Chromar, a Rule-based Language of Parameterised Objects

Abstract Modelling in biology becomes necessary when systems are complex but the more complex the systems are the harder the models become to read. The most common ways of writing models are by writing reactions on discrete, typed objects (e.g. molecules of different species), or writing rate equations for the populations of such species. One problem (1) with those approaches is that the number of species and reactions is often so large that the model cannot be realistically enumerated. Another problem (2) is that the number of species and reactions is fixed, whereas biology often grows new compartments which means new reactions and species. Here we develop an extension to the representation of reactions where the objects carry variables that are defined by their type (for example objects of type Leaf all have a Mass variable). The dynamics are defined by rules about types, which means they work for all objects of that type. This compact representation solves problem 1. If we think of the object variables as the analogue of reaction/rate equation species, creating a new object of some type means we are also creating new species (solving problem 2). We also developed an embedding of Chromar in the programming language Haskell and showed its applicability to two examples. Having a more compact representation can help make models a tool for knowledge representation and exchange instead of just a simulation input. Embedding Chromar in a general purpose programming language lifts some of the constraints of modelling languages while still maintaining the naturalness of a domain-specific language.

[1]  Dan Suciu,et al.  Comprehension syntax , 1994, SGMD.

[2]  Devendra Singh,et al.  AN OVERVIEW OF THE APPLICATIONS OF MULTISETS 1 , 2007 .

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

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

[5]  Limsoon Wong,et al.  Query Languages for Bags and Aggregate Functions , 1997, J. Comput. Syst. Sci..

[6]  Vincent Danos,et al.  Rule-Based Modelling of Cellular Signalling , 2007, CONCUR.

[7]  Gordon D. Plotkin,et al.  A High-Level Language for Rule-Based Modelling , 2015, PloS one.

[8]  Robin Milner,et al.  Stochastic Bigraphs , 2008, MFPS.

[9]  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.

[10]  Kurt Jensen Coloured Petri Nets , 1992, EATCS Monographs in Theoretical Computer Science.

[11]  Mihai Budiu,et al.  Programming Languages and Systems , 2003, Lecture Notes in Computer Science.

[12]  Vincent Danos,et al.  Scalable Simulation of Cellular Signaling Networks , 2007, APLAS.

[13]  Yin Hoon Chew,et al.  Multiscale digital Arabidopsis predicts individual organ and whole-organism growth , 2014, Proceedings of the National Academy of Sciences.

[14]  Kenneth E. Iverson,et al.  Notation as a tool of thought , 1980, APLQ.

[15]  Vincent Danos,et al.  Rule-Based Modelling, Symmetries, Refinements , 2008, FMSB.

[16]  Lars Michael Kristensen,et al.  Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems , 2007, International Journal on Software Tools for Technology Transfer.

[17]  Gordon D. Plotkin,et al.  Coloured stochastic multilevel multiset rewriting , 2011, CMSB.

[18]  Joachim Niehren,et al.  Biochemical Reaction Rules with Constraints , 2011, ESOP.

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

[20]  William S. Hlavacek,et al.  BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains , 2004, Bioinform..

[21]  D. Singh,et al.  AN OVERVIEW OF THE APPLICATIONS OF MULTISETS , 2007 .

[22]  Robert Muetzelfeldt,et al.  The Simile visual modelling environment , 2003 .

[23]  Robin Milner,et al.  Pure bigraphs: Structure and dynamics , 2006, Inf. Comput..

[24]  Matthew R. Lakin,et al.  A generic abstract machine for stochastic process calculi , 2010, CMSB '10.

[25]  Carlos F. Lopez,et al.  Programming biological models in Python using PySB , 2013, Molecular systems biology.

[26]  Adelinde M. Uhrmacher,et al.  Rule-based multi-level modeling of cell biological systems , 2011, BMC Systems Biology.

[27]  Mihai Budiu,et al.  The Compiler Forest , 2013, ESOP.