Representing network reconstruction solutions with colored Petri nets

The reconstruction of biological networks from experimental time series data is one of the challenges in systems biology. Currently, most network reconstruction approaches usually yield one solution. In contrast, the (automatic) network reconstruction method proposed by Marwan et al. generates all possible minimal solutions fitting the given set of data, and thus reveals all alternative mechanisms to explain the biological phenomena under study. Although this is interesting and helpful, the generated solutions are usually too many and thus difficult to manage. In this paper, we propose the use of colored Petri nets to represent all possible solutions for a network reconstruction problem by encoding each solution as a color. Specifically, we present two folding (coloring) approaches for generating colored Petri net models for a given set of Petri net networks (solutions). To do this, we not only offer a compact representation of all solutions in one colored model for a given network reconstruction problem, but also facilitate the analysis of each solution by choosing its corresponding color. We also give an application of our coloring approaches by taking the phosphate regulatory network in enteric bacteria as example.

[1]  Ming Yang,et al.  An efficient method for unfolding colored Petri nets , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[2]  Ming Yang,et al.  Modelling and simulating reaction-diffusion systems using coloured Petri nets , 2014, Comput. Biol. Medicine.

[3]  Annegret Wagler,et al.  An algorithmic framework for network reconstruction , 2011, Theor. Comput. Sci..

[4]  Satoru Miyano,et al.  Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data of Bacillus Subtilis Using Differential Equations , 2002, Pacific Symposium on Biocomputing.

[5]  Stephen A. Cook,et al.  The complexity of theorem-proving procedures , 1971, STOC.

[6]  Jimmy Omony,et al.  Biological Network Inference: A Review of Methods and Assessment of Tools and Techniques , 2014 .

[7]  Annegret Wagler,et al.  A mathematical approach to solve the network reconstruction problem , 2008, Math. Methods Oper. Res..

[8]  Boudewijn F. van Dongen,et al.  Process Mining: Overview and Outlook of Petri Net Discovery Algorithms , 2009, Trans. Petri Nets Other Model. Concurr..

[9]  Lars Michael Kristensen,et al.  Design/CPN - A Computer Tool for Coloured Petri Nets , 1997 .

[10]  Wolfgang Marwan,et al.  Reconstructing the regulatory network controlling commitment and sporulation in Physarum polycephalum based on hierarchical Petri Net modelling and simulation. , 2005, Journal of theoretical biology.

[11]  Min Zou,et al.  A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data , 2005, Bioinform..

[12]  Tilak Agerwala,et al.  Comments on capabilities, limitations and “correctness” of Petri nets , 1973, ISCA 1973.

[13]  N. Thornberry,et al.  Inhibition of Human Caspases by Peptide-based and Macromolecular Inhibitors* , 1998, The Journal of Biological Chemistry.

[14]  Dominique Pontier,et al.  Modeling transmission of directly transmitted infectious diseases using colored stochastic Petri nets. , 2003, Mathematical biosciences.

[15]  Thomas Runge Application of Coloured Petri Nets in Systems Biology , 2004 .

[16]  Monika Heiner,et al.  Multiscale modelling of coupled Ca2+ channels using coloured stochastic Petri nets. , 2013, IET systems biology.

[17]  Monika Heiner,et al.  Colouring Space - A Coloured Framework for Spatial Modelling in Systems Biology , 2013, Petri Nets.

[18]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[19]  Sunwon Park,et al.  Colored Petri net modeling and simulation of signal transduction pathways. , 2006, Metabolic engineering.

[20]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[21]  Annegret Wagler,et al.  Reconstruction of extended Petri nets from time-series data by using logical control functions , 2013, Journal of mathematical biology.

[22]  Monika Heiner,et al.  Spatial-Temporal Modelling and Analysis of Bacterial Colonies with Phase Variable Genes , 2015, ACM Trans. Model. Comput. Simul..

[23]  Annegret Wagler,et al.  Automatic reconstruction of molecular and genetic networks from discrete time series data , 2008, Biosyst..

[24]  Kurt Jensen,et al.  Coloured Petri Nets and the Invariant-Method , 1981, Theor. Comput. Sci..

[25]  Kurt Lautenbach,et al.  System Modelling with High-Level Petri Nets , 1981, Theor. Comput. Sci..

[26]  Hartmann J. Genrich,et al.  Executable Petri net models for the analysis of metabolic pathways , 2001, International Journal on Software Tools for Technology Transfer.

[27]  Monika Heiner,et al.  Modeling membrane systems using colored stochastic Petri nets , 2013, Natural Computing.

[28]  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.

[29]  Monika Heiner,et al.  Petri Nets for Modeling and Analyzing Biochemical Reaction Networks , 2014, Approaches in Integrative Bioinformatics.

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

[31]  Marta Simeoni,et al.  Petri nets for modelling metabolic pathways: a survey , 2010, Natural Computing.

[32]  Michael Westergaard,et al.  CPN Tools for Editing, Simulating, and Analysing Coloured Petri Nets , 2003, ICATPN.

[33]  Monika Heiner,et al.  Multiscale Modeling and Analysis of Planar Cell Polarity in the Drosophila Wing , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[34]  Mor Peleg,et al.  Qualitative models of molecular function: linking genetic polymorphisms of tRNA to their functional sequelae , 2002, Proc. IEEE.

[35]  Rainer Spang,et al.  Inferring cellular networks – a review , 2007, BMC Bioinformatics.

[36]  A. Kupfer,et al.  Modelling and Simulation of the TLR4 Pathway with Coloured Petri Nets , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.