Mary Ann Blätke

Modelling and simulating reaction-diffusion systems using coloured Petri nets

Reaction-diffusion systems often play an important role in systems biology when developmental processes are involved. Traditional methods of modelling and simulating such systems require substantial prior knowledge of mathematics and/or simulation algorithms. Such skills may impose a challenge for biologists, when they are not equally well-trained in mathematics and computer science. Coloured Petri nets as a high-level and graphical language offer an attractive alternative, which is easily approachable. In this paper, we investigate a coloured Petri net framework integrating deterministic, stochastic and hybrid modelling formalisms and corresponding simulation algorithms for the modelling and simulation of reaction-diffusion processes that may be closely coupled with signalling pathways, metabolic reactions and/or gene expression. Such systems often manifest multiscaleness in time, space and/or concentration. We introduce our approach by means of some basic diffusion scenarios, and test it against an established case study, the Brusselator model.

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.

BioModel Engineering with Petri Nets

We present a unifying Petri net framework comprising the qualitative, stochastic, continuous, and hybrid paradigm for modeling and analyzing biological reaction networks. By means of a running case study, we demonstrate how the different paradigms contribute in complementary ways to the overall understanding of emergent network properties. The framework is supported by four tools—Snoopy, Charlie, Marcie, and MC2—which will also be of use in the suggested exercises.

A Petri-Net-Based Framework for Biomodel Engineering

Petri nets provide a unifying and versatile framework for the synthesis and engineering of computational models of biochemical reaction networks and of gene regulatory networks. Starting with the basic definitions, we provide an introduction into the different classes of Petri nets that reinterpret a Petri net graph as a qualitative, stochastic, continuous, or hybrid model. Static and dynamic analysis in addition to simulative model checking provide a rich choice of methods for the analysis of the structure and dynamic behavior of Petri net models. Coloring of Petri nets of all classes is powerful for multiscale modeling and for the representation of location and space in reaction networks since it combines the concept of Petri nets with the computational mightiness of a programming language. In the context of the Petri net framework, we provide two most recently developed approaches to biomodel engineering, the database-assisted automatic composition and modification of Petri nets with the help of reusable, metadata-containing modules, and the automatic reconstruction of networks based on time series data sets. With all these features the framework provides multiple options for biomodel engineering in the context of systems and synthetic biology.

JAK-STAT Signalling as Example for a Database-Supported Modular Modelling Concept

We present a detailed model of the JAK-STAT pathway in IL-6 signaling as non-trivial case study demonstrating a new database-supported modular modeling method. A module is a self-contained and autonomous Petri net, centred around an individual protein. The modelling approach allows to easily generate and modify coherent, executable models composed from a collection of modules and provides numerous options for advanced biomodel engineering.

A module-based approach to biomodel engineering with Petri nets

Based on Petri nets as formal language for biomodel engineering, we describe the general concept of a modular modelling approach that considers the functional coupling of modules representing components of the genome, the transcriptome, and the proteome in the form of an executable model. The composable, metadata-containing Petri net modules are organized in a database with version control and accessible through a web interface. The effects of genes and their mutated alleles on downstream components are modelled by gene modules coupled to protein modules through RNA modules by specific interfaces designed for the automatic, database-assisted composition. Automatically assembled models may integrate forward and reverse engineered modules and consider cell type-specific gene expression patterns. Prospects for automatic model generation including its application to systems biology, synthetic biology, and to functional genomics are discussed.