Multi-level and hybrid modelling approaches for systems biology

Palsson, 2015 Macklin, 2014, The future of whole-cell modeling, Curr Opin Biotechnol, 28, 111, 10.1016/j.copbio.2014.01.012 Novikoff, 1945, The concept of integrative levels and biology, Science, 101, 209, 10.1126/science.101.2618.209 Pardini, 2011 Wilkinson, 2011 Alberts, 2002 Regev, 2004, Bioambients: an abstraction for biological compartments, Theor Comput Sci, 325, 141, 10.1016/j.tcs.2004.03.061 Snyder, 1986, Encapsulation and inheritance in object-oriented programming languages, vol. 21, 38 2011, Epigenetics: linking genotype and phenotype in development and evolution Oyama, 2000 Uhrmacher, 2005, 66 Gil-Quijano, 2012, 620 Stephanou, 2016, Hybrid modelling in biology: a classification review, Math Model Nat Phenom, 449, 37, 10.1051/mmnp/201611103 Machado, 2011, Modeling formalisms in systems biology, AMB Express, 1, 45, 10.1186/2191-0855-1-45 Jasmin, 2007, Executable cell biology, Nat Biotech, 25, 1239 Cull, 2005 Jones, 2009 Frank, 2001, Stationary solutions of linear stochastic delay differential equations: applications to biological systems, Phys Rev E, 64, 021917, 10.1103/PhysRevE.64.021917 Loewe, 2008, Computational models in systems biology, Genome Biol, 9, 328, 10.1186/gb-2008-9-12-328 Davidsen, 2016, Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective, J Appl Physiol, 120, 297, 10.1152/japplphysiol.01110.2014 Asai, 2011, An open platform toward large-scale multilevel modeling and simulation of physiological systems, 250 Mosca, 2010, A multilevel data integration resource for breast cancer study, BMC Syst Biol, 4, 76, 10.1186/1752-0509-4-76 Rajasundaram, 2016, More effort-more results: recent advances in integrative ‘omics' data analysis, Curr Opin Plant Biol, 30, 57, 10.1016/j.pbi.2015.12.010 Bulik, 2009, Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws - a promising method for speeding up the kinetic modelling of complex metabolic networks, FEBS J, 276, 410, 10.1111/j.1742-4658.2008.06784.x Leonelli, 2012, Introduction: making sense of data-driven research in the biological and biomedical sciences, Stud Hist Philos Sci Part C Stud Hist Phil Biol Biomed Sci, 43, 1, 10.1016/j.shpsc.2011.10.001 Cox, 2011, Quantitative, high-resolution proteomics for data-driven systems biology, Annu Rev Biochem, 80, 273, 10.1146/annurev-biochem-061308-093216 Duncan, 1998, Context, composition and heterogeneity: using multilevel models in health research, Soc Sci Med, 46, 97, 10.1016/S0277-9536(97)00148-2 Uhrmacher, 2007, Combining micro and macro-modeling in devs for computational biology, 871 Maus, 2011, Rule-based multi-level modeling of cell biological systems, BMC Syst Biol, 5, 166, 10.1186/1752-0509-5-166 Vangheluwe, 2000, Devs as a common denominator for multi-formalism hybrid systems modelling, 129 Lachowicz, 2005, Micro and meso scales of description corresponding to a model of tissue invasion by solid tumours, Math Models Methods Appl Sci, 15, 1667, 10.1142/S0218202505000935 Delsanto, 2008, A multilevel approach to cancer growth modeling, J Theor Biol, 250, 16, 10.1016/j.jtbi.2007.09.023 Asai, 2014, A versatile platform for multilevel modeling of physiological systems: Sbml-phml hybrid modeling and simulation, Adv Biomed Eng, 3, 50, 10.14326/abe.3.50 Hucka, 2003, The systems biology markup language (sbml): a medium for representation and exchange of biochemical network models, Bioinformatics, 19, 524, 10.1093/bioinformatics/btg015 Asai, 2008, Specifications of insilicoml 1.0: a multilevel biophysical model description language, J Physiol Sci, 58, 447, 10.2170/physiolsci.RP013308 Holzhütter, 2012, The virtual liver: a multidisciplinary, multilevel challenge for systems biology, Wiley Interdiscip Rev Syst Biol Med, 4, 221, 10.1002/wsbm.1158 Zhao, 2011, Applications of physiologically based pharmacokinetic (pbpk) modeling and simulation during regulatory review, Clin Pharmacol Ther, 89, 259, 10.1038/clpt.2010.298 Godfrey, 1987, Identifiability of model parameters, 1 Moles, 2003, Parameter estimation in biochemical pathways: a comparison of global optimization methods, Genome Res, 13, 2467, 10.1101/gr.1262503 Spiegelhalter, 2002, Bayesian measures of model complexity and fit, J R Stat Soc Ser B (Stat Methodol), 64, 583, 10.1111/1467-9868.00353 Liepe, 2014, A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation, Nat Protocols, 9, 439, 10.1038/nprot.2014.025 Sunnåker, 2013, Approximate Bayesian computation, PLoS Comput Biol, 9, e1002803, 10.1371/journal.pcbi.1002803 Bois, 2009, Gnu mcsim: Bayesian statistical inference for sbml-coded ystems biology models, Bioinformatics, 25, 1453, 10.1093/bioinformatics/btp162 Gilks, 1995 von Stosch, 2010, Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach, BMC Syst Biol, 4, 131, 10.1186/1752-0509-4-131 Donzé, 2010, 167 Raman, 2014, Model predictive control with signal temporal logic specifications, 81 Gutenkunst, 2007, Universally sloppy parameter sensitivities in systems biology models, PLOS Comput Biol, 3, 1, 10.1371/journal.pcbi.0030189 Matthews, 2008, Reactome knowledge base of human biological pathways and processes, Nucleic Acids Res, 37, D619 Daghir-Wojtkowiak, 2017, Multilevel pharmacokinetics-driven modeling of metabolomics data, Metabolomics, 13, 31, 10.1007/s11306-017-1164-4 Carson, 2002, Model verification and validation, vol. 1, 52 Tokishi, 2013, Evaluation and improvement of consistency of hybrid and multi-resolution traffic simulation models Behrmann, 2001, UPPAAL - present and future Xie, 2010, Improving marginal likelihood estimation for Bayesian phylogenetic model selection, Syst Biol, 60, 150, 10.1093/sysbio/syq085 Duchêne, 2016, Cross-validation to select Bayesian hierarchical models in phylogenetics, BMC Evol Biol, 16, 115, 10.1186/s12862-016-0688-y Lartillot, 2009, Phylobayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating, Bioinformatics, 25, 2286, 10.1093/bioinformatics/btp368 Box, 1976, Science and statistics, J Am Stat Assoc, 71, 791, 10.1080/01621459.1976.10480949 Kirk, 2013, Model selection in systems and synthetic biology, Curr Opin Biotechnol, 24, 767, 10.1016/j.copbio.2013.03.012 Jin, 2000, Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement, IEEE Trans Fuzzy Syst, 8, 212, 10.1109/91.842154 Surovtsova, 2006, Approaches to complexity reduction in a systems biology research environment (Sycamore), 1683 Machné, 2006, The sbml ode solver library: a native api for symbolic and fast numerical analysis of reaction networks, Bioinformatics, 22, 1406, 10.1093/bioinformatics/btl086 Steyerberg, 2010, Assessing the performance of prediction models: a framework for some traditional and novel measures, Epidemiology (Cambridge, Mass), 21, 128, 10.1097/EDE.0b013e3181c30fb2 Sober, 1981, The principle of parsimony, Br J Philos Sci, 32, 145, 10.1093/bjps/32.2.145 Raykov, 1999, On desirability of parsimony in structural equation model selection, Struct Equ Model Multidiscip J, 6, 292, 10.1080/10705519909540135 Houy, 2012, Understanding understandability of conceptual models-what are we actually talking about?, Concept Model, 64, 10.1007/978-3-642-34002-4_5 Donatelli, 2008, A design for framework-independent model components of biophysical systems Bichindaritz, 2006, Memoire: A framework for semantic interoperability of case-based reasoning systems in biology and medicine, Artif Intell Med, 36, 177, 10.1016/j.artmed.2005.10.009 Waltemath, 2016, Toward community standards and software for whole-cell modeling, IEEE Trans Biomed Eng, 63, 2007, 10.1109/TBME.2016.2560762 Waltemath, 2016, Toward community standards and software for whole-cell modeling, IEEE Trans Biomed Eng, 63, 2007, 10.1109/TBME.2016.2560762