29. P.T. Monteiro, **D. Ropers**, R. Mateescu, A.T. Freitas, H. de Jong (2008), Temporal logic patterns for querying dynamic models of cellular interaction networks, *Bioinformatics*, 24(16): i227-i233. Special issue ECCB-2008.

30. H. de Jong, **D. Ropers** (2006), Strategies for dealing with incomplete information in the modeling of molecular interaction networks, *Briefings in Bioinformatics*, 7(4):354-363.

31. H. Hallay, N. Locker, L. Ayadi, **D. Ropers**, E. Guittet, C. Branlant (2006), Biochemical and NMR study on the competition between proteins SC35, SRp40, and heterogeneous nuclear ribonucleoprotein A1 at the HIV-1 Tat exon 2 splicing site, *Journal of Biological Chemistry*, 281(48):37159-74.

32. **D. Ropers**, H. de Jong, M. Page, D. Schneider, J. Geiselmann (2006), Qualitative simulation of the carbon starvation response in *Escherichia coli. BioSystems*, 84(2):124-152.

33. G. Batt, **D. Ropers**, H. de Jong, J. Geiselmann, R. Mateescu, M. Page and D. Schneider (2005), Validation of qualitative models of genetic regulatory networks by model checking: Analysis of the nutritional stress response in *Escherichia coli. Bioinformatics*, 21(Suppl 1):i19-i28, special issue ISMB-2005.

34. D. Eveillard, **D. Ropers**, H. de Jong, C. Branlant, A. Bockmayr (2004), A multi-scale constraint programming model of alternative splicing regulation, *Theoretical Computer Science*, 135(1): 3-24.

35. **D. Ropers**, L. Ayadi, R. Gattoni, S. Jacquenet, L. Damier, C. Branlant, J. Stévenin (2004), Differential effects of the SR proteins 9G8, SC35, ASF/SF2 and SRp40 on the utilization of the A1 to A5 splicing sites of HIV-1 RNA, *Journal of Biological Chemistry*, 279(29): 29963-29973.

36. S. Jacquenet, **D. Ropers**, P. Bilodeau, L. Damier, A. Mougin, M. Stoltzfus, C. Branlant (2001), Conserved stem-loop structures in the HIV-1 RNA region containing the A3 3' splice site and its cis-regulatory element: possible involvement in RNA splicing, *Nucleic Acids Research*, 29(2): 464-478.

Conference papers

1. S. Casagranda, **D. Ropers**, J.-L. Gouzé (2015), Model reduction and process analysis of biological models. *Proceedings of 23rd Mediterranean Conference on Control and Automation* (MED 2015)

2. I. Belgacem, E. Grac, **D. Ropers**, J.-L. Gouzé (2014), *Proceedings of 21st International Symposium on Mathematical Theory of Networks and Systems*, Jul 2014, pp 1383-1386, Groningen, Netherlands.

3. **D. Ropers**, V. Baldazzi, H. de Jong (2009), Reduction of a kinetic model of the carbon starvation response in *Escherichia coli*, in *Proceedings of the 15th IFAC Symposium on System Identification*, SYSID 2009.

4. P.T. Monteiro, **D. Ropers**, R. Mateescu, A.T. Freitas, H. de Jong (2008). Temporal logic patterns for querying qualitative models of genetic regulatory networks. In: M. Ghallab, C.D. Spyropoulos, N. Fakotakis, N. Avouris (eds.), *Proceedings of 18th European Conference on Artificial Intelligence (ECAI 2008)*, IOS Press, Amsterdam, 229-233

5. **D. Ropers**, H. de Jong, J.-L. Gouzé, M. Page, D. Schneider, J. Geiselmann (2005), Piecewise-linear models of genetic regulatory networks : Analysis of the carbon starvation response in *Escherichia coli. Proceedings of ECMTB 2005, Mathematical Modeling of Biological Systems*, Volume I. A. Deutsch, L. Brusch, H. Byrne, G. de Vries and H.-P. Herzel (eds), Birkhäuser, Boston, 83-96.

6. G. Batt, H. de Jong, J. Geiselmann, M. Page, **D. Ropers**, D. Schneider (2005). Qualitative analysis and verification of hybrid models of genetic regulatory networks: Nutritional stress response in *Escherichia coli*. M. Morari, L. Thiele (eds), *Hybrid Systems: Computation and Control (HSCC 2005), Lecture Notes in Computer Science*, 3414, Springer-Verlag, Berlin, 134-150
7. G. Batt, H. de Jong, J. Geiselmann, M. Page, **D. Ropers**, D. Schneider (2005). Qualitative analysis and verification of hybrid models of genetic regulatory networks: Nutritional stress response in Escherichia coli. M. Morari, L. Thiele (eds), *Hybrid Systems: Computation and Control (HSCC 2005)*, *Lecture Notes in Computer Science* 3414, Springer-Verlag, Berlin, 134-150

8. D. Eveillard, **D. Ropers**, H. de Jong, C. Branlant, A. Bockmayr (2003), Multiscale modeling of alternative splicing regulation, C. Priami (ed.), *Computational Methods in Systems Biology (CMSB03)*, Lecture Notes in Computer Science 2602, Springer-Verlag, Berlin, 75-87.

## Book and book chapters (in English and in French)

1. S. Bourgoin-Voillard, C. Durmort, J. Geiselmann, A. Le Gouëllec, **D. Ropers**, M. Sève (2015). *La cellule procaryote en biotechnologies in Les biotechnologies en santé – Tome 1 : Introduction aux biotechnologies en santé.* Coordonnateurs : S. Bourgoin-Voillard, W. Rachidi, M. Sève. Ed. Lavoisier.

2. V. Baldazzi, P.T. Monteiro, M. Page, **D. Ropers**, J. Geiselmann, H. de Jong (2011), Qualitative analysis of genetic regulatory networks in bacteria, W. Dubitzky, J. Southgate, H. Fuss (eds.), *Understanding the Dynamics of Biological Systems: Lessons Learned from Integrative Systems Biology*, Springer-Verlag, Berlin, pp 111-130.

3. **D. Ropers** (2011), *De la complexité génomique à la diversité protéique - Analyse par modélisation et expériences de la régulation de l'épissage alternatif de l'ARN du virus VIH-1*, Editions Universitaires Européennes, Saarbrücken (Germany), ISBN 978-613-1-56368-3.

4. G. Batt, R. Casey, H. de Jong, J. Geiselmann, J.-L. Gouzé, M. Page, **D. Ropers**, T. Sari, D. Schneider (2006). Qualitative analysis of the dynamics of genetic regulatory networks using piecewise-linear models. In: E. Pecou, S. Martinez, A. Maass (eds.), *Mathematical and Computational Methods in Biology*, Editions Hermann, Paris, 206-239

5. H. de Jong, **D. Ropers** (2006), Qualitative approaches towards the analysis of genetic regulatory networks, Z. Szallasi, V. Periwal, J. Stelling (eds), *System Modeling in Cellular Biology: From Concepts to Nuts and Bolts*, MIT Press, Cambridge, MA, 125-148.

## Proceedings and journal papers in French, science popularization articles

1. D. Ropers, with T. Vieville (2019), Mais comment éduquer les garçons à l'équité des genres au niveau informatique et numérique ? Colloque "Un rêve pour les filles et les garçons : la SCIENCE", Colloque des associations Parité Science, Femmes & Sciences, de l'Union des professeurs de physique et chimie de l'académie de Grenoble et de la section Alpes de la Société Française de Physique, Grenoble, 9 novembre 2019, p55-70.

2. G. Batt, H. de Jong, J. Geiselmann, J.-L. Gouzé, M. Page, **D. Ropers**, T. Sari, D. Schneider (2007), Analyse qualitative de la dynamique de réseaux de régulation génique par des modèles linéaires par morceaux, *Technique et Science Informatique*, 26(1-2):11-45.

3. **D. Ropers**, H. de Jong, J. Geiselmann. Modélisation de la réponse au stress nutritionnel de la bactérie *Escherichia coli*, *Biofutur*, 275:36-39, 2007

4. H. de Jong, **D. Ropers**, C. Chaouiya, D. Thieffry. Modélisation, analyse et simulation de réseaux de régulation génique. *Biofutur*, 252:36-40, 2005

## Patent

Johannes Geiselmann, Hidde de Jong, Delphine Ropers, Jérôme Izard, Method for producing metabolites, peptides and recombinant proteins, EP3047031A1, US9816123B2, WO2015036622A1
https://patents.google.com/patent/US9816123B2/en
## Participation in projects

<table><tr><td>2018 - 2022</td><td>ANR project (from the French National Research Agency) RIBECO "Engineering RNA life cycle to optimize economy of microbial energy: application to the bioconversion of biomass-derived carbon sources". Principal Investigator (PI), work-package leader.</td></tr><tr><td>2018 - 2022</td><td>ANR project Maximic "Optimal control of microbial cells by natural and synthetic strategies". Participant.</td></tr><tr><td>2018 - 2020</td><td>Funding from the Institut of Complex Systems IXXI and the Federation of Systems Biology BioSyl "Multi-Omics and Metabolic models integration to study growth transition in <i>Escherichia coli</i>". Coordinator.</td></tr><tr><td>2016 - 2020</td><td>PhD grant INRA-Inria and Inria/ANR funding. Main PhD advisor (co-adviser with Muriel Cocaign-Bousquet).</td></tr><tr><td>2016 - 2020</td><td>ANR project MEMIP "Mixed-Effects Models of Intracellular Processes - Methods, Tools and Applications". Participant.</td></tr><tr><td>2012 - 2017</td><td>Projet Investissement d'Avenir RESET (Investments for the Future programme of ANR) "Arrest and restart of the gene expression machinery » (2012-2017 ; https://project.inria.fr/reset/fr/). Co-PI, work-package leader.</td></tr><tr><td>2012 - 2017</td><td>Contrat Jeune Scientifique INRA-Inria (PhD grant and two year-postdoctoral research fellowship for Manon Morin; 2012 – Octobre 2017). Co-advisor with Muriel Cocaign-Bousquet.</td></tr><tr><td>2010 - 2013</td><td>ANR project GemCo "Model reduction, experimental validation, and control for the gene expression machinery in <i>E. coli</i>". WP leader.</td></tr><tr><td>2008 - 2013</td><td>Action d'Envergure Inria ColAge "Natural and engineering solutions to the control of bacterial growth and aging: A systems and synthetic biology approach". Participant.</td></tr><tr><td>2006 - 2009</td><td>FP6 european project EC-MOAN "Scalable Modeling and Analysis Techniques to Study Emergent Cell Behavior - understanding the <i>E. coli</i> stress response". Participant.</td></tr><tr><td>2006 - 2009</td><td>ANR project MetaGenoReg "Towards an understanding of the interrelations between metabolic and gene regulation: <i>E. coli</i> carbon metabolism as a test case". Participant.</td></tr></table>

## Teaching

<table><tr><td>2020 -</td><td>Teaching on data integration in course "Artificial Intelligence for Omics", Master 1 AI for Health, Univ Grenoble-Alpes, 16h/year.</td></tr><tr><td>2013 -</td><td>Course "Modelling in Systems Biology", 2nd year student at Phelma school - Grenoble INP/Master 1 Nanobiotechnology - Univ Grenoble-Alpes, 16h/year. Responsible of course.</td></tr><tr><td>2012 - 2018</td><td>Lecture on mathematical modelling of biological systems, 5th year biotechnology students at INSA Toulouse, 4h/year.</td></tr><tr><td>2007 -</td><td>Teaching on cell systems modelling in course "Molecular tools for health", Master 1 Health engineering, Univ Grenoble-Alpes, 14h/year.</td></tr><tr><td>2006 - 2008</td><td>Course Modelling of Gene Regulatory Networks, PhD Program in Computational Biology, Instituto Gulbenkian de Ciencia, Lisbon (Portugal), 1 week/year</td></tr><tr><td>2004 - 2008</td><td>Teaching in Bioinformatics course, Master 1 Computer Science, Univ Joseph Fourier (Grenoble), 13h/year.</td></tr><tr><td>2004 - 2007</td><td>Teaching in course "Modelling of metabolism", Master 2 aMIV, Univ Claude Bernard (Lyon), 5h/year.</td></tr></table>
Supervision

PhD students

<table><tr><td>Thibault Etienne</td><td>Doctoral school Evolution Ecosystems Microbiology Modelling (E2M2), Univ Lyon. Co-advisor with Muriel Cocaïgn-Bousquet (principal advisor). Defence: November 2020.</td></tr><tr><td>Stefano Casagranda</td><td>Doctoral school Information and Communication sciences and technologies (STIC), Univ Nice. Co-advisor with Jean-Luc Gouzé. Defence: June 2017.</td></tr><tr><td>Manon Morin</td><td>Doctoral school Eco-Agro-Bio-Sciences (SEVAB), Univ Paul Sabatier, Toulouse. Co-advisor with Muriel Cocaïgn-Bousquet. Defence: November 2015.</td></tr><tr><td>Stéphane Pinhal</td><td>Doctoral school of chemistry and life sciences (CSV), Univ Joseph Fourier, Grenoble. Co-advisor with Johannes Geiselmann and Hidde de Jong. Defence: March 2015.</td></tr><tr><td>Valentin Zulkower</td><td>Doctoral school of Mathematics, Information Sciences and Technologies, and Computer Science (MSTII), Univ Joseph Fourier, Grenoble. Co-advisor with Johannes Geiselmann and Hidde de Jong. Defence: March 2015.</td></tr><tr><td>Jérôme Izard</td><td>Doctoral school of chemistry and life sciences (CSV), Univ Joseph Fourier, Grenoble. Co-advisor with Johannes Geiselmann and Stéfan Lacour. Defence: December 2012.</td></tr></table>

BSc and MSc students

<table><tr><td>Amélie Caddéo</td><td>M1, Univ Grenoble - Alpes. Defence: June 2021.</td></tr><tr><td>Olivier Feudjio</td><td>M1, Univ de Paris. Co-supervision with T. Etienne. Defence: June 2021.</td></tr><tr><td>Tommy Burnoud</td><td>M1, Univ Grenoble - Alpes. Defence: June 2020.</td></tr><tr><td>Arieta Shabani</td><td>M1, Univ Grenoble - Alpes. Co-supervision with H. de Jong. Defence: June 2019.</td></tr><tr><td>Eric Cumunel</td><td>M2, Univ Lyon 1. Co-supervision with M.-F. Sagot. Defence: July 2019.</td></tr><tr><td>Naina Goel</td><td>M1, Univ. Paris-Dauphine. Co-supervision with H. de Jong. Defence: June 2018.</td></tr><tr><td>Alex Uchenna Anyaeg-bunam</td><td>M2, Univ Paris-Dauphine. Co-supervision with E. Cinquemani. Defence: June 2016.</td></tr><tr><td>Keerthi Kurma</td><td>M1, Phelma - Grenoble INP. Co-supervision with E. Cinquemani. Defence: June 2015.</td></tr><tr><td>Julien Sauvage</td><td>2nd year, Phelma - Grenoble INP. Defence: October 2014.</td></tr><tr><td>iGEM Grenoble-EMSE-LSU team</td><td>MSc students, Univ Joseph Fourier, Grenoble INP, ENSM Saint-Etienne & Louisiana State University. iGEM 2013 competition.</td></tr><tr><td>Nils Giordano</td><td>M2, ENS Paris & Univ Pierre et Marie Curie. Defence: June 2012.</td></tr><tr><td>iGEM Grenoble team</td><td>MSc students, Univ Joseph Fourier, Grenoble INP & Polytech Grenoble. iGEM 2012 competition.</td></tr><tr><td>Stéphane Pinhal</td><td>M2, Univ Pierre et Marie Curie. Defence: June 2011.</td></tr><tr><td>iGEM Grenoble team</td><td>MSc students, Univ Joseph Fourier & Grenoble INP. iGEM 2011 competition.</td></tr><tr><td>Dishank Gupta</td><td>BSc, Institute of Technology, Banaras Hindu University, India. Defence: August 2010.</td></tr><tr><td>Ying Song</td><td>M1, Univ Joseph Fourier, Grenoble. Defence: July 2010.</td></tr><tr><td>Vaibhhav Sinha</td><td>MSc, IIT Kharagpur, India. Defence: August 2009.</td></tr><tr><td>Mohammed El Amine Youcef</td><td>M1, Univ Joseph Fourier Grenoble. Defence: July 2009.</td></tr><tr><td>Yan Cao</td><td>MSc, Zhejiang University, China. Defence: March 2009.</td></tr></table>
Scientific and Administrative Duties

<table><tr><td>2019 -</td><td>Co-coordinator of mentoring program</td></tr><tr><td>2014 -</td><td>"Référent-chercheur" at Inria Grenoble - Rhône-Alpes</td></tr><tr><td>2009 -</td><td>First-aid rescue worker</td></tr><tr><td>2017 - 2018</td><td>Member of Inria strategic plan working group</td></tr><tr><td>2015 - 2019</td><td>Nominated member of the Inria Evaluation Committee</td></tr><tr><td>2014 - 2018</td><td>Member of the Comité d'Etudes Doctorales at Inria Grenoble - Rhône-Alpes</td></tr><tr><td>2010 - 2015</td><td>Member of the Commission de Formation Permanente at Inria Grenoble - Rhône-Alpes</td></tr><tr><td>2007 - 2015</td><td>Representant of Inria in the scientific board of the Complex Systems Institute of Lyon (IXXI)</td></tr><tr><td>2007 - 2019</td><td>Member of the steering committee of the Rhône-Alpes Seminar on Modeling in the Life Sciences SEMOVI</td></tr></table>

Committees

<table><tr><td colspan="2">PhD progress committee</td></tr><tr><td>Charlotte Roux</td><td>Univ Paul Sabatier, Toulouse, 2018-2021</td></tr><tr><td>Manon Barthe</td><td>Univ Paul Sabatier, Toulouse, 2017-2021</td></tr><tr><td>Irene Ziska</td><td>Univ Claude Bernard, Lyon, 2017-2020</td></tr><tr><td>Martin Wannagat</td><td>Univ Claude Bernard, Lyon, 2012-2015</td></tr><tr><td>Alice Julien-Laferrière</td><td>Univ Claude Bernard, Lyon, 2012-2015</td></tr><tr><td>Claire Villiers</td><td>Univ Joseph Fourier, 2010-2013</td></tr><tr><td>Sirichai Sunya</td><td>Univ Paul Sabatier, 2007-2010</td></tr></table>

<table><tr><td colspan="2">PhD committee</td></tr><tr><td>Thibault Etienne</td><td>PhD co-advisor. Univ Claude Bernard, Lyon. December 2020</td></tr><tr><td>Joël Espel</td><td>Invited member. Univ Grenoble-Alpes. October 2020.</td></tr><tr><td>Ronan Duchesne</td><td>Examiner. ENS Lyon. December 2019.</td></tr><tr><td>Marianyela Petrizzelli</td><td>Examiner. Univ Paris-Sud. July 2019.</td></tr><tr><td>Stefano Casagranda</td><td>PhD co-advisor. Univ Nice. June 2017.</td></tr><tr><td>Manon Morin</td><td>PhD co-advisor. Univ Paul Sabatier, Toulouse. November 2015</td></tr><tr><td>Ismail Belgacem</td><td>Examiner. Univ Joseph Fourier, Grenoble. March 2015</td></tr><tr><td>Stéphane Pinhal</td><td>PhD co-advisor. Univ Joseph Fourier, Grenoble. March 2015</td></tr><tr><td>Valentin Zulkower</td><td>PhD co-advisor. Univ Joseph Fourier, Grenoble. March 2015</td></tr><tr><td>Anna Zukhova</td><td>Examiner. Univ Bordeaux. December 2014</td></tr><tr><td>Claire Villiers</td><td>Examiner. Univ Joseph Fourier, Grenoble. October 2013</td></tr><tr><td>Jérôme Izard</td><td>PhD co-advisor. Univ Joseph Fourier, Grenoble. December 2012</td></tr></table>

<table><tr><td colspan="2">Selection committee</td></tr><tr><td>2021</td><td>Inria Starting and Advanced Research positions</td></tr><tr><td>2020</td><td>CR Inria admission panel</td></tr><tr><td></td><td>CR Inria Grenoble - Rhône-Alpes</td></tr><tr><td>2019</td><td>INRA-Inria PhD grants</td></tr><tr><td></td><td>CR Inria Bordeaux Sud-Ouest</td></tr></table>
<table><tr><td>2018</td><td>CR Inria (national)<br/>Assistant Professor, Univ Rennes<br/>Assistant Professor, INSA Lyon<br/>CR Inria Grenoble - Rhône-Alpes<br/>CR Inria (national)<br/>INRA-Inria PhD grant</td></tr><tr><td>2017</td><td>CR2 Inria Sophia-Antipolis - Méditerranée<br/>CR1 Inria<br/>Assistant Professor, INSA Lyon<br/>PhD grant INSERM-Inria</td></tr><tr><td>2016</td><td>CR Inria admission panel<br/>CR Inria Lille - Nord-Europe</td></tr><tr><td>2015</td><td>Assistant Professor, INSA Lyon<br/>CR Inria admission panel<br/>Assistant Professor, INSA Lyon<br/>Assistant Professor, INSA Lyon</td></tr><tr><td>2008</td><td>IR INRA</td></tr></table>

## Reviewing activities and program committees

<table><tr><td>Funding agencies</td><td>ANR, BMBF (Germany), DAAD (Germany), BBSRC (UK), NWO (The Netherlands), NSC (Poland), Univ Grenoble-Alpes, PEPS</td></tr><tr><td>Journal articles</td><td>Nature Communications, Cell Systems, Bioinformatics, Biosystems, Current Biotechnology, BMC Systems Biology, PLoS One, Journal of Theoretical Biology, Biophysical Journal...</td></tr><tr><td>Books and book chapters</td><td>CRC Press, Wiley, Springer</td></tr><tr><td>Program committees</td><td>JOBIM (2007, 2008, 2010, 2011, 2017, 2021), CSBio (2019, 2020), ECCB 2020</td></tr></table>

## Scientific animation

<table><tr><td>2018 –</td><td>Participation to the creation of the community "Microbial bioinformatics" for the European infrastructure ELIXIR</td></tr><tr><td>2008 – 2014</td><td>Creation and co-organization of the Grenoble seminar on Complex Systems</td></tr><tr><td>2011</td><td>Co-organization with Eugenio Cinquemani of workshop on Identification and Control of Biological Interaction Networks</td></tr><tr><td>2007 – 2017, 2019</td><td>Steering committee member of Modelling in Life Sciences seminar in Rhône-Alpes</td></tr></table>
<figure><img src="image_42.png" /></figure>
Selected articles

The articles that I highlighted in the manuscript are listed below, together with their abstract.

[Ropers et al., 2011] Model reduction using piecewise-linear approximations preserves dynamic properties of the carbon starvation response in *Escherichia coli*. Delphine Ropers, Valentina Baldazzi, Hidde de Jong. *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, 8(1), 166-181, 2011.

The adaptation of the bacterium *Escherichia coli* to carbon starvation is controlled by a large network of biochemical reactions involving genes, mRNAs, proteins, and signalling molecules. The dynamics of these networks is difficult to analyze, notably due to a lack of quantitative information on parameter values. To overcome these limitations, model reduction approaches based on quasi-steady-state (QSS) and piecewise-linear (PL) approximations have been proposed, resulting in models that are easier to handle mathematically and computationally. These approximations are not supposed to affect the capability of the model to account for essential dynamical properties of the system, but the validity of this assumption has not been systematically tested. In this paper we carry out such a study by evaluating a large and complex PL model of the carbon starvation response in *E. coli* using an ensemble approach. The results show that, in comparison with conventional nonlinear models, the PL approximations generally preserve the dynamics of the carbon starvation response network, although with some deviations concerning notably the quantitative precision of the model predictions. This encourages the application of PL models to the qualitative analysis of bacterial regulatory networks, in situations where the reference time-scale is that of protein synthesis and degradation.

[Ropers et al., 2006] *Qualitative simulation of the carbon starvation response in Escherichia coli*. Delphine Ropers, Hidde de Jong, Michel Page, Dominique Schneider and Johannes Geiselmann. *Biosystems* 84 (2), 124-152, 2006.

In case of nutritional stress, like carbon starvation, *Escherichia coli* cells abandon their exponential-growth state to enter a more resistant, non-growth state called stationary phase. This growth-phase transition is controlled by a genetic regulatory network integrating various environmental signals. Although *E. coli* is a paradigm of the bacterial world, it is little understood how its response to carbon starvation conditions emerges from the interactions between the different components of the regulatory network. Using a qualitative method that is able to overcome the current lack of quantitative data on kinetic parameters and molecular concentrations, we model the carbon starvation response network and simulate the response of *E. coli* cells to carbon deprivation. This allows us to identify essential features of the transition between exponential and stationary phase and to make new predictions on the qualitative system behaviour following a carbon upshift.

[Cinquemani et al., 2017] *Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data*. Eugenio Cinquemani, Valérie Laroute, Muriel Cocaign-Bousquet, Hidde de Jong, Delphine Ropers, *Bioinformatics* (Proceedings of the 25th ISMB/16th ECCB), 33(14):i301–i310.

*Motivation*. Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuities in the profiles due to sudden changes in metabolic regime. *Results*. We present a method for precisely estimating time-varying uptake and excretion rates from time-series measurements of extracellular
metabolite concentrations, specifically addressing all of the above issues. The estimation problem is formulated in a regularized Bayesian framework and solved by a combination of extended Kalman filtering and smoothing. The method is shown to improve upon methods based on spline smoothing of the data. Moreover, when applied to two actual datasets, the method recovers known features of overflow metabolism in *Escherichia coli* and *Lactococcus lactis*, and provides evidence for acetate uptake by *L. lactis* after glucose exhaustion. The results raise interesting perspectives for further work on rate estimation from measurements of intracellular metabolites.

[Zulkower et al., 2015] **Robust reconstruction of gene expression profiles from reporter gene data using linear inversion.** Valentin Zulkower, Michel Page, Delphine Ropers, Johannes Geiselmann & Hidde de Jong. *Bioinformatics*, 31(12), i71-i79, 2015.

*Motivation:* Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest.

*Results:* We propose a general approach based on regularized linear inversion to solve a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles. We evaluate the validity of the approach using in silico simulation studies, and observe that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data. We apply the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with *Escherichia coli*. The methods are capable of reliably reconstructing time-course profiles of growth rate, promoter activity and protein concentration from weak and noisy signals at low population volumes. Moreover, they capture critical features of those profiles, notably rapid changes in gene expression during growth transitions.

[Berthoumieux et al., 2013] **Shared control of gene expression in bacteria by transcription factors and global physiology of the cell.** Sara Berthoumieux, Hidde de Jong, Guillaume Baptist, Corinne Pinel, Caroline Ranquet, Delphine Ropers, Johannes Geiselmann. *Molecular Systems Biology*, 9:634, 2013.

Gene expression is controlled by the joint effect of (i) the global physiological state of the cell, in particular the activity of the gene expression machinery, and (ii) DNA-binding transcription factors and other specific regulators. We present a model-based approach to distinguish between these two effects using time-resolved measurements of promoter activities. We demonstrate the strength of the approach by analyzing a circuit involved in the regulation of carbon metabolism in *E. coli*. Our results show that the transcriptional response of the network is controlled by the physiological state of the cell and the signaling metabolite cyclic AMP (cAMP). The absence of a strong regulatory effect of transcription factors suggests that they are not the main coordinators of gene expression changes during growth transitions, but rather that they complement the effect of global physiological control mechanisms. This change of perspective has important consequences for the interpretation of transcriptome data and the design of biological networks in biotechnology and synthetic biology.

[Izard et al., 2015] **A synthetic growth switch based on controlled expression of RNA polymerase.** Jérôme Izard, Cindy Gomez Balderas, Delphine Ropers, Stephan Lacour, Xiaohu Song, Yifan Yang, Ariel B. Lindner, Johannes Geiselmann, Hidde de Jong, *Molecular Systems Biology*, 11(11):840, 2015.

The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an *Escherichia coli* strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase
concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium-independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild-type *E. coli* strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of *E. coli* metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology.

[Morin et al., 2016] The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of *Escherichia coli*. Manon Morin, Delphine Ropers, Fabien Letisse, Sandrine Laguerre, Jean-Charles Portais, Muriel Cocaign-Bousquet, Brice Enjalbert. *Molecular Microbiology*, 100(4), 686-700, 2016.

Metabolic control in *Escherichia coli* is a complex process involving multilevel regulatory systems but the involvement of post-transcriptional regulation is uncertain. The post-transcriptional factor CsrA is stated as being the only regulator essential for the use of glycolytic substrates. A dozen enzymes in the central carbon metabolism (CCM) have been reported as potentially controlled by CsrA, but its impact on the CCM functioning has not been demonstrated. Here, a multiscale analysis was performed in a wild-type strain and its isogenic mutant attenuated for CsrA (including growth parameters, gene expression levels, metabolite pools, abundance of enzymes and fluxes). Data integration and regulation analysis showed a coordinated control of the expression of glycolytic enzymes. This also revealed the imbalance of metabolite pools in the csrA mutant upper glycolysis, before the phosphofructokinase PfkA step. This imbalance is associated with a glucose-phosphate stress. Restoring PfkA activity in the csrA mutant strain suppressed this stress and increased the mutant growth rate on glucose. Thus, the carbon storage regulator system is essential for the effective functioning of the upper glycolysis mainly through its control of PfkA. This work demonstrates the pivotal role of post-transcriptional regulation to shape the carbon metabolism.

[Morin et al., 2017] The Csr system regulates *Escherichia coli* fitness by controlling glycogen accumulation and energy levels. Manon Morin, Delphine Ropers, Eugenio Cinquemani, Jean-Charles Portais, Brice Enjalbert and Muriel Cocaign-Bousquet. *mBio*, 8(5), 2017.

In the bacterium *Escherichia coli*, the post-transcriptional regulatory system Csr was postulated to influence the transition from glycolysis to gluconeogenesis. Here, we explored the role of the Csr system in the glucose-acetate transition as a model of the glycolysis-to-gluconeogenesis switch. Mutations in the Csr system influence the reorganization of gene expression after glucose exhaustion and disturb the timing of acetate consumption after glucose exhaustion. Analysis of metabolite concentrations during the transition revealed that the Csr system has a major effect on the energy levels of the cells after glucose exhaustion. This influence was demonstrated to result directly from the effect of the Csr system on glycogen accumulation. Mutation in glycogen metabolism was also demonstrated to hinder metabolic adaptation after glucose exhaustion because of insufficient energy. This work explains how the Csr system influences *E. coli* fitness during the glycolysis-gluconeogenesis switch and demonstrates the role of glycogen in maintenance of the energy charge during metabolic adaptation.

[Etienne et al., 2020] Competitive effects in bacterial mRNA decay. Thibault A. Etienne, Muriel Cocaign-Bousquet, Delphine Ropers. *Journal of Theoretical Biology*, 504: 110333, 2020.

In living organisms, the same enzyme catalyses the degradation of thousands of different mRNAs, but the possible influence of competing substrates has been largely ignored so far. We develop a simple
mechanistic model of the coupled degradation of all cell mRNAs using the total quasi-steady-state approximation of the Michaelis-Menten framework. Numerical simulations of the model using carefully chosen parameters and analyses of rate sensitivity coefficients show how substrate competition alters mRNA decay. The model predictions reproduce and explain a number of experimental observations on mRNA decay following transcription arrest, such as delays before the onset of degradation, the occurrence of variable degradation profiles with increased non linearities and the negative correlation between mRNA half-life and concentration. The competition acts at different levels, through the initial concentration of cell mRNAs and by modifying the enzyme affinity for its targets. The consequence is a global slow down of mRNA decay due to enzyme titration and the amplification of its apparent affinity. Competition happens to stabilize weakly affine mRNAs and to destabilize the most affine ones. We believe that this mechanistic model is an interesting alternative to the exponential models commonly used for the determination of mRNA half-lives. It allows analysing regulatory mechanisms of mRNA degradation and its predictions are directly comparable to experimental data.

[Etienne et al., In preparation] A mechanistic model informed by dynamic omics data reveals the physiological control of bacterial mRNA decay. Thibault A. Etienne, Eugenio Cinquemani, Laurence Girbal, Muriel Cocaign-Bousquet, Delphine Ropers. In preparation.
Bibliography

W. Abou-Jaoudé, P. Traynard, P. T. Monteiro, J. Saez-Rodriguez, T. Helikar, D. Thieffry, and C. Chaouiya. Logical modeling and dynamical analysis of cellular networks. *Frontiers Genet*, 7: 94, 2016.

R. Agren, S. Bordel, A. Mardinoglu, N. Pornputtapong, I. Nookaew, and J. Nielsen. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using init. *PLoS Comput. Biol.*, 8(5):e1002518, 2012.

M. Åkesson, J. Förster, and J. Nielsen. Integration of gene expression data into genome-scale metabolic models. *Metab. Eng.*, 6(4):285-293, 2004.

K. R. Albe, M. H. Butler, and B. E. Wright. Cellular concentrations of enzymes and their substrates. *J. Theor. Biol.*, 143(2):163-195, 1990.

T. Ali Azam, A. Iwata, A. Nishimura, S. Ueda, and A. Ishihama. Growth phase-dependent variation in protein composition of the *Escherichia coli* nucleoid. *J. Bacteriol.*, 181(20):6361-70, 1999.

A. P. Arkin. A wise consistency: engineering biology for conformity, reliability, predictability. *Curr. Opin. Chem. Biol.*, 17(6):893-901, 2013.

C. Auffray and L. Nottale. Scale relativity theory and integrative systems biology: 1: founding principles and scale laws. *Prog Biophys Mol Biol*, 97(1):79-114, 2008.

V. Baldazzi, D. Ropers, Y. Markowicz, D. Kahn, J. Geiselmann, and H. de Jong. The carbon assimilation network in *Escherichia coli* is densely connected and largely sign-determined by directions of metabolic fluxes. *PLoS Comput. Biol.*, 6:e1000812, 2010.

V. Baldazzi, D. Ropers, J. Geiselmann, D. Kahn, and H. de Jong. Importance of metabolic coupling for the dynamics of gene expression following a diauxic shift in *Escherichia coli*. *J. Theor. Biol.*, 295:100-115, 2012.

G. Batt, D. Ropers, H. de Jong, J. Geiselmann, R. Mateescu, M. Page, and D. Schneider. Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in *Escherichia coli*. *Bioinformatics*, 21(suppl 1):i19-28, 2005.

G. Batt, B. Besson, P.-E. Ciron, H. de Jong, E. Dumas, J. Geiselmann, R. Monte, P. T. Monteiro, M. Page, F. Rechenmann, and D. Ropers. Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks. In *Bacterial Molecular Networks*, pages 439-462. Springer, 2012.

D. A. Beard, E. Babson, E. Curtis, and H. Qian. Thermodynamic constraints for biochemical networks. *J. Theor. Biol.*, 228(3):327-333, 2004.

S. A. Becker, A. M. Feist, M. L. Mo, G. Hannon, B. Ø. Palsson, and M. J. Herrgard. Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox. *Nat. Protoc.*, 2(3):727-738, 2007.

I. Belgacem, S. Casagranda, E. Grac, D. Ropers, and J.-L. Gouzé. Reduction and stability analysis of a transcription-translation model of rna polymerase. *Bull. Math. Biol.*, 80(2):294-318, 2018.

C. Bernard. *Introduction à l'étude de la médecine expérimentale*. JB Baillière et fils, 1865.

M. Bersanelli, E. Mosca, D. Remondini, E. Giampieri, C. Sala, G. Castellani, and L. Milanesi. Methods for the integration of multi-omics data: mathematical aspects. *BMC Bioinform.*, 17(S2): S15, 2016.

S. Berthoumieux, H. de Jong, G. Baptist, C. Pinel, C. Ranquet, D. Ropers, and J. Geiselmann. Shared control of gene expression in bacteria by transcription factors and global physiology of the cell. *Mol. Syst. Biol.*, 9:634, 2013.

H. P. Bonarius, G. Schmid, and J. Tramper. Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. *Trends Biotechnol*, 15(8):308-314, 1997.

A. Bordbar, J. Monk, Z. King, and B. Palsson. Constraint-based models predict metabolic and associated cellular functions. *Nature Rev Genet*, 15(2):107-120, 2014.
J. A. M. Borghans, R. J. de Boer, and L. A. Segel. Extending the quasi-steady state approximation by changing variables. *Bull. Math. Biol.*, 58(1): 43–63, Jan 1996. ISSN 1522-9602.

F. Boyer, B. Besson, G. Baptist, J. Izard, C. Pinel, D. Ropers, J. Geiselmann, and H. de Jong. Well-Reader: a MATLAB program for the analysis of fluorescence and luminescence reporter gene data. *Bioinformatics*, 26:1262–1263, 2010.

I. Brigandt. Beyond reduction and pluralism: Toward an epistemology of explanatory integration in biology. *Erkenntnis*, 73(3):295–311, 2010.

G. E. Briggs and J. B. S. Haldane. A note on the kinetics of enzyme action. *Biochem J*, 19(2):338, 1925.

F. J. Bruggeman and H. V. Westerhoff. The nature of systems biology. *Trends Microbiol.*, 15 (1):45–50, 2007.

J. Calvert and J. H. Fujimura. Calculating life? duelling discourses in interdisciplinary systems biology. *Stud Hist Philos Biol Biomed Sci*, 42(2): 155–163, 2011.

W. B. Cannon. Organization for physiological homeostasis. *Phys Rev*, 9(3):399–431, 1929.

W. B. Cannon. *The wisdom of the body*. Norton & Co., 1939.

R. Carlson and F. Srienc. Fundamental *Escherichia coli* biochemical pathways for biomass and energy production: creation of overall flux states. *Biotechnol. Bioeng.*, 86(2):149–162, 2004a.

R. Carlson and F. Srienc. Fundamental *Escherichia coli* biochemical pathways for biomass and energy production: identification of reactions. *Biotechnol. Bioeng.*, 85(1):1–19, 2004b.

S. Casagranda, D. Ropers, and J.-L. Gouzé. Model reduction and process analysis of biological models. In *2015 23rd Mediterranean Conference on Control and Automation (MED)*, pages 1132–1139. IEEE, 2015.

S. Casagranda, S. Touzeau, D. Ropers, and J.-L. Gouzé. Principal process analysis of biological models. *BMC Syst. Biol.*, 12(1):68, 2018.

S. Chandrasekaran and N. D. Price. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in *Escherichia coli* and *Mycobacterium tuberculosis*. *Proc Nat Acad Sciences USA*, 107(41):17845–17850, 2010.

H. Chen, K. Shiroguchi, H. Ge, and X. Xie. Genome-wide study of mRNA degradation and transcript elongation in *Escherichia coli*. *Mol Syst Biol*, 11(1):781, 2015.

W. Chen, M. Niepel, and P. Sorger. Classic and contemporary approaches to modeling biochemical reactions. *Genes Dev.*, 24(17):1861–1875, 2010.

B. Choi, G. A. Rempala, and J. K. Kim. Beyond the Michaelis-Menten equation: Accurate and efficient estimation of enzyme kinetic parameters. *Sci Rep*, 7(1):17018, 2017.

A. Ciliberto, F. Capuani, and J. J. Tyson. Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation. *PLoS Comput. Biol.*, 3(3):e45, 2007.

E. Cinquemani, V. Laroute, M. Cocaïgn-Bousquet, H. de Jong, and D. Ropers. Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data. *Bioinformatics*, 33 (14):i301–i310, 2017.

C. Colijn, A. Brandes, J. Zucker, D. S. Lun, B. Weiner, M. R. Farhat, T.-Y. Cheng, D. B. Moody, M. Murray, and J. E. Galagan. Interpreting expression data with metabolic flux models: predicting *Mycobacterium tuberculosis* mycolic acid production. *PLoS Comput Biol*, 5(8): e1000489, 2009.

S. J. Cooper. From claude bernard to walter cannon. emergence of the concept of homeostasis. *Appetite*, 51(3):419–427, 2008.

F. Corblin, S. Tripodi, E. Fanchon, D. Ropers, and L. Trilling. A declarative constraint-based method for analyzing discrete genetic regulatory networks. *Biosystems*, 98(2):91–104, 2009.

A. Cornish-Bowden. One hundred years of michaelis–menten kinetics. *Perspectives Sci*, 4: 3–9, 2015.
C. Cotten and J. L. Reed. Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models. *BMC Bioinf.*, 14(1):32, 2013.

M. W. Covert, C. H. Schilling, and B. Palsson. Regulation of gene expression in flux balance models of metabolism. *J. Theor. Biol.*, 213(1):73–88, 2001.

M. W. Covert, N. Xiao, T. J. Chen, and J. R. Karr. Integrating metabolic, transcriptional regulatory and signal transduction models in *Escherichia coli*. *Bioinformatics*, 24(18):2044–2050, 2008.

H. de Jong. Modeling and simulation of genetic regulatory systems: a literature review. *J. Comput. Biol.*, 9(1):69–105, 2002.

H. de Jong and D. Ropers. Qualitative approaches to the analysis of genetic regulatory networks. *System Modeling in Cellular Biology: From Concepts to Nuts and Bolts*, pages 125–147, 2006a.

H. de Jong and D. Ropers. Strategies for dealing with incomplete information in the modeling of molecular interaction networks. *Briefings Bioinform.*, 7(4):354–363, 2006b.

H. de Jong, J. Geiselmann, C. Hernandez, and M. Page. Genetic Network Analyzer: qualitative simulation of genetic regulatory networks. *Bioinformatics*, 19:336–44, 2003.

H. de Jong, J.-L. Gouzé, C. Hernandez, M. Page, T. Sari, and J. Geiselmann. Qualitative simulation of genetic regulatory networks using piecewise-linear models. *Bull. Math. Biol.*, 66 (2):301–40, 2004.

H. de 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 Syst Biol*, 4:55, 2010.

H. de Jong, S. Casagranda, N. Giordano, E. Cinquemani, D. Ropers, J. Geiselmann, and J.-L. Gouzé. Mathematical modeling of microbes: Metabolism, gene expression, and growth. *J R Soc Interface*, 14:20170502, 2017a.

H. de Jong, J. Geiselmann, and D. Ropers. Resource reallocation in bacteria by reengineering

the gene expression machinery. *Trends Microbiol*, 25(6):480–493, 2017b.

P. Dennis and H. Bremer. Modulation of chemical composition and other parameters of the cell at different exponential growth rates. *EcoSal Plus*, 3(1):1–49, 2008.

P. Dennis, M. Ehrenberg, and H. Bremer. Control of rRNA synthesis in *Escherichia coli*: a systems biology approach. *Microbio.l Mol. Biol. Rev.*, 68 (4):639–68, 2004.

H. Dourado and M. Lercher. An analytical theory of balanced cellular growth. *Nat. Commun.*, 11: 1226, 2020.

R. G. Duggleby and R. B. Clarke. Experimental designs for estimating the parameters of the michaelis-menten equation from progress curves of enzyme-catalyzed reactions. *Biochim Biophys Acta*, 1080(3):231–236, 1991.

J. Edwards and B. Palsson. The *Escherichia coli* MG1655 *in silico* metabolic genotype: its definition, characteristics, and capabilities. *Proc. Natl. Acad. Sci. USA*, 97(10):5528–5533, 2000.

J. S. Edwards and B. O. Palsson. Systems properties of the *Haemophilus influenzae* Rd metabolic genotype. *J Biol Chem*, 274(25):17410–17416, 1999.

J. S. Edwards, R. U. Ibarra, and B. O. Palsson. In silico predictions of *Escherichia coli* metabolic capabilities are consistent with experimental data. *Nat. Biotechnol.*, 19(2):125–130, Feb 2001.

T. Esquerré, S. Laguerre, C. Turlan, A. Carpousis, L. Girbal, and M. Cocaïgn-Bousquet. Dual role of transcription and transcript stability in the regulation of gene expression in *Escherichia coli* cells cultured on glucose at different growth rates. *Nucleic Acids Res*, 42(4):2460–2472, 2014.

T. Esquerré, A. Moisan, H. Chiapello, L. Arike, R. Vilu, C. Gaspin, M. Cocaïgn-Bousquet, and L. Girbal. Genome-wide investigation of mRNA lifetime determinants in *Escherichia coli* cells cultured at different growth rates. *BMC Genomics*, 16(1):275, 2015.
T. Esquerré, M. Bouvier, C. Turlan, A. J. Car-
pousis, L. Girbal, and M. Cocaign-Bousquet.
The Csr system regulates genome-wide mRNA
stability and transcription and thus gene expres-
sion in *Escherichia coli*. *Sci. Rep.*, 6:25057, 2016.

T. Etienne, M. Cocaign-Bousquet, and D. Ropers.
Competitive effects in bacterial mRNA decay. *J.
Theor. Biol.*, 504:110333, 2020.

T. Etienne, E. Cinquemani, L. Girbal, M. Cocaign-Bousquet, and D. Ropers. A mechanistic model informed by dynamic omics data reveals the physiological control of bacterial mRNA decay. In preparation.

D. Eveillard, D. Ropers, H. De Jong, C. Branlant, and A. Bockmayr. Multiscale modeling of alternative splicing regulation. In *International Conference on Computational Methods in Systems Biology*, pages 75–87. Springer, 2003.

D. Eveillard, D. Ropers, H. de Jong, C. Branlant, and A. Bockmayr. A multi-scale constraint programming model of alternative splicing regulation. *Theor. Comput. Sci.*, 325(1):3–24, 2004.

A. M. Feist and B. O. Palsson. The biomass objective function. *Curr Opin Microbiol*, 13(3):344–349, 2010.

R. M. Fleming, I. Thiele, and H. Nasheuer. Quantitative assignment of reaction directionality in constraint-based models of metabolism: application to *Escherichia coli*. *Biophys Chem*, 145(2-3): 47–56, 2009.

R. M. Fleming, I. Thiele, G. Provan, and H. Nasheuer. Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism. *J. Theor. Biol.*, 264(3):683–692, 2010.

J. Geiselmann, H. de Jong, D. Ropers, and J. Izard. Method for producing metabolites, peptides and recombinant proteins, 2015. Also published as EP3047031 (A1) US2016222428 (A1) WO2015036622 (A1).

L. Gerosa, K. Kochanowski, M. Heinemann, and U. Sauer. Dissecting specific and global transcriptional regulation of bacterial gene expression. *Mol. Syst. Biol.*, 9:658, 2013.

L. Glass and S. Kauffman. The logical analysis of continuous, non-linear biochemical control networks. *J. Theor. Biol.*, 39(1):103–29, 1973.

A. Gonzalez, J. Uhlendorf, J. Schaul, E. Cinquemani, G. Batt, and G. Ferrari-Trecate. Identification of biological models from single-cell data: a comparison between mixed-effects and moment-based inference. In *2013 European Control Conference (ECC)*, pages 3652–3657. IEEE, 2013.

B. C. Goodwin et al. Temporal organization in cells. a dynamic theory of cellular control processes. *Temporal organization in cells. A dynamic theory of cellular control processes*. , 1963.

S. Gopalakrishnan, S. Dash, and C. Maranas.
K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data.
*Metabol. Eng.*, 2020.

C. Gu, G. B. Kim, W. J. Kim, H. U. Kim, and S. Y. Lee. Current status and applications of genome-scale metabolic models. *Genome Biol*, 20(1):121, 2019.

S. Gudmundsson, L. Agudo, and J. Nogales. Applications of genome-scale metabolic models of microalgae and cyanobacteria in biotechnology. In *Microalgae-Based Biofuels and Bioproducts*, pages 93–111. Elsevier, 2017.

H. Hallay, N. Locker, L. Ayadi, D. Ropers, E. Guittet, and C. Branlant. Biochemical and NMR study on the competition between proteins SC35, SRp40, and heterogeneous nuclear ribonucleoprotein A1 at the HIV-1 Tat exon 2 splicing site. *J. Biol. Chem.*, 281(48):37159–37174, 2006.

T. J. Hanly and M. A. Henson. Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures. *Biotechnol. Bioeng.*, 108(2):376–385, 2011.

H. S. Haraldsdóttir, B. Cousins, I. Thiele, R. M. Fleming, and S. Vempala. Chrr: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. *Bioinformatics*, 33(11): 1741–1743, 2017.
W. R. Harcombe, N. F. Delaney, N. Leiby, N. Kl-
itgord, and C. J. Marx. The ability of flux
balance analysis to predict evolution of central
metabolism scales with the initial distance to
the optimum. *PLoS Comput Biol*, 9(6):e1003091,
2013.

J. Heijnen. Approximative kinetic formats used in
metabolic network modeling. *Biotechnol. Bio-
eng.*, 91(5):534–545, 2005.

R. Heinrich and T. A. Rapoport. A linear steady-state treatment of enzymatic chains: general properties, control and effector strength. *Eur. J. Biochem.*, 42(1):89–95, 1974.

R. Heinrich and S. Schuster. *The Regulation of Cellular Systems*. Chapman and Hall, New-York, 1996.

L. Heirendt, S. Arreckx, T. Pfau, S. N. Mendoza, A. Richelle, A. Heinken, H. S. Haraldsdóttir, J. Wachowiak, S. M. Keating, V. Vlasov, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0. *Nat. protoc.*, 14(3):639–702, 2019.

C. S. Henry, M. D. Jankowski, L. J. Broadbelt, and V. Hatzimanikatis. Genome-scale thermodynamic analysis of *Escherichia coli* metabolism. *Biophysical J*, 90(4):1453–1461, 2006.

C. S. Henry, L. J. Broadbelt, and V. Hatzi-
manikatis. Thermodynamics-based metabolic
flux analysis. *Biophys. J.*, 92(5):1792–1805,
2007.

A. Hoppe, S. Hoffmann, and H.-G. Holzhütter. In-
cluding metabolite concentrations into flux bal-
ance analysis: thermodynamic realizability as a
constraint on flux distributions in metabolic net-
works. *BMC Syst. Biol.*, 1(1):23, 2007.

R. U. Ibarra, J. S. Edwards, and B. O. Palsson. *Escherichia coli* K-12 undergoes adaptive evolution to achieve *in silico* predicted optimal growth. *Nature*, 420(6912):186–189, 2002.

T. Ideker, T. Galitski, and L. Hood. A new ap-
proach to decoding life: systems biology. *Annu
Rev Genomics Hum Genet*, 2(1):343–372, 2001.

J. Izard, C. Gomez Balderas, D. Ropers, S. La-
cour, X. Song, Y. Yang, A. Lindner, J. Geisel-
mann, and H. de Jong. A synthetic growth

switch based on controlled expression of RNA
polymerase. *Mol Syst Biol*, 11(11):840, 2015.

S. Jacquenet, D. Ropers, P. S. Bilodeau, L. Damier, A. Mougin, C. M. Stoltzfus, and C. Branlant. Conserved stem-loop structures in the HIV-1 RNA region containing the A3 3' splice site and its cis-regulatory element: possible involvement in RNA splicing. *Nucleic Acids Res.*, 29(2):464–478, 2001.

M. L. Jenior, T. J. Moutinho Jr, B. V. Dougherty, and J. A. Papin. Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. *PLOS Computational Biology*, 16(4):e1007099, 2020.

P. A. Jensen and J. A. Papin. Functional integration of a metabolic network model and expression data without arbitrary thresholding. *Bioinformatics*, 27(4):541–547, 2011.

K. A. Johnson. A century of enzyme kinetic analysis, 1913 to 2013. *FEBS Lett*, 587(17):2753–2766, 2013.

S. Jun, F. Si, R. Pugatch, and M. Scott. Fundamental principles in bacterial physiology-history, recent progress, and the future with focus on cell size control: a review. *Rep. Prog. Phys.*, 81(5): 056601, 2018.

H. Kacser. The control of flux. In *Symp. Soc. Exp. Biol.*, volume 27, pages 65–104, 1973.

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

S. Kauffman. Gene regulation networks: A theory for their global structure and behaviors. In *Curr. Top. Dev. Biol.*, volume 6, pages 145–182. Elsevier, 1971.

S. A. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. *J. Theor. Biol.*, 22(3):437–467, 1969.

D. E. Kaufman and R. L. Smith. Direction choice for accelerated convergence in hit-and-run sampling. *Oper. Res.*, 46(1):84–95, 1998.
T. C. Keaty and P. A. Jensen. Gapsplit: Efficient random sampling for non-convex constraint-based models. *Bioinformatics*, 36(8):2623-2625, 2020.

D. Kell, M. Brown, H. Davey, W. Dunn, I. Spasic, and S. Oliver. Metabolic footprinting and systems biology: the medium is the message. *Nat. Rev. Microbiol.*, 3(7):557-65, 2005.

D. B. Kell and S. G. Oliver. Here is the evidence, now what is the hypothesis? the complementary roles of inductive and hypothesis-driven science in the post-genomic era. *Bioessays*, 26(1):99-105, 2004.

L. Keren, O. Zackay, M. Lotan-Pompan, U. Barenholz, E. Dekel, V. Sasson, G. Aidelberg, A. Bren, D. Zeevi, A. Weinberger, U. Alon, R. Milo, and E. Segal. Promoters maintain their relative activity levels under different growth conditions. *Mol. Syst. Biol.*, 9:701, 2013.

G. Khoury, L. Ayadi, J. M. Sailou, S. Sanglier, D. Ropers, and C. Branlant. New actors in regulation of HIV-1 tat mRNA production. *Retrovirology*, 6(2):1-1, 2009.

Z. A. King, J. Lu, A. Dräger, P. Miller, S. Federowicz, J. A. Lerman, A. Ebrahim, B. O. Palsson, and N. E. Lewis. BiGG models: A platform for integrating, standardizing and sharing genome-scale models. *Nucleic Acids Res.*, 44(D1):D515-D522, 2016.

M. W. Kirschner. The meaning of systems biology. *Cell*, 121(4):503-504, 2005.

H. Kitano. Systems biology: toward system-level understanding of biological systems. *Foundations of Systems Biology*, pages 1-36, 2001.

H. Kitano. Looking beyond the details: a rise in system-oriented approaches in genetics and molecular biology. *Curr. Genet.*, 41(1):1-10, 2002.

E. Klipp, W. Liebermeister, C. Wierling, and A. Kowald. *Systems biology: a textbook*. John Wiley & Sons, 2016.

S. Klumpp and T. Hwa. Growth-rate-dependent partitioning of RNA polymerases in bacteria. *Proc Natl Acad Sci U S A*, 105:20245-20250, 2008.

K. Kochanowski, B. Volkmer, L. Gerosa, B. R. Haverkorn van Rijsewijk, A. Schmidt, and M. Heinemann. Functioning of a metabolic flux sensor in *Escherichia coli*. *Proc Natl Acad Sci USA*, 110(3):1130-1135, Jan 2013.

K. Kohn. Molecular interaction maps as information organizers and simulation guides. *Chaos*, 11 (1):84-97, 2001.

O. Kotte, J. B. Zaugg, and M. Heinemann. Bacterial adaptation through distributed sensing of metabolic fluxes. *Mol. Syst. Biol.*, 6:355, 2010.

A. Kümmel, S. Panke, and M. Heinemann. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. *Mol. Syst. Biol.*, 2(1): 2006-0034, 2006a.

A. Kümmel, S. Panke, and M. Heinemann. Systematic assignment of thermodynamic constraints in metabolic network models. *BMC Bioinf.*, 7(1): 1-12, 2006b.

S. Laguerre, I. González, S. Nouaille, A. Moisan, N. Villa-Vialaneix, C. Gaspin, M. Bouvier, A. J. Carpousis, M. Cocaign-Bousquet, and L. Girbal. Large-scale measurement of mRNA degradation in *Escherichia coli*: To delay or not to delay. *Methods Enzymol.*, 612:47-66, 2018.

K. Larrabee, J. Phillips, G. Williams, and A. Larrabee. The relative rates of protein synthesis and degradation in a growing culture of *Escherichia coli*. *J Biol Chem*, 255(9):4125-4130, 1980.

M. Lavielle. *Mixed effects models for the population approach: models, tasks, methods and tools*. CRC press, 2014.

D. Lee, K. Smallbone, W. B. Dunn, E. Murabito, C. L. Winder, D. B. Kell, P. Mendes, and N. Swainston. Improving metabolic flux predictions using absolute gene expression data. *BMC Syst Biol*, 6(1):73, 2012.

J. M. Lee, E. P. Gianchandani, J. A. Eddy, and J. A. Papin. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. *PLoS Comput Biol*, 4(5):e1000086, 2008.
N. E. Lewis, K. K. Hixson, T. M. Conrad, J. A. Lerman, P. Charusanti, A. D. Polpitiya, J. N. Adkins, G. Schramm, S. O. Purvine, D. Lopez-Ferrer, et al. Omic data from evolved *E. coli* are consistent with computed optimal growth from genome-scale models. *Mol. Syst. Biol.*, 6(1):390, 2010.

D. Lipson. The complex relationship between mi-
crobial growth rate and yield and its implications
for ecosystem processes. *Front. Microbiol.*, 6:615,
2015.

D. Machado and M. Herrgård. Systematic evalua-
tion of methods for integration of transcriptomic
data into constraint-based models of metabolism.
*PLoS Comp. Biol.*, 10(4):e1003580, 2014.

R. Mahadevan and C. Schilling. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. *Metabol. Eng.*, 5(4):264–276, 2003.

R. Mahadevan, J. S. Edwards, and F. J. Doyle III. Dynamic flux balance analysis of diauxic growth in *Escherichia coli*. *Biophys. J.*, 83(3):1331–1340, 2002.

A. Marguet, M. Lavielle, and E. Cinquemani. In-
heritance and variability of kinetic gene expres-
sion parameters in microbial cells: modeling and
inference from lineage tree data. *Bioinformatics*,
35(14):i586–i595, 2019.

Y. Martin, M. Page, C. Blanchet, and H. de Jong. WellInverter: a web application for the analysis of fluorescent reporter gene data. *BMC Bioinf*, 20(1):309, 2019.

W. Megchelenbrink, M. Huynen, and E. Mar-
chiori. optGpSampler: an improved tool for uni-
formly sampling the solution-space of genome-
scale metabolic networks. *PLoS one*, 9(2):
e86587, 2014.

M. D. Mesarović. Systems theory and biol-
ogy—view of a theoretician. In *Systems theory
and biology*, pages 59–87. Springer, 1968.

T. Mestl, E. Plahte, and S. Omholt. A mathemati-
cal framework for describing and analysing gene
regulatory networks. *J. Theor. Biol.*, 176(2):291–
300, 1995.

A. Métris, S. M. George, and D. Ropers. Piece-wise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens. *Int J Food Microbiol*, 240:63–74, 2017.

L. Michaelis and M. L. Menten. The kinetics of the
inversion effect. *Biochem. Z*, 49:333–369, 1913.

P. Mitchell. Coupling of phosphorylation to elec-
tron and hydrogen transfer by a chemi-osmotic
type of mechanism. *Nature*, 191(4784):144–148,
1961.

M. L. Mo, B. Ø. Palsson, and M. J. Herrgård. Con-
necting extracellular metabolomic measurements
to intracellular flux states in yeast. *BMC Syst.
Biol.*, 3(1):37, 2009.

D. Molenaar, R. Van Berlo, D. De Ridder, and B. Teusink. Shifts in growth strategies reflect tradeoffs in cellular economics. *Molecular systems biology*, 5(1):323, 2009.

P. Monteiro, D. Ropers, R. Mateescu, A. Freitas, and H. de Jong. Temporal logic patterns for querying dynamic models of cellular interaction networks. *Bioinformatics*, 24(16):i227–33, 2008.

P. T. Monteiro, P. J. Dias, D. Ropers, A. L. Oliveira, I. Sa-Correia, M. C. Teixeira, and A. T. Freitas. Qualitative modelling and formal verification of the FLR1 gene mancozeb response in *Saccharomyces cerevisiae*. *IET Syst Biol*, 5:308–316, 2011.

M. Morin, D. Ropers, F. Letisse, S. Laguerre, J.-C. Portais, M. Cocaign-Bousquet, and B. Enjalbert. The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of *Escherichia coli*. *Mol Microbiol*, 100 (4):686–700, 2016.

M. Morin, D. Ropers, E. Cinquemani, J. Portais, B. Enjalbert, and M. Cocaign-Bousquet. The Csr system regulates *Escherichia coli* fitness by controlling glycogen accumulation and energy levels. *mBio*, 8(5), 2017.

M. Morin, B. Enjalbert, D. Ropers, L. Girbal, and M. Cocaign-Bousquet. Genomewide stabilization of mRNA during a “feast-to-famine” growth transition in *Escherichia coli*. *Msphere*, 5(3), 2020.
A. C. Müller and A. Bockmayr. Fast thermodynamically constrained flux variability analysis. *Bioinformatics*, 29(7):903–909, 2013.

E. Noor, S. Cherkaoui, and U. Sauer. Biological insights through omics data integration. *Curr. Opin. Syst. Biol.*, 15:39–47, 2019.

C. J. Norsigian, N. Pusarla, J. L. McConn, J. T. Yurkovich, A. Dräger, B. O. Palsson, and Z. King. BiGG models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. *Nucleic Acids Res.*, 48(D1): D402–D406, 2020.

S. Nouaille, S. Mondeil, A.-L. Finoux, C. Moulis, L. Girbal, and M. Cocaïn-Bousquet. The stability of an mRNA is influenced by its concentration: a potential physical mechanism to regulate gene expression. *Nucleic Acids Res*, 45(20): 11711–11724, 2017.

E. O'Brien, J. Lerman, R. Chang, D. Hyduke, and B. Palsson. Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. *Mol. Syst. Biol.*, 9:693, 2013.

M. Okino and M. Mavrovouniotis. Simplification of mathematical models of chemical reaction systems. *Chemical Rev*, 98(2):391–408, 1998.

R. E. O'Malley. *Singular perturbation methods for ordinary differential equations*, volume 89. Springer, 1991.

J. D. Orth, I. Thiele, and B. Ø. Palsson. What is flux balance analysis? *Nature Biotechnol.*, 28(3): 245–248, 2010.

J. Papin, J. Stelling, N. Price, S. Klamt, S. Schuster, and B. Palsson. Comparison of network-based pathway analysis methods. *Trends Biotechnol*, 22(8):400–405, 2004.

E. Pecou. Splitting the dynamics of large biochemical interaction networks. *J. Theor. Biol.*, 232(3): 375–384, 2005.

M. G. Pedersen, A. M. Bersanib, and E. Bersanic. The total quasi-steady-state approximation for fully competitive enzyme reactions. *Bull. Math. Biol.*, 69(1):433, 2007.

M. G. Pedersen, A. M. Bersani, and E. Bersani. Quasi steady-state approximations in complex intracellular signal transduction networks–a word of caution. *J Math Chem*, 43(4):1318–1344, 2008a.

M. G. Pedersen, A. M. Bersani, E. Bersani, and G. Cortese. The total quasi-steady-state approximation for complex enzyme reactions. *Math. Comput. Simul.*, 79(4):1010–1019, 2008b.

S. Pinhal, D. Ropers, J. Geiselmann, and H. de Jong. Acetate metabolism and the inhibition of bacterial growth by acetate. *J. Bacteriol.*, 201(13):e00147–19, 2019.

C. Pourciau, Y.-J. Lai, M. Gorelik, P. Babitzke, and T. Romeo. Diverse mechanisms and circuitry for global regulation by the RNA-binding protein CsrA. *Front Microbiol*, 11:2709, 2020.

N. D. Price, J. L. Reed, and B. Ø. Palsson. Genome-scale models of microbial cells: evaluating the consequences of constraints. *Nature Rev. Microbiol.*, 2(11):886–897, 2004.

I. Prigogine and G. Nicolis. Biological order, structure and instabilities. *Q. Rev. Biophys.*, 4(2-3): 107–148, 1971.

T. Pusa, M. Ferrarini, R. Andrade, A. Mary, A. Marchetti-Spaccamela, L. Stougie, and M. Sagot. MOOMIN–mathematical exploration of’omics data on a metabolic network. *Bioinformatics*, 36(2):514–523, 2020.

H. Qian and D. A. Beard. Thermodynamics of stoichiometric biochemical networks in living systems far from equilibrium. *Biophys. Chem.*, 114 (2-3):213–220, 2005.

O. Radulescu, A. N. Gorban, A. Zinovyev, and V. Noel. Reduction of dynamical biochemical reactions networks in computational biology. *Front. Genet.*, 3:131, 2012.

C. Ramon, M. G. Gollub, and J. Stelling. Integrating–omics data into genome-scale metabolic network models: principles and challenges. *Essays Biochem.*, 62(4):563–574, 2018.
T. Romeo, M. Gong, M. Y. Liu, and A.-M. Brun-Zinkernagel. Identification and molecular characterization of *csrA*, a pleiotropic gene from *Escherichia coli* that affects glycogen biosynthesis, gluconeogenesis, cell size, and surface properties. *J. Bacteriol.*, 175(15):4744-4755, 1993.

M. Ronen, R. Rosenberg, B. I. Shraiman, and U. Alon. Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. *Proc Natl Acad Sci USA*, 99:10555-10560, 2002.

D. Ropers and A. Métris. Data for the qualitative modeling of the osmotic stress response to NaCl in *Escherichia coli*. *Data Brief*, 9:606-612, 2016.

D. Ropers, L. Ayadi, R. Gattoni, S. Jacquenet, L. Damier, C. Branlant, and J. Stévenin. Differential effects of the SR proteins 9G8, SC35, ASF/SF2, and SRp40 on the utilization of the A1 to A5 splicing sites of HIV-1 RNA. *J. Biol. Chem.*, 279(29):29963-29973, 2004.

D. Ropers, H. de Jong, M. Page, D. Schneider, and J. Geiselmann. Qualitative simulation of the carbon starvation response in *Escherichia coli*. *Biosystems*, 84(2):124-152, 2006.

D. Ropers, H. de Jong, J.-L. Gouzé, M. Page, D. Schneider, and J. Geiselmann. *Piecewise-Linear Models of Genetic Regulatory Networks: Analysis of the Carbon Starvation Response in Escherichia coli*, chapter 8, pages 83-96. Springer, 2007. Proceedings of the Fifth European Conference on Mathematical and Theoretical Biology (ECMTB05), Dresden, Germany.

D. Ropers, V. Baldazzi, and H. de Jong. Model reduction using piecewise-linear approximations preserves dynamic properties of the carbon starvation response in *Escherichia coli*. *IEEE/ACM Trans. Comput. Biol. Bioinform.*, 8(1):166-181, 2011.

R. Rosen. A relational theory of biological systems. *Bull Math Biophys*, 20(3):245-260, 1958.

H. Rottenberg, S. Caplan, and A. Essig. Stoichiometry and coupling: theories of oxidative phosphorylation. *Nature*, 216(5115):610-611, 1967.

M. R. Roussel and S. J. Fraser. Invariant manifold methods for metabolic model reduction. *Chaos*, 11(1):196-206, 2001.

P. Salvy and V. Hatzimanikatis. The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models. *Nature Comm.*, 11(1):1-17, 2020.

U. Sauer, D. C. Cameron, and J. E. Bailey. Metabolic capacity of *Bacillus subtilis* for the production of purine nucleosides, riboflavin, and folic acid. *Biotechnol Bioeng*, 59(2):227-238, 1998.

M. Savageau. Design principles for elementary gene circuits: Elements, methods, and examples. *Chaos*, 11(1):142-159, 2001.

M. A. Savageau. Biochemical systems analysis: a study of function and design in molecular biology. In *ADDISON WESLEY PUBL*. 1976.

C. H. Schilling, D. Letscher, and B. Ø. Palsson. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. *J. Theor. Biol.*, 203(3):229-248, 2000.

S. Schnell and P. Maini. Enzyme kinetics at high enzyme concentration. *Bull. Math. Biol.*, 62(3): 483-499, 2000.

S. Schnell and P. K. Maini. A century of enzyme kinetics. should we believe in the Km and Vmax estimates? *Comments Theor. Biol*, 8:169-187, 2003.

R. Schuetz, L. Kuepfer, and U. Sauer. Systematic evaluation of objective functions for predicting intracellular fluxes in *Escherichia coli*. *Molecular systems biology*, 3(1):119, 2007.

S. Schuster, T. Dandekar, and D. A. Fell. Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. *Trends Biotechnol*, 17(2): 53-60, 1999.

I. Segel. *Enzyme kinetics: behavior and analysis of rapid equilibrium and steady state enzyme systems*. Wiley & Sons, 1993.
L. A. Segel. On the validity of the steady state assumption of enzyme kinetics. *Bull. Math. Biol.*, 50(6):579-593, 1988.

L. A. Segel and M. Slemrod. The quasi-steady-state assumption: a case study in perturbation. *SIAM rev*, 31(3):446-477, 1989.

M. Shamir, Y. Bar-On, R. Phillips, and R. Milo. Snapshot: timescales in cell biology. *Cell*, **164**(6):1302-1302, 2016.

T. Shlomi, M. N. Cabili, M. J. Herrgård, B. Ø. Palsson, and E. Ruppin. Network-based prediction of human tissue-specific metabolism. *Nature Biotechnol.*, 26(9):1003-1010, 2008.

K. Smallbone, E. Simeonidis, D. S. Broomhead, and D. B. Kell. Something from nothing- bridging the gap between constraint-based and kinetic modelling. *The FEBS journal*, 274(21):5576-5585, 2007.

C. D. Smolke and P. A. Silver. Informing biological design by integration of systems and synthetic biology. *Cell*, 144(6):855-859, 2011.

D. Stefan, C. Pinel, S. Pinhal, E. Cinquemani, J. Geiselmann, and H. de Jong. Inference of quantitative models of bacterial promoters from time-series reporter gene data. *PLoS Comput Biol*, 11(1):e1004028, 2015.

G. Stephanopoulos, A. Aristidou, and J. Nielsen. *Metabolic Engineering: Principles and Methodologies*. Academic Press, San Diego, CA, 1998.

W. Stroberg and S. Schnell. On the estimation errors of KM and V from time-course experiments using the michaelis-menten equation. *Biophys Chem*, 219:17-27, 2016.

J. Tang and W. Riley. A total quasi-steady-state formulation of substrate uptake kinetics in complex networks and an example application to microbial litter decomposition. *Biogeosciences*, 10 (12):8329-8351, 2013.

B. H. ter Kuile and H. V. Westerhoff. Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway. *FEBS Lett*, 500(3):169-171, 2001.

I. Thiele, S. Sahoo, A. Heinken, J. Hertel, L. Heirendt, M. K. Aurich, and R. M. Fleming. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. *Mol. Syst. Biol.*, 16(5):e8982, 2020.

R. Thomas and R. d'Ari. *Biological Feedback*. CRC Press, Boca Raton, FL, 1990.

M. Tian and J. L. Reed. Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis. *Bioinformatics*, 34(22):3882-3888, 2018.

J. Timmermans and L. Van Melderen. Conditional essentiality of the i gene in *Escherichia coli*. *J. Bacteriol.*, 191(5):1722-1724, 2009.

K. Tummler, T. Lubitz, M. Schelker, and E. Klipp. New types of experimental data shape the use of enzyme kinetics for dynamic network modeling. *FEBS J*, 281(2):549-571, 2014.

A. Turing. The chemical theory of morphogenesis. *Phil. Trans. Roy. Soc*, 13(1), 1952.

A. Tzafriri. Michaelis-Menten kinetics at high enzyme concentrations. *Bull. Math. Biol.*, 65(6): 1111-1129, 2003.

A. Tzafriri, M. Bercovier, and H. Parnas. Reaction diffusion model of the enzymatic erosion of insoluble fibrillar matrices. *Biophys J*, 83(2):776-793, 2002.

R. J. van Berlo, D. de Ridder, J.-M. Daran, P. A. Daran-Lapujade, B. Teusink, and M. J. Reinders. Predicting metabolic fluxes using gene expression differences as constraints. *IEEE/ACM Trans. Comput. Biol. Bioinform.*, 8(1):206-216, 2009.

K. van Eunen, S. Rossell, J. Bouwman, H. V. Westerhoff, and B. M. Bakker. Quantitative analysis of flux regulation through hierarchical regulation analysis. In *Methods Enzymol.*, volume 500, pages 571-595. Elsevier, 2011.

A. Varma and B. Palsson. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type *Escherichia coli* w3110. *Appl Environ Microbiol*, 60 (10):3724-3731, 1994a.
A. Varma and B. O. Palsson. Metabolic flux bal-
ancing: basic concepts, scientific and practical
use. *Nature Biotechnol.*, 12(10):994–998, 1994b.

E. Voit. *A first course in systems biology*. Garland Science, 2017.

E. O. Voit, H. A. Martens, and S. W. Omholt. 150 years of the mass action law. *PLoS Comput. Biol.*, 11(1):e1004012, 2015.

S. Volkova, M. R. Matos, M. Mattanovich, and I. Marín de Mas. Metabolic modelling as a framework for metabolomics data integration and analysis. *Metabolites*, 10(8):303, 2020.

L. von Bertalanffy. Der organismus als physikalischen system betrachtet. *Naturwissenschaften*, 28 (33):521–531, 1940.

L. Von Bertalanffy. The theory of open systems in physics and biology. *Science*, 111(2872):23–29, 1950.

L. Von Bertalanffy. General system theory: Foundations, development, applications. Technical report, Georges Braziller, Inc., 1969.

G. Wahba. *Spline models for observational data*. SIAM, 1990.

Y. Wang, J. A. Eddy, and N. D. Price. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. *BMC Syst Biol*, 6(1):153, 2012.

A. Y. Weiße, D. A. Oyarzún, V. Danos, and P. S. Swain. Mechanistic links between cellular trade-offs, gene expression, and growth. *Proc Natl Acad Sci USA*, 112(9):E1038–E1047, 2015.

H. V. Westerhoff and B. O. Palsson. The evolution of molecular biology into systems biology. *Nature Biotechnol.*, 22(10):1249–1252, 2004.

S. J. Wiback, I. Famili, H. J. Greenberg, and B. Ø. Palsson. Monte Carlo sampling can be used to determine the size and shape of the steady-state flux space. *J. Theor. Biol.*, 228(4):437–447, 2004.

W. Wiechert. 13c metabolic flux analysis. *Metab. Eng.*, 3(3):195–206, 2001.

N. Wiener. *Cybernetics or Control and Communication in the Animal and the Machine*. MIT press, 1948.

A. J. Wolfe. The acetate switch. *Microbio.l Mol. Biol. Rev.*, 69(1):12–50, 2005.

O. Wolkenhauer. Systems biology: the reincarna-
tion of systems theory applied in biology? *Brief. Bioinform.*, 2(3):258–270, 2001.

K. Yizhak, T. Benyamini, W. Liebermeister, E. Ruppin, and T. Shlomi. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. *Bioinformatics*, 26(12):i255–i260, 2010.

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*, 31(12):i71–i79, 2015.

C. Zupke and G. Stephanopoulos. Modeling of isotope distributions and intracellular fluxes in metabolic networks using atom mapping matrices. *Biotechnol. Prog.*, 10(5):489–498, 1994.