Predicting Phenotype from Genotype through Automatically Composed Petri Nets

We describe a modular modelling approach permitting curation, updating, and distributed development of modules through joined community effort overcoming the problem of keeping a combinatorially exploding number of monolithic models up to date. For this purpose, the effects of genes and their mutated alleles on downstream components are modeled by composable, metadata-containing Petri net models organized in a database with version control, accessible through a web interface (www.biomodelkit.org). Gene modules can be coupled to protein modules through mRNA modules by specific interfaces designed for the automatic, database-assisted composition. Automatically assembled executable models may then consider cell type-specific gene expression patterns and the resulting protein concentrations. Gene modules and allelic interference modules may represent effects of gene mutation and predict their pleiotropic consequences or uncover complex genotype/phenotype relationships. Forward and reverse engineered modules are fully compatible.

[1]  Annegret Wagler,et al.  Reconstruction of extended Petri nets from time series data and its application to signal transduction and to gene regulatory networks , 2011, BMC Systems Biology.

[2]  Wolfgang Marwan,et al.  A first glimpse at the transcriptome of Physarum polycephalum , 2008, BMC Genomics.

[3]  T. Ideker,et al.  Differential network biology , 2012, Molecular systems biology.

[4]  D R Westhead,et al.  Petri Net representations in systems biology. , 2003, Biochemical Society transactions.

[5]  Monika Heiner,et al.  Petri Nets for Systems and Synthetic Biology , 2008, SFM.

[6]  H. Cerutti,et al.  On the origin and functions of RNA-mediated silencing: from protists to man , 2006, Current Genetics.

[7]  M. Selbach,et al.  Global quantification of mammalian gene expression control , 2011, Nature.

[8]  Daniel Herschlag,et al.  Genome-wide identification of mRNAs associated with the translational regulator PUMILIO in Drosophila melanogaster. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Wolfgang Marwan,et al.  Isolation of Physarum polycephalum plasmodial mutants altered in sporulation by chemical mutagenesis of flagellates , 2005 .

[10]  W. Doolittle,et al.  A kingdom-level phylogeny of eukaryotes based on combined protein data. , 2000, Science.

[11]  Muffy Calder,et al.  When kinases meet mathematics: the systems biology of MAPK signalling , 2005, FEBS letters.

[12]  N. D. Clarke,et al.  A genome-wide RNAi screen reveals determinants of human embryonic stem cell identity , 2010, Nature.

[13]  Muffy Calder,et al.  The Mammalian MAPK/ERK Pathway Exhibits Properties of a Negative Feedback Amplifier , 2010, Science Signaling.

[14]  Wolfgang Marwan,et al.  Transcriptomic changes arising during light-induced sporulation in Physarum polycephalum , 2010, BMC Genomics.

[15]  Monika Heiner,et al.  JAK/STAT signalling--an executable model assembled from molecule-centred modules demonstrating a module-oriented database concept for systems and synthetic biology. , 2012, Molecular bioSystems.

[16]  F. Neidhardt,et al.  Physiology of the bacterial cell : a molecular approach , 1990 .

[17]  Monika Heiner,et al.  Extended Stochastic Petri Nets for Model-Based Design of Wetlab Experiments , 2009, Trans. Comp. Sys. Biology.

[18]  Monika Heiner,et al.  Snoopy - a unifying Petri net framework to investigate biomolecular networks , 2010, Bioinform..

[19]  Tiziana Bonaldi,et al.  Systems biology "on-the-fly": SILAC-based quantitative proteomics and RNAi approach in Drosophila melanogaster. , 2010, Methods in molecular biology.

[20]  Torsten Schaub,et al.  Automatic network reconstruction using ASP , 2011, Theory and Practice of Logic Programming.

[21]  Stefan Wiemann,et al.  Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer , 2012, Molecular systems biology.

[22]  T. Henzinger,et al.  Executable cell biology , 2007, Nature Biotechnology.

[23]  T G Burland,et al.  Patterns of inheritance, development and the mitotic cycle in the protist Physarum polycephalum. , 1993, Advances in microbial physiology.

[24]  David R. Gilbert,et al.  Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway , 2009, BMC Systems Biology.

[25]  Wolfgang Marwan,et al.  Pain Signaling-A Case Study of the Modular Petri Net Modeling Concept with Prospect to a Protein-Oriented Modeling Platform , 2011 .

[26]  Michael Hecker,et al.  Gene regulatory network inference: Data integration in dynamic models - A review , 2009, Biosyst..

[27]  W. Doolittle,et al.  Origin and evolution of the slime molds (Mycetozoa) , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[28]  David M. Sabatini,et al.  Building mammalian signalling pathways with RNAi screens , 2006, Nature Reviews Molecular Cell Biology.

[29]  Yi-Ju Hsieh,et al.  Global regulation by the seven-component Pi signaling system. , 2010, Current opinion in microbiology.

[30]  Doktor der Naturwissenschaften Colored Petri Nets for Systems Biology , 2012 .

[31]  Monika Heiner,et al.  Petri nets in Snoopy: a unifying framework for the graphical display, computational modelling, and simulation of bacterial regulatory networks. , 2012, Methods in molecular biology.

[32]  W. Marwan,et al.  Futile attempts to differentiate provide molecular evidence for individual differences within a population of cells during cellular reprogramming , 2012, FEMS microbiology letters.

[33]  Martin Schwarick,et al.  Snoopy - A Unifying Petri Net Tool , 2012, Petri Nets.