Advances in flux balance analysis by integrating machine learning and mechanism-based models
Tài liệu tham khảo
Kim, 2017, Current state and applications of microbial genome-scale metabolic models, Curr Opin Syst Biol, 2, 10, 10.1016/j.coisb.2017.03.001
Price, 2004, Genome-scale models of microbial cells: evaluating the consequences of constraints, Nat Rev Microbiol, 2, 886, 10.1038/nrmicro1023
Fang, 2020, Reconstructing organisms in silico: genome-scale models and their emerging applications, Nat Rev Microbiol, 18, 731, 10.1038/s41579-020-00440-4
Geng, 2017, In silico analysis of human metabolism: Reconstruction, contextualization and application of genome-scale models, Curr Opin Syst Biol, 2, 29, 10.1016/j.coisb.2017.01.001
Ryu, 2015, Reconstruction of genome-scale human metabolic models using omics data, Integr Biol, 7, 859, 10.1039/c5ib00002e
de Oliveira Dal’Molin CG, Nielsen LK. Plant genome-scale metabolic reconstruction and modelling. Curr Opin Biotechnol 2013;24:271–7.
Collakova, 2012, Are we ready for genome-scale modeling in plants?, Plant Sci, 191, 53, 10.1016/j.plantsci.2012.04.010
de Oliveira Dal’Molin CG, Nielsen LK. Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses. Curr Opin Biotechnol 2018;49:42–8.
Sweetlove LJ, Ratcliffe RG. Flux-balance modeling of plant metabolism. Front Plant Sci 2011;2:38–38.
Töpfer, 2021, Environment-coupled models of leaf metabolism, Biochem Soc Trans, 49, 119, 10.1042/BST20200059
Jensen, 2018
Xu, 2018, Genome-scale biological models for industrial microbial systems, Appl Microbiol Biotechnol, 102, 3439, 10.1007/s00253-018-8803-1
Cook DJ, Nielsen J. Genome‐scale metabolic models applied to human health and disease. Wiley Interdiscip Rev Syst Biol Med 2017;9:e1393–e1393.
Nilsson, 2017, Genome scale metabolic modeling of cancer, Metab Eng, 43, 103, 10.1016/j.ymben.2016.10.022
Li, 2020, Applications of genome editing technology in the targeted therapy of human diseases: mechanisms, advances and prospects, Signal Transduct Target Ther, 5, 1, 10.1038/s41392-019-0089-y
Shameer, 2018, Computational analysis of the productivity potential of CAM, Nat Plants, 4, 165, 10.1038/s41477-018-0112-2
Shameer, 2020, Flux balance analysis of metabolism during growth by osmotic cell expansion and its application to tomato fruits, Plant J, 103, 10.1111/tpj.14707
Töpfer, 2020, Alternative Crassulacean Acid Metabolism Modes Provide Environment-Specific Water-Saving Benefits in a Leaf Metabolic Model, Plant Cell, 32, 3689, 10.1105/tpc.20.00132
Bauer E, Thiele I. From network analysis to functional metabolic modeling of the human gut microbiota. MSystems 2018;3.
Sen P, Orešič M. Metabolic modeling of human gut microbiota on a genome scale: an overview. Metabolites 2019;9:22–22.
van der Ark, 2017, More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes, Microbiome, 5, 1, 10.1186/s40168-017-0299-x
Magnúsdóttir, 2017, Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota, Nat Biotechnol, 35, 81, 10.1038/nbt.3703
Cho, 2019, Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring, Curr Opin Syst Biol, 15, 1, 10.1016/j.coisb.2019.02.009
Dunphy, 2018, Biomedical applications of genome-scale metabolic network reconstructions of human pathogens, Curr Opin Biotechnol, 51, 70, 10.1016/j.copbio.2017.11.014
Moreira, 2019, A genome-scale metabolic model of soybean (Glycine max) highlights metabolic fluxes in seedlings, Plant Physiol, 180, 1912, 10.1104/pp.19.00122
Shaw, 2019, A mass and charge balanced metabolic model of Setaria viridis revealed mechanisms of proton balancing in C4 plants, BMC Bioinf, 20, 1, 10.1186/s12859-019-2941-z
Scheunemann, 2018, Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models, Sci Rep, 8, 1, 10.1038/s41598-018-26232-8
Grafahrend-Belau, 2013, Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale, Plant Physiol, 163, 637, 10.1104/pp.113.224006
Mintz-Oron, 2012, Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity, Proc Natl Acad Sci, 109, 339, 10.1073/pnas.1100358109
Gomes de Oliveira Dal’Molin C, Quek L-E, Saa PA, Nielsen LK. A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. Front Plant Sci 2015;6:4–4.
Shaw R, Cheung CY. A dynamic multi-tissue flux balance model captures carbon and nitrogen metabolism and optimal resource partitioning during Arabidopsis growth. Front Plant Sci 2018;9:884–884.
Orth, 2010, What is flux balance analysis?, Nat Biotechnol, 28, 245, 10.1038/nbt.1614
Feist, 2010, The biomass objective function, Curr Opin Microbiol, 13, 344, 10.1016/j.mib.2010.03.003
Antoniewicz, 2015, Methods and advances in metabolic flux analysis: a mini-review, J Ind Microbiol Biotechnol, 42, 317, 10.1007/s10295-015-1585-x
Nikoloski, 2015, Inference and prediction of metabolic network fluxes, Plant Physiol, 169, 1443
Dai, 2017, Understanding metabolism with flux analysis: From theory to application, Metab Eng, 43, 94, 10.1016/j.ymben.2016.09.005
Cheung, 2014, A diel flux balance model captures interactions between light and dark metabolism during day-night cycles in C3 and crassulacean acid metabolism leaves, Plant Physiol, 165, 917, 10.1104/pp.113.234468
Mahadevan, 2002, Dynamic flux balance analysis of diauxic growth in Escherichia coli, Biophys J, 83, 1331, 10.1016/S0006-3495(02)73903-9
Imam S, Schäuble S, Brooks AN, Baliga NS, Price ND. Data-driven integration of genome-scale regulatory and metabolic network models. Front Microbiol 2015;6:409–409.
Cruz, 2020, A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation, Biochem Soc Trans, 48, 1889, 10.1042/BST20190840
Blazier AS, Papin JA. Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 2012;3:299–299.
Kim, 2014, Methods for integration of transcriptomic data in genome-scale metabolic models, Comput Struct Biotechnol J, 11, 59, 10.1016/j.csbj.2014.08.009
Robaina Estévez S, Nikoloski Z. Generalized framework for context-specific metabolic model extraction methods. Front Plant Sci 2014;5:491–491.
Töpfer N, Kleessen S, Nikoloski Z. Integration of metabolomics data into metabolic networks. Front Plant Sci 2015;6:49–49.
Töpfer, 2018, 297
Sánchez, 2017, Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints, Mol Syst Biol, 13, 935, 10.15252/msb.20167411
Beg, 2007, Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity, Proc Natl Acad Sci, 104, 12663, 10.1073/pnas.0609845104
Adadi, 2012, Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters, PLoS Comput Biol, 8, 10.1371/journal.pcbi.1002575
Goelzer, 2011, Cell design in bacteria as a convex optimization problem, Automatica, 47, 1210, 10.1016/j.automatica.2011.02.038
Goelzer, 2017, Resource allocation in living organisms, Biochem Soc Trans, 45, 945, 10.1042/BST20160436
O’brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BØ. Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 2013;9:693–693.
Nilsson, 2017, Metabolic models of protein allocation call for the kinetome, Cell Syst, 5, 538, 10.1016/j.cels.2017.11.013
Rana, 2020, Recent advances on constraint-based models by integrating machine learning, Curr Opin Biotechnol, 64, 85, 10.1016/j.copbio.2019.11.007
Xu C, Jackson SA. Machine learning and complex biological data 2019.
Jordan, 2015, Machine learning: Trends, perspectives, and prospects, Science, 349, 255, 10.1126/science.aaa8415
Gilpin, 2020, Learning dynamics from large biological datasets: machine learning meets systems biology. Curr Opin, Syst Biol
Pasolli E, Truong DT, Malik F, Waldron L, Segata N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput Biol 2016;12:e1004977–e1004977.
Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019;15:e1007084–e1007084.
Bhadra, 2018, Principal metabolic flux mode analysis, Bioinformatics, 34, 2409, 10.1093/bioinformatics/bty049
Kim, 2016, Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli, Nat Commun, 7, 1, 10.1038/ncomms13090
Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE, Palsson BO. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep 2017;7:46249–46249.
Lever, 2017, Points of significance: Principal component analysis, Nat Methods, 14, 641, 10.1038/nmeth.4346
Saxena, 2017, A review of clustering techniques and developments, Neurocomputing, 267, 664, 10.1016/j.neucom.2017.06.053
Wang, 2017, Research and implementation of SVD in machine learning, IEEE, 471
Garcia-Dias R, Vieira S, Pinaya WHL, Mechelli A. Clustering analysis. Mach. Learn., Elsevier; 2020, p. 227–47.
Sánchez, 2019, SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework, BMC Syst Biol, 13, 1, 10.1186/s12918-018-0673-8
Dai D, Horvath N, Varner J. Dynamic sequence specific constraint-based modeling of cell-free protein synthesis. Processes 2018;6:132–132.
Patané, 2019, Multi-objective optimization of genome-scale metabolic models: the case of ethanol production, Ann Oper Res, 276, 211, 10.1007/s10479-018-2865-4
Singh, 2016, 1310
Montgomery, 2021
Ogutu, 2012, Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions, vol. 6, 1
Navada, 2011, Overview of use of decision tree algorithms in machine learning, IEEE, 37
Van Gerven, 2017, Artificial neural networks as models of neural information processing, Front Comput Neurosci, 11, 114, 10.3389/fncom.2017.00114
Huang, 2018, Applications of support vector machine (SVM) learning in cancer genomics, Cancer Genomics Proteomics, 15, 41
Grossberg, 2013, Recurrent neural networks, Scholarpedia, 8, 1888, 10.4249/scholarpedia.1888
Dong, 2020, A survey on ensemble learning, Front Comput Sci, 14, 241, 10.1007/s11704-019-8208-z
Carrera J, Estrela R, Luo J, Rai N, Tsoukalas A, Tagkopoulos I. An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of E scherichia coli. Mol Syst Biol 2014;10:735–735.
Lerman, 2012, In silico method for modelling metabolism and gene product expression at genome scale, Nat Commun, 3, 1, 10.1038/ncomms1928
O’brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BØ. Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 2013;9:693.
Occhipinti A, Eyassu F, Rahman TJ, Rahman PKSM, Angione C. In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production. PeerJ 2018;6:e6046–e6046.
Shaked, 2016, Metabolic network prediction of drug side effects, Cell Syst, 2, 209, 10.1016/j.cels.2016.03.001
DiMucci, 2018, Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks, Msystems, 3, 10.1128/mSystems.00181-18
Jalili, 2021, Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models, J Pers Med, 11, 496, 10.3390/jpm11060496
Yaneske, 2018, The poly-omics of ageing through individual-based metabolic modelling, BMC Bioinf, 19, 83, 10.1186/s12859-018-2383-z
Vijayakumar, 2020, A hybrid flux balance analysis and machine learning pipeline elucidates metabolic adaptation in cyanobacteria, Iscience, 23, 10.1016/j.isci.2020.101818
Culley, 2020, A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth, Proc Natl Acad Sci, 117, 18869, 10.1073/pnas.2002959117
Magazzù, 2021, Multimodal regularised linear models with flux balance analysis for mechanistic integration of omics data, Bioinformatics, 10.1093/bioinformatics/btab324
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Sen, 2020, Deep learning meets metabolomics: A methodological perspective, Brief Bioinform
Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol 2016;12:878–878.
Liu, 2017, A survey of deep neural network architectures and their applications, Neurocomputing, 234, 11, 10.1016/j.neucom.2016.12.038
Yasenko, 2020, 351
Banerjee, 2020, 1249
Guo, 2017, DeepMetabolism: a deep learning system to predict phenotype from genome sequencing, ArXiv Prepr ArXiv170503094
Barsacchi M, Terre HA, Lió P. GEESE: Metabolically driven latent space learning for gene expression data. BioRxiv 2018:365643–365643.
Radhakrishnan M, Edwards S, Doyle FJ. Dynamic flux balance analysis of diauxic growth in Eschericha coli. Biophys J 2002;83:3–3.
Rügen, 2015, Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA, Sci Rep, 5, 1, 10.1038/srep15247
Kim OD, Rocha M, Maia P. A review of dynamic modeling approaches and their application in computational strain optimization for metabolic engineering. Front Microbiol 2018;9:1690–1690.
Popp, 2020, μbialSim: constraint-based dynamic simulation of complex microbiomes. Front Bioeng, Biotechnol, 8
Perez-Garcia O, Lear G, Singhal N. Metabolic network modeling of microbial interactions in natural and engineered environmental systems. Front Microbiol 2016;7:673–673.
Bosi E, Bacci G, Mengoni A, Fondi M. Perspectives and challenges in microbial communities metabolic modeling. Front Genet 2017;8:88–88.
Kostewicz, 2014, PBPK models for the prediction of in vivo performance of oral dosage forms, Eur J Pharm Sci, 57, 300, 10.1016/j.ejps.2013.09.008
Sager, 2015, Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification, Drug Metab Dispos, 43, 1823, 10.1124/dmd.115.065920
Zhao, 2011, Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review, Clin Pharmacol Ther, 89, 259, 10.1038/clpt.2010.298
Wu, 2016, MUFINS: multi-formalism interaction network simulator, NPJ Syst Biol Appl, 2, 1, 10.1038/npjsba.2016.32
Krauss M, Schaller S, Borchers S, Findeisen R, Lippert J, Kuepfer L. Integrating cellular metabolism into a multiscale whole-body model. PLoS Comput Biol 2012;8:e1002750–e1002750.
Toroghi, 2016, Multiscale metabolic modeling approach for predicting blood alcohol concentration, IEEE Life Sci Lett, 2, 59, 10.1109/LLS.2016.2644647
Wadehn F, Schaller S, Eissing T, Krauss M, Küpfer L. A multiscale, model-based analysis of the multi-tissue interplay underlying blood glucose regulation in type I diabetes. 2016 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC, IEEE; 2016, p. 1417–21
Toroghi, 2016, A multi-scale model of the whole human body based on dynamic parsimonious flux balance analysis, IFAC-Pap, 49, 937
Sier, 2017, Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology, BMC Syst Biol, 11, 1, 10.1186/s12918-017-0520-3
Cordes, 2018, Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation, NPJ Syst Biol Appl, 4, 1, 10.1038/s41540-018-0048-1
Guebila, 2015, Systems pharmacology of levodopa absorption, Adv Syst Synth Biol
Guebila, 2016, Model-based dietary optimization for late-stage, levodopa-treated, Parkinson’s disease patients, NPJ Syst Biol Appl, 2, 1, 10.1038/npjsba.2016.13
Shepelyuk, 2016, Computational modeling of quiescent platelet energy metabolism in the context of whole-body glucose turnover, Math Model Nat Phenom, 11, 91, 10.1051/mmnp/201611606
Maldonado, 2017, Integration of genome scale metabolic networks and gene regulation of metabolic enzymes with physiologically based pharmacokinetics, CPT Pharmacomet Syst Pharmacol, 6, 732, 10.1002/psp4.12230
Øyås, 2018, Genome-scale metabolic networks in time and space, Curr Opin Syst Biol, 8, 51, 10.1016/j.coisb.2017.12.003
Martins Conde P do R, Sauter T, Pfau T. Constraint based modeling going multicellular. Front Mol Biosci 2016;3:3–3.
Thiele, 2017, Quantitative systems pharmacology and the personalized drug–microbiota–diet axis, Curr Opin Syst Biol, 4, 43, 10.1016/j.coisb.2017.06.001
Nilsson, 2020, Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis, Proc Natl Acad Sci, 117, 10294, 10.1073/pnas.1919250117
Henson, 2014, Dynamic flux balance analysis for synthetic microbial communities, IET Syst Biol, 8, 214, 10.1049/iet-syb.2013.0021
Gottstein, 2016, Constraint-based stoichiometric modelling from single organisms to microbial communities, J R Soc Interface, 13, 10.1098/rsif.2016.0627
Zomorrodi, 2014, d-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities, ACS Synth Biol, 3, 247, 10.1021/sb4001307
Zhuang K, Yang L, Cluett WR, Mahadevan R. Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. BMC Biotechnol 2013;13:8–8. 10.1186/1472-6750-13-8.
Chen J, Gomez JA, Höffner K, Phalak P, Barton PI, Henson MA. Spatiotemporal modeling of microbial metabolism. BMC Syst Biol 2016;10:21–21. 10.1186/s12918-016-0259-2.
Harcombe, 2014, Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics, Cell Rep, 7, 1104, 10.1016/j.celrep.2014.03.070
Phalak, 2016, Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species, BMC Syst Biol, 10, 1, 10.1186/s12918-016-0334-8
Lee JM, Gianchandani EP, Eddy JA, Papin JA. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol 2008;4:e1000086–e1000086.
Covert, 2008, Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli, Bioinformatics, 24, 2044, 10.1093/bioinformatics/btn352
Mueller J, Eschenroeder A, Christen O, Junker B, Schreiber F. ProNet-CN model: a dynamic and multi-scale process network combining photosynthesis, primary carbon metabolism and effects of leaf nitrogen status. 2012 IEEE 4th Int. Symp. Plant Growth Model. Simul. Vis. Appl., IEEE; 2012, p. 289–96.
Kang MZ, Dumont Y, Guo Y. Plant growth modeling, simulation, visualization and applications. Proceedings PMA12: The Fourth International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Shanghai, China, 31 October-3 November 2012 2012.
Von Caemmerer, 2000, Biochemical models of leaf photosynthesis, Csiro publishing
de Oliveira Dal’Molin CG, Quek L-E, Palfreyman RW, Brumbley SM, Nielsen LK. C4GEM, a genome-scale metabolic model to study C4 plant metabolism. Plant Physiol 2010;154:1871–85.
Mallmann J, Heckmann D, Bräutigam A, Lercher MJ, Weber APM, Westhoff P, et al. The role of photorespiration during the evolution of C4 photosynthesis in the genus Flaveria. Elife 2014;3:e02478–e02478.
Petri, 1962, kommunikation mit automaten, PhD Univ Bonn West Ger
Matsuno H, Doi A, Nagasaki M, Miyano S. Hybrid Petri net representation of gene regulatory network. Biocomput. 2000, World Scientific; 1999, p. 341–52.
Sackmann A, Heiner M, Koch I. Application of Petri net based analysis techniques to signal transduction pathways. BMC Bioinformatics 2006;7:482–482.
Koch, 2005, Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber, Bioinformatics, 21, 1219, 10.1093/bioinformatics/bti145
Murata, 1989, Petri nets: properties, analysis and applications, Proceed IEEE, 77, 541, 10.1109/5.24143
Heiner, 2004, 216
Baldan, 2010, Petri nets for modelling metabolic pathways: a survey, Nat Comput, 9, 955, 10.1007/s11047-010-9180-6
Koch I, Nöthen J, Schleiff E. Modeling the metabolism of Arabidopsis thaliana: Application of network decomposition and network reduction in the context of Petri nets. Front Genet 2017;8:85–85.
Smallbone, 2013, Large-scale metabolic models: From reconstruction to differential equations, Ind Biotechnol, 9, 179, 10.1089/ind.2013.0003
Fisher, 2013, QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells, Bioinformatics, 29, 3181, 10.1093/bioinformatics/btt552
Gille C, Bölling C, Hoppe A, Bulik S, Hoffmann S, Hübner K, et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol 2010;6:411–411.
Gevorgyan, 2011, SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks, Bioinformatics, 27, 433, 10.1093/bioinformatics/btq679
Ptak, 2016, 113
Simone, 2020, Integrating Petri Nets and Flux Balance Methods in Computational Biology Models: a Methodological and Computational Practice, Fundam Informaticae, 171, 367, 10.3233/FI-2020-1888
Amparore, 2014, 354
Gilbert D, Heiner M. From Petri Nets to Differential Equations – An Integrative Approach for Biochemical Network Analysis BT - Petri Nets and Other Models of Concurrency - ICATPN 2006. In: Donatelli S, Thiagarajan PS, editors., Berlin, Heidelberg: Springer Berlin Heidelberg; 2006, p. 181–200.
Roy M, Finley SD. Computational model predicts the effects of targeting cellular metabolism in pancreatic cancer. Front Physiol 2017;8:217–217.
Palsson, 2015
Self, 2018, Derivation of a biomass proxy for dynamic analysis of whole genome metabolic models, Int. Conf. Comput. Methods Syst. Biol., 39, 10.1007/978-3-319-99429-1_3
Orth, 2010, Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide, EcoSal Plus, 10.1128/ecosalplus.10.2.1
Rohr, 2018, Discrete-time leap method for stochastic simulation, Fundam Informaticae, 160, 181, 10.3233/FI-2018-1680
Kursa, 2010, Feature selection with the Boruta package, J Stat Softw, 36, 1, 10.18637/jss.v036.i11
Heinken, 2021, Advances in constraint-based modelling of microbial communities. Curr Opin, Syst Biol
Júlvez, 2018, Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease, NPJ Syst Biol Appl, 4, 1, 10.1038/s41540-017-0044-x
Júlvez, 2019, Flexible Nets: a modeling formalism for dynamic systems with uncertain parameters, Discrete Event Dyn Syst, 29, 367, 10.1007/s10626-019-00287-9
Júlvez, 2020, A unifying modelling formalism for the integration of stoichiometric and kinetic models, J R Soc Interface, 17, 20200341, 10.1098/rsif.2020.0341
Wilkinson, 2016, The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, 1, 10.1038/sdata.2016.18
Zhang, 2020, Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2, J Integr Bioinforma, 17, 10.1515/jib-2020-0015
Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, et al. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020;16:e9110–e9110.
Tefagh, 2020, SWIFTCORE: a tool for the context-specific reconstruction of genome-scale metabolic networks, BMC Bioinf, 21, 1, 10.1186/s12859-020-3440-y
Willmann, 2012, Integration of dissolution into physiologically-based pharmacokinetic models III: PK-Sim®, J Pharm Pharmacol, 64, 997, 10.1111/j.2042-7158.2012.01534.x
Eissing, 2011, A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks, Front Physiol, 2, 4, 10.3389/fphys.2011.00004
Heiner, 2012, 398
Heiner, 2015, Charlie–an extensible Petri net analysis tool, Springer, 200
Heiner, 2013, MARCIE–model checking and reachability analysis done efficiently, Springer, 389
Blätke MA, Heiner M, Marwan W. Chapter 7 - BioModel Engineering with Petri Nets. In: Robeva RS, editor. Algebr. Discrete Math. Methods Mod. Biol., Boston: Academic Press; 2015, p. 141–92. 10.1016/B978-0-12-801213-0.00007-1.
Blätke MA, Rohr C, Heiner M, Marwan W. A Petri-Net-Based Framework for Biomodel Engineering. In: Benner P, Findeisen R, Flockerzi D, Reichl U, Sundmacher K, editors. Large-Scale Netw. Eng. Life Sci., Cham: Springer International Publishing; 2014, p. 317–66. 10.1007/978-3-319-08437-4_6.