A. Fukushima, M. Kusano, H. Redestig, M. Arita, and K. Saito, Integrated omics approaches in plant systems biology, Curr Opin Chem Biol, vol.13, issue.5-6, pp.532-540, 2009.

J. Stelling, Mathematical models in microbial systems biology, Curr Opin Microbiol, vol.7, issue.5, p.15451507, 2004.

B. Pereira, J. Miguel, P. Vilaça, S. Soares, I. Rocha et al., Reconstruction of a genome-scale metabolic model for Actinobacillus succinogenes 130Z, BMC Syst Biol, vol.12, 2018.

I. Nookaew, R. Olivares-herná-ndez, S. Bhumiratana, and J. Nielsen, Genome-Scale Metabolic Models of Saccharomyces cerevisiae, Methods in molecular biology, pp.445-63, 2011.

C. H. Schilling, M. W. Covert, I. Famili, G. M. Church, J. S. Edwards et al., Genome-Scale Metabolic Model of Helicobacter pylori 26695, J Biotechnol, vol.184, issue.16, pp.4582-93, 2002.

A. Acharjee, B. Kloosterman, R. Visser, and C. Maliepaard, Integration of multi-omics data for prediction of phenotypic traits using random forest, BMC Bioinformatics [Internet], vol.17, issue.5, pp.363-73, 2016.

C. E. Wheelock, V. G. , D. Balgoma, B. N. Brandsma, J. Paulj et al., Application of 'omics technologies to biomarker discovery in inflammatory lung diseases, Eur Respir J, vol.42, issue.3, p.23397306, 2013.

G. N. Vemuri and A. A. Aristidou, Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks, Microbiol Mol Biol Rev, vol.69, issue.2, p.15944454, 2005.

G. Q. Chen, Omics Meets Metabolic Pathway Engineering. Cell Syst, vol.2, issue.6, p.27237740, 2016.

N. C. Duarte, M. J. Herrgård, and B. Ø. Palsson, Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model, Genome Res, vol.14, issue.7, p.15197165, 2004.

J. Fö-rster, I. Famili, P. Fu, B. Ø. Palsson, and J. Nielsen, Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network, Genome Res, vol.13, issue.2, p.12566402, 2003.

N. D. Price, J. L. Reed, and B. Palsson, Genome-scale models of microbial cells: Evaluating the consequences of constraints, Nat Rev Microbiol, vol.2, issue.11, p.15494745, 2004.

A. M. Feist, C. S. Henry, J. L. Reed, M. Krummenacker, A. R. Joyce et al., A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information, Mol Syst Biol, vol.3, issue.1, p.121, 2007.

J. L. Reed and B. Ø. Palsson, Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli, J Biotechnol, vol.185, issue.9, pp.2692-2701, 2003.

D. S. Weaver, I. M. Keseler, A. Mackie, I. T. Paulsen, and P. D. Karp, A genome-scale metabolic flux model of Escherichia coli K-12 derived from the EcoCyc database, BMCSystems Biol [Internet], vol.8, issue.79, pp.1-24, 2014.

R. Rios-estepa and B. M. Lange, Experimental and mathematical approaches to modeling plant metabolic networks, Phytochemistry, vol.68, p.17561179, 2007.

J. Arturo and M. Mora, System biology: Mathematical modeling of biological systems, Int J Adv Res Comput Eng Technol, vol.5, issue.8, pp.2243-2249, 2016.

. Volkenstein-mv, Mathematical Modeling of Biological Processes, Physics and Biology, vol.154, 2012.

Z. Nikoloski, R. Perez-storey, and L. J. Sweetlove, Inference and prediction of metabolic network fluxes, Plant Physiol, vol.169, p.26392262, 2015.

J. B. Fié-vet, C. Dillmann, G. Curien, and D. De-vienne, Simplified modelling of metabolic pathways for flux prediction and optimization: lessons from an in vitro reconstruction of the upper part of glycolysis, Biochem J [Internet], vol.396, issue.2, p.16460310, 2006.

M. R. Antoniewicz, G. Stephanopoulos, and J. K. Kelleher, Evaluation of regression models in metabolic physiology: Predicting fluxes from isotopic data without knowledge of the pathway, Metabolomics, vol.2, issue.1, p.17066125, 2006.

J. Almquist, M. Cvijovic, V. Hatzimanikatis, and J. Nielsen, Kinetic models in industrial biotechnology-Improving cell factory performance, Metab Eng [Internet], vol.24, p.24747045, 2014.

R. Steuer, T. Gross, J. Selbig, and B. Blasius, Structural kinetic modeling of metabolic networks, Proc Natl Acad Sci, vol.103, issue.32, p.16880395, 2006.

S. Srinivasan, W. R. Cluett, and R. Mahadevan, Constructing kinetic models of metabolism at genome-scales: A review, Biotechnol J, vol.10, issue.9, p.26332243, 2015.

M. W. Covert, I. Famili, and B. O. Palsson, Identifying Constraints that Govern Cell Behavior: A Key to Converting Conceptual to Computational Models in Biology?, Biotechnol Bioeng, vol.84, issue.7, p.14708117, 2003.

S. Vijayakumar, M. Conway, P. Lió, and C. Angione, Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling, Brief Bioinform, vol.19, issue.6, p.28575143, 2018.

A. Cornish-bowden and C. Wharton, Enzyme kinetics (In Focus). oxford: IRL press ltd, 1988.

B. Teusink, J. Passarge, C. A. Reijenga, E. Esgalhado, C. C. Van-der-weijden et al., Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry

J. Eur and . Biochem, , vol.267, p.10951190, 2000.

H. Bisswanger, Enzyme assays, Perspect Sci [Internet], vol.1, pp.41-55, 2014.

B. E. Wright, M. H. Butler, and K. R. Albe, Systems analysis of the tricarboxylic acid cycle in dictyostelium discoideum. I. The basis for model construction, J Biol Chem, vol.267, issue.5, p.1737766, 1992.

K. R. Albe and B. E. Wright, Systems Analysis of the Tricarboxylic Acid Cycle in Dictyostelium discoideum II. control Analysis, J Biol Chem, vol.267, issue.5, p.1737767, 1992.

J. D. Orth, I. Thiele, and B. O. Palsson, What is flux balance analysis?, Nat Biotechnol [Internet], vol.28, issue.3, p.20212490, 2010.

W. Wiechert, 13C Metabolic Flux Analysis, Metab Eng [Internet], vol.3, issue.3, p.11461141, 2001.

J. S. Edwards and B. O. Palsson, The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities, Proc Natl Acad Sci, vol.97, issue.10, pp.5528-5561, 2002.

N. Jamshidi and B. Palsson, Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets, BMC Syst Biol, vol.1, issue.26, pp.1-20, 2007.

S. Claudia and J. C. Quintero, Flux Balance Analysis in the Production of Clavulanic Acid by Streptomyces clavuligerus, Biotechnol Prog, vol.31, issue.5, p.26171767, 2015.

B. Christensen and J. Nielsen, Metabolic Network Analysis of Penicillium chrysogenum Using C-Labeled Glucose, Biotechnol Bioeng, vol.68, issue.6, p.10799990, 2000.

X. Chen, A. P. Alonso, D. K. Allen, J. L. Reed, and Y. Shachar-hill, Synergy between 13C-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in E. coli, Metab Eng [Internet], vol.13, issue.1, p.21129495, 2010.

A. A. De-graaf, M. Mahle, M. Mö-llney, W. Wiechert, P. Stahmann et al., Determination of full 13C isotopomer distributions for metabolic flux analysis using heteronuclear spin echo difference NMR spectroscopy, J Biotechnol, vol.77, issue.1, p.10674212, 2000.

C. Mead and I. Chopra, Organic Acids: Chemistry. Antibacterial Activity and Practical Applications, Adv Microb Physiol, vol.32, p.1882730, 1991.

R. Alsaheb, A. Aladdin, N. Z. Othman, R. A. Malek, O. M. Leng et al., Lactic acid applications in pharmaceutical and cosmeceutical industries, J Chem Pharm Res, vol.7, issue.10, pp.729-764, 2015.

K. J. Weissman and P. F. Leadlay, Combinatorial biosynthesis of reduced polyketides, Nat Rev Microbiol, vol.3, issue.12, p.16322741, 2005.

A. R. Awan, B. A. Blount, D. J. Bell, W. M. Shaw, J. Ho et al., Biosynthesis of the antibiotic nonribosomal peptide penicillin in baker's yeast, Nat Commun, vol.8, pp.1-8, 2017.

S. Haris, C. Fang, J. R. Bastidas-oyanedel, K. J. Prather, J. E. Schmidt et al., Natural antibacterial agents from arid-region pretreated lignocellulosic biomasses and extracts for the control of lactic acid bacteria in yeast fermentation, vol.8, pp.1-7, 2018.

W. A. Khattak, M. Ul-islam, M. W. Ullah, B. Yu, S. Khan et al., Yeast cell-free enzyme system for bio-ethanol production at elevated temperatures, Process Biochem, vol.49, issue.3, pp.357-64, 2014.

Y. Zhang, J. Sun, and J. J. Zhong, Biofuel production by in vitro synthetic enzymatic pathway biotransformation, Curr Opin Biotechnol [Internet], vol.21, issue.5, p.20566280, 2010.

Y. Zhang, Renewable carbohydrates are a potential high-density hydrogen carrier, Int J Hydrogen Energy [Internet], vol.35, pp.10334-10376, 2010.

H. Yim, R. Haselbeck, W. Niu, C. Pujol-baxley, A. Burgard et al., Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol, Nat Chem Biol [Internet], vol.7, issue.7, p.21602812, 2011.

L. A. Anderson, M. Islam, and K. Prather, Synthetic biology strategies for improving microbial synthesis of "green" biopolymers, J Biol Chem [Internet], vol.293, p.29339554, 2018.

J. W. Lee, N. D. Park, J. M. Lee, J. Choi, S. Lee et al., systems metabolic engineering of microorganisms for natural and non-natural chemicals, Nat Chem Biol [Internet], vol.8, issue.6, p.22596205, 2012.

J. Zeng, J. Teo, A. Banerjee, T. W. Chapman, J. Kim et al., A Synthetic Microbial Operational Amplifier, ACS Synth Biol [Internet], vol.7, issue.9, p.30152993, 2018.

L. Jingjing, L. Hyun-dong, and L. Long, Guocheng Du JC. Protein and metabolic engineering for the production of organic acids, Bioresour Technol, 2017.

, , vol.239, p.28538198

M. H. Hassoun, Fundamentals of Artificial Neural Networks. Proc IEEE, vol.84, p.906, 1996.

A. K. Jain, J. Mao, and K. M. Mohiuddin, Artificial neural networks: A tutorial. Computer (Long Beach Calif), vol.29, pp.31-44, 1996.

H. Kolanoski, Application of artificial neural networks in particle physics, Nucl Instruments Methods Phys Res Sect A, vol.367, pp.14-20, 1995.

G. Carleo and M. T. , Solving the quantum many-body problem with artificial neural networks. Science (80-), vol.355, pp.602-608, 2017.

P. Liu-zelin, . Changhui, . Wenhua, . Tian-dalun, and Z. M. Deng-xiangwen, Application of artificial neural networks in global climate change and ecological research: An overview, Chinese Sci Bull, vol.55, issue.34, pp.3853-63, 2010.

M. Pawul and M. ?liwka, Application of artificial neural networks for prediction of air pollution levels in environmental monitoring, J Ecol Eng, vol.17, issue.4, pp.190-196, 2016.

A. Gamal-el-din, W. Daniel, and M. Smith, The application of artificial neural network in wastewater treatment, J Environ Eng Sci, vol.3, pp.81-95, 2004.

S. M. Kamruzzaman, J. Sarkar, and A. M. , A new data mining scheme using artificial neural networks. Sensors, vol.11, p.22163866, 2011.

Å. Eide, R. Johansson, T. Lindblad, and C. S. Lindsey, Data mining and neural networks for knowledge discovery, Nucl Instruments Methods Phys Res Sect A Accel Spectrometers, Detect Assoc Equip, vol.389, issue.1-2, pp.251-255, 1997.

J. Schmidhuber, Deep Learning in neural networks: An overview, Neural Networks [Internet], vol.61, p.25462637, 2015.

K. Raman and N. Chandra, Flux balance analysis of biological systems: Applications and challenges, Brief Bioinform, vol.10, issue.4, p.19287049, 2009.

D. K. Allen, I. Libourel, and Y. Shachar-hill, Metabolic flux analysis in plants: Coping with complexity, Plant, Cell Environ, vol.32, issue.9, pp.1241-57, 2009.

E. Vasilakou, D. Machado, A. Theorell, I. Rocha, K. Nöh et al., Current state and challenges for dynamic metabolic modeling, Curr Opin Microbiol, vol.33, issue.1, pp.97-104, 2016.

J. M. Rohwer, Kinetic modelling of plant metabolic pathways, J Exp Bot, vol.63, issue.6, p.22419742, 2012.

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain, Psychol Rev, vol.65, issue.6, p.13602029, 1958.

K. Hornik, S. Maxwell, and H. White, Multilayer Feedforward Networks are Universal Approximators. Neural Networks, vol.2, pp.359-66, 1989.

W. N. Venables and B. D. Ripley, Modern Applied Statistics with S-Plus, 2002.

F. Günther and F. S. Neuralnet, Training of Neural Networks. R J, vol.2, issue.1, pp.30-38, 2010.

C. Bergmeir and J. M. Benitez, Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS, J Stat Softw, vol.46, issue.7, pp.1-26, 2012.

R. Gupta, P. Rathi, N. Gupta, and S. Bradoo, Lipase assays for conventional and molecular screening: an overview, Biotechnol Appl Biochem, vol.37, issue.1, p.63, 2003.

S. Schmeier, J. Hakenberg, E. Klipp, U. Leser, and A. Kowald, Finding Kinetic Parameters Using Text Mining. Omi A, J Integr Biol, vol.8, issue.2, pp.131-52, 2004.