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Showing papers presented at "Computational Methods in Systems Biology in 2010"


Proceedings ArticleDOI
29 Sep 2010
TL;DR: In this article, a stochastic hybrid model is proposed for the analysis of continuous-time Markov chains that describe networks of biochemical reactions and play an important role in the stochastically modeling of biological systems.
Abstract: We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe networks of biochemical reactions and play an important role in the stochastic modeling of biological systems. Our approach is based on the construction of a stochastic hybrid model in which certain discrete random variables of the original Markov chain are approximated by continuous deterministic variables. We compute the solution of the stochastic hybrid model using a numerical algorithm that discretizes time and in each step performs a mutual update of the transient probability distribution of the discrete stochastic variables and the values of the continuous deterministic variables. We implemented the algorithm and we demonstrate its usefulness and efficiency on several case studies from systems biology.

45 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: A comparative study focusing on the three base case techniques of stochastic analysis: exact numericalAnalysis, approximative numerical analysis, and simulation is reported on.
Abstract: Stochastic models are becoming increasingly popular in Systems Biology. They are compulsory, if the stochastic noise is crucial for the behavioural properties to be investigated. Thus, substantial effort has been made to develop appropriate and efficient stochastic analysis techniques. The impressive progress of hardware power and specifically the advent of multicore computers have ameliorated the computational tractability of stochastic models. We report on a comparative study focusing on the three base case techniques of stochastic analysis: exact numerical analysis, approximative numerical analysis, and simulation. For modelling we use extended stochastic Petri nets, which allows us to take advantage of structural information and to complement the stochastic analyses by qualitative analyses, belonging to the standard body of Petri net theory.

26 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: The generic framework allows an appropriate high-level language to be paired with the required simulation algorithm, based on the biological system under consideration, to simulate a stochastic pi-calculus model of plasmid co-transfection, where plasmids can form aggregates of arbitrary size and where rates of mRNA degradation are non-exponential.
Abstract: This paper presents a generic abstract machine for simulating a broad range of process calculi with an arbitrary reaction-based simulation algorithm. The abstract machine is instantiated to a particular calculus by defining two functions: one for transforming a process of the calculus to a set of species, and another for computing the set of possible reactions between species. Unlike existing simulation algorithms for chemical reactions, the abstract machine can simulate process calculi that generate potentially unbounded numbers of species and reactions. This is achieved by means of a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. As a proof of concept, the generic abstract machine is instantiated for the stochastic pi-calculus, and the instantiation is implemented as part of the SPiM stochastic simulator. The structure of the abstract machine facilitates a significant optimisation by allowing channel restrictions to be stored as species complexes. We also present a novel algorithm for simulating chemical reactions with general distributions, based on the Next Reaction Method of Gibson and Bruck. We use our generic framework to simulate a stochastic pi-calculus model of plasmid co-transfection, where plasmids can form aggregates of arbitrary size and where rates of mRNA degradation are non-exponential. The example illustrates the flexibility of our framework, which allows an appropriate high-level language to be paired with the required simulation algorithm, based on the biological system under consideration.

23 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: The presented cabin (Causal Analysis of Biological Interaction Network) method determines a causal model view composed of a subnetwork and a set of agent states deduced from observations with regards to a model of network dynamics.
Abstract: Signaling and regulatory pathways coordinate multiple cellular functions in response to environmental variations. Discovering the pathways governing functionally specific responses is essential for understanding of biological systems. It aims at determining the causal cascades of regulations leading to the observed responses. Their characterization by computational methods remains an important and challenging question. The presented cabin (Causal Analysis of Biological Interaction Network) method determines a causal model view composed of a subnetwork and a set of agent states deduced from observations with regards to a model of network dynamics. The validity of the results is ensured by formally checking the conditions of correctness of a model with respect to observations. State-based and symbolic versions of the algorithm have been implemented and used for a biological case study.

19 citations


Book ChapterDOI
01 Jan 2010
TL;DR: This brief review for systems biologists and computational modelers introduces some of the basic concepts of successful biomodel engineering, illustrating them with examples from a variety of application domains, ranging from metabolic networks to cellular signaling cascades.
Abstract: Biomodel engineering is the science of designing, constructing and analyzing computational models of biological systems. It forms a systematic and powerful extension of earlier mathematical modeling approaches and has recently gained popularity in systems biology and synthetic biology. In this brief review for systems biologists and computational modelers, we introduce some of the basic concepts of successful biomodel engineering, illustrating them with examples from a variety of application domains, ranging from metabolic networks to cellular signaling cascades. We also present a more detailed outline of one of the major techniques of biomodel engineering – Petri net models – which provides a flexible and powerful tool for building, validating and exploring computational descriptions of biological systems.

17 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: This paper elaborate on both static analysis based on the structure of models and dynamic analysis of generated stochastic simulation traces performed using the Traviando trace analyser, using the Bio-PEPA process algebra as a modelling language.
Abstract: Verifying that a computational model implements the conceptual model of some dynamic biological phenomena is an important yet non-trivial task In this paper, we discuss a variety of steps that contribute to this verification process, using the Bio-PEPA process algebra as a modelling language and describing the verification steps that are supported by the Bio-PEPA tool In particular, we elaborate on both static analysis based on the structure of models and dynamic analysis of generated stochastic simulation traces performed using the Traviando trace analyser We illustrate the approach with a model of a JAK/STAT signalling pathway

9 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: A way to generalize well-known results about the steady-state analysis of some symbolic Ordinary Differential Equations systems by taking into account the structure of the reaction network is presented.
Abstract: In recent years Systems Biology has become a rich field of study, trying to encompass all the information that has become available thanks to the new high-throughput techniques of biologists, in order to build detailed models of complex systems. Some models have been growing bigger and bigger, but lacking most of precise kinetic data. Other models remain of reasonable size, but have an even larger uncertainty about parameter values. Unfortunately, very few analyses allow to extract information about the dynamics of these models when pure symbolic computations fails. This article presents a way to generalize well-known results about the steady-state analysis of some symbolic Ordinary Differential Equations systems by taking into account the structure of the reaction network. The structural study of the underlying Petri net, usually used mostly for metabolic flux analysis, will provide classes where the computation of some steady states of the system is possible, even though the original symbolic model did not form an S-system and was not solvable by state-of-the-art symbolic computation software. This new method is then illustrated on some models of the Biomodels repository and is followed by a brief discussion.

8 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: A method to compute all pathways in a signalling network that satisfy a simple property constraining initial, final and intermediate states is introduced and is compared to the steady state view underlying Petri net place/transition invariants and flux balance analysis.
Abstract: A signalling network is a network of reactions that govern how a cell responds to its environment. A pathway is a dynamic flow of "signal" through the network (signal transduction), for example from a receptor to a transcription factor that enables expression of a gene. In this paper we introduce a method to compute all pathways in a signalling network that satisfy a simple property constraining initial, final and intermediate states. This method, concerned with signal transduction, is compared to the steady state view underlying Petri net place/transition invariants and flux balance analysis. We apply the method to the signalling network model being developed in the Pathway Logic project and identify knockout/inhibition targets and common (pathway) events. This approach also allows us to better understand and formalise the interaction between pathways in a network, for example to identifying pathway inhibition targets that limit the effect on unrelated pathways.

7 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: A method and an associated computational tool are described to modify and piecewise enlarge the topology of a biological network model, using a set of biochemical components, in order to generate one or more models whose behaviours simulate that of a target biological system.
Abstract: In this paper we describe a method and an associated computational tool to modify and piecewise enlarge the topology of a biological network model, using a set of biochemical components, in order to generate one or more models whose behaviours simulate that of a target biological system. These components are defined as continuous Petri nets and stored in a library for ease of reuse. An optimization algorithm is proposed which exploits Simulated Annealing in order to alter an initial model by reference to the desired behaviour of the target model.Simulation results on a realistic illustrative example signalling pathway show that the proposed method performs well in terms of exploiting the characteristics of simulated annealing in order to generate interesting models with behaviours close to that of the target biochemical system without any pre-knowledge on the target topology itself. In future work we plan to use the generated topologies as population candidates when using an evolutionary approach to further tune the network structure and kinetic parameters.

7 citations


Proceedings ArticleDOI
29 Sep 2010
TL;DR: This paper model and analyze the cytokinin response network of Arabidopsis thaliana with a focus on clarifying the character of an important feedback mechanism and presents a formalism that augments Boolean models with stochastic aspects.
Abstract: Boolean modeling frameworks have long since proved their worth for capturing and analyzing essential characteristics of complex systems. Hybrid approaches aim at exploiting the advantages of Boolean formalisms while refining expressiveness. In this paper, we present a formalism that augments Boolean models with stochastic aspects. More specifically, biological reactions effecting a system in a given state are associated with probabilities, resulting in dynamical behavior represented as a Markov chain. Using this approach, we model and analyze the cytokinin response network of Arabidopsis thaliana with a focus on clarifying the character of an important feedback mechanism.

6 citations


Proceedings Article
29 Sep 2010
TL;DR: The papers contained in this volume present original models or paradigms for modelling biological processes together with their application domains; frameworks and techniques for verifying, validating, analyzing, and simulating biological systems; inference from high-throughput experimental data.
Abstract: This volume contains the papers presented at the 8th International Conference on Computational Methods in Systems Biology, CMSB 2010, held in Trento, Italy, on September 29 -- October 1, 2010. The Conference gathered together computer scientists, biologists, mathematicians, and physicists interested in a system-level understanding of biological systems. In particular, CMSB 2010 solicited innovative research papers focussing on the dynamics and on the analysis of biological systems, networks, and data. The papers contained in this volume present original models or paradigms for modelling biological processes together with their application domains; frameworks and techniques for verifying, validating, analyzing, and simulating biological systems; inference from high-throughput experimental data.

Proceedings ArticleDOI
29 Sep 2010
TL;DR: A local search algorithm for structural identification of Generalized Mass Action models from time course data is proposed and is able to find as good or better models than any of the other approaches.
Abstract: We propose a local search algorithm for structural identification of Generalized Mass Action (GMA) models from time course data. The algorithm has been implemented as part of our existing system for identification of dynamical systems.We compare this approach to existing alternatives in terms of analytical GMA models, analytical GMA models with parameter estimation from time course data, S-systems, and linear models. This is done on three new test problems designed to exhibit different characteristic properties of biochemical pathways, and which are defined with chemical rate reactions. By applying state-of-the-art algorithmic methods we are able to make a full investigation for the test problems also with noisy data.The results show that on the tested problems, our structural identification algorithm is able to find as good or better models than any of the other approaches. It can therefore be expected to be a useful tool for identifying models of unknown systems from time course data.All test problems are available on the web.

BookDOI
01 Jan 2010
TL;DR: Transactions on Computational Systems Biology XII: Special Issue on Modeling Methodologies book is released and additional information that is in conjuction with this book is provided.
Abstract: TRA NSA CTIONS ON COMPUTATIONA L SYSTEMS BIOLOGY XII: SPECIA L ISSUE ON MODELING METHODOLOGIES To get Transact ions on Computational Systems Biolog y XII: Special Issue on Modeling Methodolog ies PDF, please access the button under and save the file or get access to additional information that are in conjuction with Transactions on Computational Systems Biology XII: Special Issue on Modeling Methodologies book.

Proceedings ArticleDOI
29 Sep 2010
TL;DR: In this article, an extension of discrete regulatory networks with short-term stimuli is proposed to incorporate instant effects and different timescales within a single biological system, which is implemented by means of a front end to the mCRL2 tool set and illustrated for the switching of bacteriophage lambda and a bio-medical case study related to TGFβ driven fibrotic conditions.
Abstract: To incorporate instant effects and different timescales within a single biological system, an extension of discrete regulatory networks with short-term stimuli is proposed. By maintaining a vector of recent changes, activities initiated by a steep increase or decrease can be captured in a qualitative setting. In order to compensate for the blow-up due to enhanced states, we focus on observable behavior. Identification of bisimilar states yields a compact system representation truthfully expressing the information relevant for deciding logical properties. The approach is implemented by means of a front-end to the mCRL2 tool set and illustrated for the switching of bacteriophage lambda and a bio-medical case study related to TGFβ driven fibrotic conditions.

Proceedings ArticleDOI
29 Sep 2010
TL;DR: This talk considers how to extend basic reasoning about concentration levels by the addition of trend formulas, state formulas that represent ascending or descending trends of concentration, similar to the sign of a first-order derivative, but in a stochastic setting.
Abstract: Process algebras were originally designed for modelling concurrent computations. Over the last decade, computer scientists have explored their application to modelling bio-molecular processes, with considerable success. A predominant abstraction is molecule-as-process [RSS01, Car08], where each process represents a molecule. Analysis is by simulation and in a stochastic setting, there is a clear correspondence with stochastic simulation as proposed by Gillespie [Gil77].An alternative abstraction is species-as-process [CGH06, CH09b], based on models that are continuous time Markov chains (CTMC) with levels of concentration. This population-based abstraction allows control of the granularity of representation, at one end of the spectrum corresponding to Gillespie simulation and at the other end, ordinary differential equations. A key feature of this style is it permits a range of analysis techniques in addition to simulation, namely relations (e.g. bisimulation) and model-checking properties expressed in qualitative and quantitative logics.Within the species-as-process paradigm, a useful style has been reagent-centric models[CH09a], where all reagents in a reaction map to processes, whose variation reflect decrease through consumption and increase through product formation (consumers and producers). The reagent-centric style of modelling provides a distributed view of a system and is easily represented in a state-based formalism where state variables represent levels of concentration. An example is the language of reactive modules used in the PRISM model-checker [KNP02]. Whilst this language is not strictly a process algebra: processes are represented by modules, there is process algebraic synchronisation between modules. Moreover, modules can be generic.This talk gives an overview of recent advances and applications of the reagent-centric modelling paradigm, extending basic reasoning about concentration levels and then developing higher level concepts such as pathway-as-process and tissue-as-process.We consider how to extend basic reasoning about concentration levels by the addition of trend formulas, state formulas that represent ascending or descending trends of concentration [AC10]. These are similar to the sign of a first-order derivative, but in a stochastic setting. We then consider extending the species-as-process paradigm to pathway-as-process. While still adopting the reagent-centric style, we model a signalling pathway as a (synchronising) parallel composition (with renaming) of instances of generic modules, which have both internal and external reactions. The motivation is to investigate pathway interactions, known as crosstalk, and so pathways are themselves composed. We show how we can use a quantitative logic to detect cross-talk, and a qualitative logic to characterise the type of crosstalk [DC10b]. Finally, we describe a new stochastic process algebra for modelling different levels of abstraction, specifically biochemistry and tissue. The algebra is motivated by modelling pattern formation based on reaction-diffusion equations. Processes represent both biochemical species and tissues at certain locations; an explicit notion of geometrical space is embedded in the algebra. Synchronisation between the two levels is through special actions called hooks [DC10a]. The ultimate goal is to be able to compare models of similar tissue formation, but with different underlying biochemistry.