Mary-Ann Blätke | Otto-von-Guericke-University Magdeburg (original) (raw)

Papers by Mary-Ann Blätke

Reaction–diffusion systems often play an important role in systems biology when developmental pro... more 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.

Petri nets provide a unifying and versatile framework for the synthesis and engineering of comput... more 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.

Based on Petri nets as formal language for biomodel engineering, we describe the general concept ... more 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. 1 INTRODUCTION Since the One Gene – One Protein Hypothesis has originally been proposed by George Beadle and Edward Tatum (Beadle and Tatum 1941) we have learned that the building blocks of life, the genes, the RNAs, the proteins, and the metabolites all together form a complex network of regulatory interactions. This network is robust, adaptive, and to some extent self-healing, as it includes multiple regulatory feedback loops composed of interacting proteins that often involve other types of biomolecules (Figure 1A). The early view that the flow of information within a cell occurs from the genes to the proteins has been revisited by many exciting discoveries that have been made during the past decades. We meanwhile appreciate that in reality the flow of information, in terms of regulatory interactions, occurs back and forth between the components of the different classes of biomolecules (DNA, RNA, proteins, small molecules). We also understand that there is extensive information processing mediated by the network of interacting proteins and that many proteins seem to be just made for fulfilling these computational tasks. Many qualitative models on molecular mechanisms as well as the corresponding computational (kinetic) models exclusively focus on protein-protein interactions. When working with such models one should keep in mind that the considered networks are not necessarily hard-wired but may change. Alterations in the wiring due to components that may be added, deleted, or modified may be brought about by changes in the pattern of expressed genes. The gene expression pattern in general is responsive to environmental (experimental) conditions, it may depend on the considered cell type, or even on the history of an individual cell and impact stimulus sensing and responses (see (Otomo et al. 1989) for example). Changes in gene expression patterns can be central to regulatory processes. For a given process, the importance of gene regulation may differ from species to species. In fission yeast for example , the cell cycle is regulated mainly through protein-protein interactions. In mammalian cells, the proteins regulating the cell cycle are similar. However, regulation in addition affects changes in the gene expression altering the concentration of proteins involved in cell cycle regulation (Lodish et al. 1996). For technical reasons, (high-throughput) experimental techniques often reveal information restricted to one class of biomolecule at a time (the genome, the transcriptome, the proteome, the metabolome etc.) or to one type of molecular interaction (e.g. protein-protein or protein-DNA interactions). For a true systems level understanding which systems biology aims at, we have to integrate these data to

Here, we introduce a modular and hierarchical modeling concept for large biological Petri nets. T... more Here, we introduce a modular and hierarchical modeling concept for large biological Petri nets. This modeling concept suggests representing every functional system component of a molecular network by an autonomous and self-contained Petri net, so-called module. Due to the specific architecture of the modules, they need to fulfill certain properties important for biological Petri nets to be valid. The entire network is build-up by connecting the modules via common places corresponding to shared molecular components. The individual modules are coupled in a way that the structural properties that are common to all modules apply to the composed network as well. We applied this modeling concept on nociceptive signaling in DRG-neurons to compose a model describing pain on a molecular level for the first time. We verified the applicability of our modeling concept for very complex components and confirmed preservation of the properties after module coupling.

We describe signal transduction of nociceptive mechanisms involved in chronic pain by a qualitati... more We describe signal transduction of nociceptive mechanisms involved in chronic pain by a qualitative Petri net model. More precisely , we investigate signaling in the peripheral terminals of dorsal root ganglion (DRG) neurons. It is a first approach to integrate the current neurobiological and clinical knowledge about nociception on the molecular level from literature in a model describing all the interactions between the involved molecules. Due to the large expected total size of the model under development, we employed a hierarchical and modular approach. In our entire noci-ceptive network, each biological entity like a receptor, enzyme, macro-molecular complex etc. is represented by a self-contained and functional autonomous Petri net, a module. Analysis of the Petri net modules and simulation studies ensure the fulfillment of criteria important for biological Petri nets and the ability to represent the modeled biological function.

We describe a modular modelling approach permitting cura-tion, updating, and distributed developm... more We describe a modular modelling approach permitting cura-tion, 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.

The construction of monolithic pathway models, as well as their coupling, curation and the integr... more The construction of monolithic pathway models, as well as their coupling, curation and the integration of new data is arduous and inconvenient. The modular Petri net modeling concept we present here shows one way to manage these difficulties. In our concept, proteins are represented as functional units by Petri net submodels with a defined structure and connection interface, called modules. Each module integrates all publicly available information about its intramolecular changes and interactions with other molecules. Hence, a module corresponds to an interactive review written in a formalized language. This allows to intuitively understand the functionality of a protein. Modules of interacting proteins communicate through matching subnets, which renders the automatic generation of molecular networks possible. Here, we demonstrate the applicability and advantages of our concept on pain signaling. The molecular mechanisms involved in pain signaling are complex and poorly understood. To enhance our understanding of the mechanisms and to get an impression of the functional interactions among the involved pathways, we systematically build a model from modules of pain-relevant proteins. We also offer a prospect of a platform to organize approved cu-rated modules in order to generate molecular networks. Hopefully, our concept helps bridging the gap between experimental bioscientists and theoretically oriented systems biologists.

We present a detailed model of the JAK-STAT pathway in IL-6 signaling as non-trivial case study d... more 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.

Systems and synthetic biology require multiscale biomodel engineering approaches to integrate div... more Systems and synthetic biology require multiscale biomodel engineering approaches to integrate diverse spatial and temporal scales in order to understand and describe the various interactions in biological systems. Our BioModelKit framework for modular biomodel engineering allows to compose multiscale models from a set of modules, each describing an individual molecular component in the form of a Petri net. In this framework, we do now propose a feature for spatial modelling of molecular biosystems. Our spatial modelling methodology allows to represent the local positioning and movement of individual molecular components represented as modules. In the spatial model, interactions between components are restricted by their local positions. In this context, we use coloured Petri nets to scale the modular composed spatial model, such that each molecular component can exist in an arbitrary number of instances. Thus, a modular composed spatial model can be mapped to the cellular arrangement and different cell geometries.

Mathematical models of molecular networks regulating biological processes in cells or organisms a... more Mathematical models of molecular networks regulating biological processes in cells or organisms are most frequently designed as sets of ordinary differential equations. Various modularisation methods have been applied to reduce the complexity of models, to analyse their structural properties, to separate biological processes, or to reuse model parts. Taking the JAK/STAT signalling pathway with the extensive combinatorial cross-talk of its components as a case study, we make a natural approach to modularisation by creating one module for each biomolecule. Each module consists of a Petri net and associated metadata and is organised in a database publically accessible through a web interface (www.biomodelkit.org). The Petri net describes the reaction mechanism of a given biomolecule and its functional interactions with other components including relevant conformational states. The database is designed to support the curation, documentation, version control, and update of individual modules, and to assist the user in automatically composing complex models from modules. Biomolecule centred modules, associated metadata, and database support together allow the automatic creation of models by considering differential gene expression in given cell types or under certain physiological conditions or states of disease. Modularity also facilitates exploring the consequences of alternative molecular mechanisms by comparative simulation of automatically created models even for users without mathematical skills. Models may be selectively executed as an ODE system, stochastic, or qualitative models or hybrid and exported in the SBML format. The fully automated generation of models of redesigned networks by metadata-guided modification of modules representing biomolecules with mutated function or specificity is proposed.

We developed a generic approach to support the systematic modelling of multiscale biological syst... more We developed a generic approach to support the systematic modelling of multiscale biological systems by the use of colour in Petri nets, which promises to be particularly helpful in investigating spatial aspects of the behaviour of biochemical systems, such as ...

Tutorial by Mary-Ann Blätke

Tutorial, Aug 1, 2011

What is the background of this tutorial? During the last decade, the integrative research area of... more What is the background of this tutorial? During the last decade, the integrative research area of systems biology has constantly been gaining more importance. Experimental and computational approaches are combined to investigate biological systems systematically. To understand biology on its system level, the structural and dynamic properties of regulatory networks in biological systems have to be represented by a model describing the involved species and their interactions. Petri net theory offers the possibility to construct and analyse such models and to represent their structural and dynamic properties by various techniques.

Who should read this tutorial? This tutorial addresses scientists who are looking for an easy and intuitive way to translate a biological system into a Petri net model at an arbitrarily chosen level of abstraction with the option of representing time and space-dependent processes. The tutorial is equally suitable for experimental and theory oriented bio-scientists. The examples given in the tutorial can be used by the interested reader to model her/his biological system.

What can I learn? The tutorial offers an introduction to the Petri net formalism, how to construct a model of a biological system, analyse its structure and dynamic behaviour regarding time-dependent behaviour, which is shown by several intuitive examples. At the end of the tutorial, you will be able to model a biological system on your own using Petri nets. You will also know how to analyse the structure of your model, how to interpret the results and how to perform simulation studies to investigate the time-dependent dynamic behaviour. Also, we also provide a chapter about model checking, which might be helpful to evaluate your model by verifying specified properties that you are interested in. We also show how to use the two Petri net tools Snoopy [19] and Charlie [8]. Based on these instruments you will be able to enhance your knowledge about the modelled biological system and to draw new conclusions from that.

Why should I use Petri nets? The graphical notation and construction of Petri nets allow you to easily and intuitively construct models of biological systems and to characterise the structure, behavioural properties related to the structure and time-dependent dynamic behaviour of a model by several related techniques. Petri nets can describe concurrent and parallel processes, as well as communication and synchronisation in bipartite systems regardless of the abstraction level in a comprehensive and mathematically correct model [12]. Time, as well as space aspects, can be modelled by a Petri net. Several specialised Petri net classes are available to describe di erent scenarios and to consider di erent simulative approaches. Therefore, the kinetics of the qualitative Petri net model can be considered as stochastic, continuous or as a mixture of both (hybrid) [12]. In silico experiments with Petri net models, permit to analyse a biological system systematically by applying structural as well as dynamic analysing techniques to investigate perturbations. From the obtained results new insights can be achieved by the biological system. Thereby, you can increase your understanding, reveal gaps in knowledge, and detect missing and essential components. Based on a valid model it is possible to predict the system behaviour. This might be helpful to investigate pathological states and their molecular basis aimed at identifying potential targets to develop therapeutical intervention strategies. The Petri net formalism offers quite a few advantages over other and more broadly used modelling frameworks. The di fferent Petri net classes are interconvertible with each other without changing the qualitative structure. Due to the graphical visualisation of molecular networks by Petri nets, a bioscientist can intuitively understand the modelled mechanisms. The user does not have to deal with many di erent representations of a molecular network which do not obviously correspond to each other like a biological cartoon, the structure of the biological network, the mathematical equations (stochastic, continuous, etc.) and the implementation of the equations. Besides, the transformation of a molecular network represented by a Petri net into e.g. ODE equations is unique, but not vice versa [24]. Several reliable analysis tools have been developing to investigate qualitative and quantitative properties of Petri nets by structural analysis, simulation of the time-dependent dynamic behaviour and model checking.

What is the scheme of this tutorial? First of all, you will learn all the basics about the Petri net formalism motivated by small biological examples that are easy to understand. Next, you will see how to analyse the structure of a model and how to interpret the obtained results and their biological meaning. Afterwards, you will learn how to perform simulations with your model. We also off er a chapter about model checking, where you can learn how to verify specific properties of your model. Then, we introduce the two Petri net tools Snoopy [19] and Charlie [8]. All sections, where theoretical concepts are explained, are divided into an informal and a formal part. We start with an informal introduction, where we explain the basics and the biological relations. Subsequently and to be complete, we give the formal definitions and a small help on \how to read" the definitions at the end of the section.

What tools do I need? Several tools are available to model biological systems, simulate their time-dependent dynamic behaviour and analyse their structure. Here, we use the Petri net editor Snoopy [19] to model biological systems and simulate/animate their time-dependent dynamic behaviour. Charlie [8] is used to analyse the Petri net structure. Both software tools were developed at the chair of Data Structures and Software Dependability at the Brandenburg University of Technology Cottbus and are freely available for non-commercial use. You can download them at http://www-dssz.informatik. tu-cottbus.de/DSSZ/Software/Software [1].

Tutorial, Aug 1, 2011