights into microbial growth strategies, PLoS Computational Biology, vol.12, issue.3, p.1004802, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01332394

J. Grefenstette, S. Kim, and S. Kauffman, An analysis of the class of gene regulatory functions implied by a biochemical model, Biosystems, vol.84, issue.2, pp.81-90, 2006.

E. Harvey, J. Heys, and T. Gedeon, Quantifying the effects of the division of labor in metabolic pathways, Journal of Theoretical Biology, vol.360, pp.222-242, 2014.

J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich et al., Identification of models of heterogeneous cell populations from population snapshot data, BMC Bioinformatics, vol.12, issue.1, p.125, 2011.

J. P. Hespanha, Modelling and analysis of stochastic hybrid systems, IEE Proceedings -Control Theory and Applications, vol.153, issue.5, pp.520-535, 2006.

J. Hesseler, J. K. Schmidt, U. Reichl, and D. Flockerzi, Coexistence in the chemostat as a result of metabolic by-products, Journal of Mathematical Biology, vol.53, pp.556-584, 2006.

A. Hilfinger and J. Paulsson, Separating intrinsic from extrinsic fluctuations in dynamic biological systems, PNAS, vol.108, issue.29, pp.12167-12172, 2011.

, IBIS. Project-Team

N. Ishii, Multiple high-throughput analyses monitor the response of E.coli to perturbations, Science, vol.316, issue.5824, pp.593-597, 2007.

J. Izard, C. Balderas, D. Ropers, S. Lacour, X. Song et al., A synthetic growth switch based on controlled expression of rna polymerase, Molecular Systems Biology, vol.11, issue.11, p.840, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01247993

S. J. Julier and J. K. Uhlmann, Unscented filtering and nonlinear estimation, Proceedings of the IEEE, vol.92, issue.3, pp.401-423, 2004.

T. Kailath, A. H. Sayed, and B. Hassibi, Linear Estimation, 2000.

M. Komorowski, B. Finkenstädt, C. Harper, and D. Rand, Bayesian inference of biochemical kinetic parameters using the linear noise approximation, BMC Bioinformatics, vol.10, issue.1, p.343, 2009.

O. Kotte, B. Volkmer, J. L. Radzikowski, and M. Heinemann, Phenotypic bistability in Escherichia coli 's central carbon metabolism, Molecular Systems Biology, vol.10, pp.736-736, 2014.

J. U. Kreft, C. M. Plugge, C. Prats, J. H. Leveau, W. Zhang et al., From genes to ecosystems in microbiology: Modeling approaches and the importance of individuality, Frontiers in Microbiology, vol.8, 2017.

L. Kuepfer, M. Peter, U. Sauer, and J. Stelling, Ensemble modeling for analysis of cell signaling dynamics, Nature Biotechnology, vol.25, issue.9, pp.1001-1007, 2007.

M. Lavielle, Mixed effects models for the population approach, Tasks, Methods & Tools. Chapman & Hall/CRC Biostatistics Series, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01122873

I. Lestas, J. Paulsson, N. E. Ross, and G. Vinnicombe, Noise in gene regulatory networks, IEEE Transactions on Automatic Control, vol.53, pp.189-200, 2008.

G. Lillacci, Y. Benenson, and M. H. Khammash, Synthetic control systems for high performance gene expression in mammalian cells, Nucleic Acids Research, vol.46, issue.18, pp.9855-9863, 2018.

G. Lillacci and M. Khammash, Parameter estimation and model selection in computational biology, PLoS Computational Biology, vol.6, issue.3, p.1000696, 2010.

. Lispb and T. Insa-laboratory,

L. Ljung, System Identification -Theory for the User, 1999.

A. Llamosi, A. M. Gonzalez-vargas, C. Versari, E. Cinquemani, G. Ferrari-trecate et al., What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast, PLOS Computational Biology, vol.12, issue.2, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01248298

J. Lygeros and M. Prandini, Stochastic hybrid systems: A powerful framework for complex, large scale applications, European Journal of Control, vol.16, pp.583-594, 2010.

M. L. Maeder and C. A. Gersbach, Genome-editing technologies for gene and cell therapy, Molecular Therapy, vol.24, issue.3, pp.430-476, 2016.

. Maiage, . Research-unit, and . Jouy-en-josas,

D. Marbach, J. C. Costello, R. Küffner, N. M. Vega, R. J. Prill et al., Wisdom of crowds for robust gene network inference, Nature Methods, vol.9, pp.796-804, 2012.

A. Marguet, M. Lavielle, and E. Cinquemani, Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data, Proceedings of the ISMB/ECCB conference 2019), 2019.

M. Mauri, J. L. Gouzé, H. Jong, and E. Cinquemani, Modelling and design of a synthetic microbial community for enhanced productivity in bioreactor, 2019.

, MAXIMIC -Optimal control of microbial cells by natural and synthetic strategies

, MEMIP -Mixed-Effects models of intracellular processes: Methods, tools and applications. ANR project

A. Milias-argeitis, M. Rullan, S. K. Aoki, P. Buchmann, and M. H. Khammash, Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth, Nature Communications, vol.7, p.12546, 2016.

A. Milias-argeitis, S. Summers, J. Stewart-ornstein, I. Zuleta, D. Pincus et al., In silico feedback for in vivo regulation of a gene expression circuit, Nature Biotechnology, vol.29, pp.1114-1116, 2011.

, MSC (laboratoire matière et systèmes complexes)

B. Munsky, B. Trinh, and M. Khammash, Listening to the noise: Random fluctuations reveal gene network parameters, Molecular Systems Biology, vol.5, issue.318, 2009.

N. Nakamura, H. C. Lin, C. S. Mcsweeney, R. I. Mackie, and H. R. Gaskins, Mechanisms of microbial hydrogen disposal in the human colon and implications for health and disease, Annual Review of Food and Science Technology, vol.1, pp.363-395, 2010.

G. Neuert, B. Munsky, R. Z. Tan, L. Teytelman, M. Khammash et al., Systematic identification of signal-activated stochastic gene regulation, Science, vol.339, issue.6119, pp.584-587, 2013.

O. Inria, Action de Développement Technologique (ADT), 2012.

, OPTICO -Optimal control software for microbial communities in a system of mini-bioreactors. Inria Action de Développement Technologique (ADT), 2019.

J. D. Orth, I. Thiele, and B. O. Palsson, What is flux balance analysis?, Nature Biotechnology, vol.28, issue.3, pp.245-248, 2010.

D. A. Oyarzún and M. Chaves, Design of a bistable switch to control cellular uptake, Journal of the Royal Society Interface, vol.12, issue.113, 2015.

G. J. Patti, O. Yanes, and G. Siuzdak, Metabolomics: the apogee of the omics trilogy, Nature Reviews Molecular Cell Biology, vol.13, issue.4, pp.263-272, 2012.

J. Paulsson, Models of stochastic gene expression, Physics of Life Reviews, vol.2, issue.2, pp.157-175, 2005.

J. M. Pedraza and A. Van-oudenaarden, Noise propagation in gene networks, Science, vol.307, 1965.

R. Porreca, E. Cinquemani, J. Lygeros, and G. Ferrari-trecate, Identification of genetic network dynamics with unate structure, Bioinformatics, vol.26, issue.9, pp.1239-1284, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00793025

R. Porreca, E. Cinquemani, J. Lygeros, and G. Ferrari-trecate, Invalidation of the structure of genetic network dynamics: A geometric approach, International Journal of Robust and Nonlinear Control (Special Issue on System Identification for Biological Systems), vol.22, issue.10, pp.1140-56, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00762592

L. Postiglione, S. Napolitano, E. Pedone, D. L. Rocca, F. Aulicino et al., Regulation of gene expression and signaling pathway activity in mammalian cells by automated microfluidics feedback control, ACS Synthetic Biology, vol.7, issue.11, pp.2558-2565, 2018.

A. Prékopa, Stochastic Programming, Mathematics and its Applications, vol.324, 1995.

J. Qin, A human gut microbial gene catalogue established by metagenomic sequencing, Nature, vol.464, pp.59-65, 2010.
URL : https://hal.archives-ouvertes.fr/cea-00908974

S. Raguideau, S. Plancade, N. Pons, M. Leclerc, and B. Laroche, Inferring aggregated functional traits from metagenomic data using constrained non-negative matrix factorization: Application to fiber degradation in the human gut microbiota, Plos computational Biology, vol.12, issue.12, pp.1-29, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01454674

A. Raj and A. Van-oudenaarden, Nature, nurture, or chance: Stochastic gene expression and its consequences, Cell, vol.135, pp.216-226, 2008.

M. A. Rapsomaniki, E. Cinquemani, N. N. Giakoumakis, P. Kotsantis, J. Lygeros et al., Inference of protein kinetics by stochastic modeling and simulation of fluorescence recovery after photobleaching experiments, Bioinformatics, vol.31, issue.3, pp.355-362, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01096966

J. M. Raser and E. K. O'shea, Control of stochasticity in eukaryotic gene expression, Science, vol.304, pp.1811-1814, 2004.

C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, 2006.

A. Raue, C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling et al., Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood, Bioinformatics, vol.25, issue.15, pp.1923-1952, 2009.

J. B. Rawlings and D. Q. Mayne, Model Predictive Control: Theory and Design, 2009.

, RIB-ECO -Engineering RNA life cycle to optimize economy of microbial energy: Application to the bioconversion of biomass-derived carbon sources, ANR project (ANR-18-CE43-0010), pp.2018-2022

M. Rullan, D. Benzinger, G. Schmidt, A. Milias-argeitis, and M. H. Khammash, An optogenetic platform for real-time, single-cell interrogation of stochastic transcriptional regulation, Molecular Cell, vol.70, issue.4, pp.745-756, 2018.

A. Samson, M. Lavielle, and F. Mentré, Extension of the SAEM algorithm to left-censored data in non-linear mixed-effects model: Application to hiv dynamics model, Computational Statistics and Data Analysis, vol.51, issue.3, pp.1562-74, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00263506

S. Santala, M. Karp, and V. Santala, Rationally engineered synthetic coculture for improved biomass and product formation, PLoS One, vol.9, issue.12, p.113786, 2014.

M. Schelker, A. Raue, J. Timmer, and C. Kreutz, Comprehensive estimation of input signals and dynamics in biochemical reaction networks, Bioinformatics, vol.28, issue.18, pp.529-563, 2012.

M. Scudellari, Software startups aim to automate bio labs. IEEE spectrum, 2017.

D. Stefan, C. Pinel, S. Pinhal, E. Cinquemani, J. Geiselmann et al., Inference of quantitative models of bacterial promoters from time-series reporter gene data, PLoS Comput. Biol, vol.11, issue.1, p.1004028, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01097632

A. Succurro, D. Segrè, and O. Ebenhöh, Emergent subpopulation behavior uncovered with a community dynamic metabolic model of Escherichia coli diauxic growth. mSystems, vol.4, 2019.

P. S. Swain, M. B. Elowitz, and E. D. Siggia, Intrinsic and extrinsic contributions to stochasticity in gene expression, vol.99, pp.12795-12800, 2002.

Y. Taniguchi, P. J. Choi, G. W. Li, H. Chen, M. Babu et al., Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells, Science, vol.329, issue.5991, pp.533-541, 2010.

O. Tenaillon, D. Skurnik, B. Picard, and E. Denamur, The population genetics of commensal Escherichia coli, Nature Reviews Microbiology, vol.8, issue.3, pp.207-224, 2010.

M. Thattai and A. Van-oudenaarden, Intrinsic noise in gene regulatory networks, vol.98, pp.8614-8619, 2001.

T. Inc and C. A. Park,

J. Uhlendorf, A. Miermont, T. Delaveau, G. Charvin, F. Fages et al., Long-term model predictive control of gene expression at the population and single-cell levels, vol.109, pp.14271-14276, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01528440

D. Vecchio, A. J. Dy, and Y. Qian, Control theory meets synthetic biology, Journal of The Royal Society Interface, vol.13, issue.120, p.20160380, 2016.

A. Villaverde and J. Banga, Reverse engineering and identification in systems biology: Strategies, perspectives and challenges, Journal of the Royal Society Interface, vol.11, 2014.

G. Wahba, Spline Models for Observational Data, 1990.

S. Waldherr, Estimation methods for heterogeneous cell population models in systems biology, Journal of The Royal Society Interface, vol.15, issue.147, p.20180530, 2018.

L. Weber, W. Raymond, and B. Munsky, Identification of gene regulation models from single-cell data, Physical Biology, vol.15, issue.5, p.55001, 2018.

E. Weill, V. Andreani, C. Aditya, P. Martinon, G. Batt et al., Optimal control of an artificial microbial differentiation system for protein bioproduction, Proceedings of the 17th European Control Conference, 2019.

S. Widder, Challenges in microbial ecology: Building predictive understanding of community function and dynamics, The ISME Journal, vol.10, pp.2557-2568, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01512087

A. S. Willsky, Multiresolution M]arkov models for signal and image processing, Proceedings of the IEEE, vol.90, issue.8, pp.1396-1458, 2002.

I. Yegorov, F. Mairet, H. Jong, and J. Gouzé, Optimal control of bacterial growth for the maximization of metabolite production, Journal of Mathematical Biology, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01929475

E. Yeung, A. J. Dy, K. B. Martin, A. H. Ng, D. Vecchio et al., Biophysical constraints arising from compositional context in synthetic gene networks, Cell Systems, vol.5, issue.1, pp.11-24, 2017.

H. Youk and W. A. Lim, Secreting and sensing the same molecule allows cells to achieve versatile social behaviors, Science, vol.343, p.1242782, 2014.

C. Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter et al., Moment-based inference predicts bimodality in transient gene expression, vol.109, pp.8340-8345, 2012.

C. Zechner, M. Unger, S. Pelet, M. Peter, and H. Koeppl, Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings, Nature Methods, vol.11, pp.197-202, 2014.

V. Zulkower, M. Page, D. Ropers, J. Geiselmann, and H. De-jong, Robust reconstruction of gene expression profiles from reporter gene data using linear inversion, Bioinformatics, vol.31, issue.12, pp.71-80, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01217800