Results 1  10
of
79
BioPEPA: a framework for the modelling and analysis of biological systems
, 2008
"... In this work we present BioPEPA, a process algebra for the modelling and the analysis of biochemical networks. It is a modification of PEPA, originally defined for the performance analysis of computer systems, in order to handle some features of biological models, such as stoichiometry and the use ..."
Abstract

Cited by 94 (25 self)
 Add to MetaCart
In this work we present BioPEPA, a process algebra for the modelling and the analysis of biochemical networks. It is a modification of PEPA, originally defined for the performance analysis of computer systems, in order to handle some features of biological models, such as stoichiometry and the use of general kinetic laws. The domain of application is the one of biochemical networks. BioPEPA may be seen as an intermediate, formal, compositional representation of biological systems, on which different kinds of analysis can be carried out. BioPEPA is enriched with some notions of equivalence. Specifically, the isomorphism and strong bisimulation for PEPA have been considered. Finally, we show the translation of three biological models into the new language and we report some analysis results.
Machine learning biochemical networks from temporal logic properties
 Transactions on Computational Systems Biology
, 2006
"... Abstract. One central issue in systems biology is the definition of formal languages for describing complex biochemical systems and their behavior at different levels. The biochemical abstract machine BIOCHAM is based on two formal languages, one rulebased language used for modeling biochemical net ..."
Abstract

Cited by 49 (11 self)
 Add to MetaCart
(Show Context)
Abstract. One central issue in systems biology is the definition of formal languages for describing complex biochemical systems and their behavior at different levels. The biochemical abstract machine BIOCHAM is based on two formal languages, one rulebased language used for modeling biochemical networks, at three abstraction levels corresponding to three semantics: boolean, concentration and population; and one temporal logic language used for formalizing the biological properties of the system. In this paper, we show how the temporal logic language can be turned into a specification language. We describe two algorithms for inferring reaction rules and kinetic parameter values from a temporal specification formalizing the biological data. Then, with an example of the cell cycle control, we illustrate how these machine learning techniques may be useful to the modeler. 1
Abstract machines of systems biology
 Transactions on Computational Systems Biology
, 2005
"... Abstract. Living cells are extremely wellorganized autonomous systems, consisting of discrete interacting components. Key to understanding and modeling their behavior is modeling their system organization. Four distinct chemical toolkits (classes of macromolecules) have been characterized, each com ..."
Abstract

Cited by 47 (2 self)
 Add to MetaCart
(Show Context)
Abstract. Living cells are extremely wellorganized autonomous systems, consisting of discrete interacting components. Key to understanding and modeling their behavior is modeling their system organization. Four distinct chemical toolkits (classes of macromolecules) have been characterized, each combinatorial in nature. Each toolkit consists of a small number of simple components that are assembled (polymerized) into complex structures that interact in rich ways. Each toolkit abstracts away from chemistry; it embodies an abstract machine with its own instruction set and its own peculiar interaction model. These interaction models are highly effective, but are not ones commonly used in computing: proteins stick together, genes have fixed output, membranes carry activity on their surfaces. Biologists have invented a number of notations attempting to describe these abstract machines and the processes they implement. Moving up from molecular biology, systems biology aims to understand how these interaction models work, separately and together. 1
Graph theory for rulebased modeling of biochemical networks
 Lect. Notes Comput. Sci
, 2006
"... Abstract. We introduce a graphtheoretic formalism suitable for modeling biochemical networks marked by combinatorial complexity, such as signaltransduction systems, in which proteinprotein interactions play a prominent role. This development extends earlier work by allowing for explicit represen ..."
Abstract

Cited by 30 (10 self)
 Add to MetaCart
(Show Context)
Abstract. We introduce a graphtheoretic formalism suitable for modeling biochemical networks marked by combinatorial complexity, such as signaltransduction systems, in which proteinprotein interactions play a prominent role. This development extends earlier work by allowing for explicit representation of the connectivity of a protein complex. Within the formalism, typed attributed graphs are used to represent proteins and their functional components, complexes, conformations, and states of posttranslational covalent modification. Graph transformation rules are used to represent proteinprotein interactions and their effects. Each rule defines a generalized reaction, i.e., a class of potential reactions that are logically consistent with knowledge or assumptions about the represented biomolecular interaction. A model is specified by defining 1) molecularentity graphs, which delimit the molecular entities and material components of a system and their possible states, 2) graph transformation rules, and 3) a seed set of graphs representing chemical species, such as the initial species present before introduction of a signal. A reaction network is generated iteratively through application of the graph transformation rules. The rules are first applied to the seed graphs and then to any and all new graphs that subsequently arise as a result of graph transformation. This procedure continues until no new graphs are generated or a specified termination condition is satisfied. The formalism supports the generation of a list of reactions in a system, which can be used to derive different types of physicochemical models, which can be simulated and analyzed in different ways. The processes of generating and simulating the network may be combined so that species are generated only as needed. 1
Abstract interpretation and types for systems biology
 IN: THEORETICAL COMPUTER SCIENCE
, 2008
"... Abstract interpretation is a theory of abstraction that has been introduced for the analysis of programs. In particular, it has proved useful for organizing the multiple semantics of a given programming language in a hierarchy corresponding to different detail levels, and for defining type systems f ..."
Abstract

Cited by 26 (8 self)
 Add to MetaCart
(Show Context)
Abstract interpretation is a theory of abstraction that has been introduced for the analysis of programs. In particular, it has proved useful for organizing the multiple semantics of a given programming language in a hierarchy corresponding to different detail levels, and for defining type systems for programming languages and program analyzers in software engineering. In this paper, we investigate the application of these concepts to systems biology formalisms. More specifically, we consider the Systems Biology Markup Language SBML, and the Biochemical Abstract Machine BIOCHAM with its differential, stochastic, discrete and boolean semantics. We first show how all of these different semantics, except the differential one, can be formally related by simple Galois connections. Then we define three type systems: one for checking or inferring the functions of proteins in a reaction model, one for checking or inferring the activation and inhibition effects of proteins in a reaction model, and another one for checking or inferring the topology of compartments or locations. We show that the framework of abstract interpretation elegantly applies to the formalization of these further abstractions, and to the implementation of linear or quadratic time type checking as well as type inference algorithms. Furthermore, we show a theorem of independence of the graph of activation and inhibition effects from the kinetic expressions in the reaction model, under general conditions. Through some examples, we show that the analysis of biochemical models by type inference provides accurate and useful information. Interestingly, such a mathematical formalization of the abstractions commonly used in systems biology already provides some guidelines for the extensions of biochemical reaction rule languages.
Rulebased modeling of biochemical networks
 Complexity
, 2005
"... We present a method for generating a biochemical reaction network from a description of the interactions of components of biomolecules. The interactions are specified in the form of reaction rules, each of which defines a class of reaction associated with a type of interaction. Reactants within a cl ..."
Abstract

Cited by 22 (14 self)
 Add to MetaCart
(Show Context)
We present a method for generating a biochemical reaction network from a description of the interactions of components of biomolecules. The interactions are specified in the form of reaction rules, each of which defines a class of reaction associated with a type of interaction. Reactants within a class have shared properties, which are specified in the rule defining the class. A rule also provides a rate law, which governs each reaction in a class, and a template for transforming reactants into products. A set of reaction rules can be applied to a seed set of chemical species and, subsequently, any new species that are found as products of reactions to generate a list of reactions and a list of the chemical species that participate in these reactions, i.e., a reaction network, which can be translated into a mathematical model. © 2005 Wiley Periodicals, Inc. Complexity 10: 22–41, 2005 Key Words: local rules; automatic model generation; networks; signal transduction; combinatorial complexity; systems biology The cell is a complex adaptive system whose emergent behavior we understand only poorly. One reason for our lack of understanding is the complexity of cellular decision making, which is often mediated by a system of interacting proteins. Systems of interacting proteins are particularly prominent in signal transduction [1], 1 the focus Correspondence to: William S. Hlavacek,
From Reaction Models to Influence Graphs and Back: a Theorem
, 2008
"... Biologists use diagrams to represent interactions between molecular species, and on the computer, diagrammatic notations are also more and more employed in interactive maps. These diagrams are fundamentally of two types: reaction graphs and activation/inhibition graphs. In this paper, we study the ..."
Abstract

Cited by 17 (8 self)
 Add to MetaCart
(Show Context)
Biologists use diagrams to represent interactions between molecular species, and on the computer, diagrammatic notations are also more and more employed in interactive maps. These diagrams are fundamentally of two types: reaction graphs and activation/inhibition graphs. In this paper, we study the formal relationship between these graphs. We consider systems of biochemical reactions with kinetic expressions, as written in the Systems Biology Markup Language SBML, and interpreted by a system of Ordinary Differential Equations over molecular concentrations. We show that under a general condition of increasing monotonicity of the kinetic expressions, and in absence of both activation and inhibition effects between a pair of molecules, the influence graph inferred from the stoichiometric coefficients of the reactions is equal to the one defined by the signs of the coefficients of the Jacobian matrix. Under these conditions, satisfied by mass action law, MichaelisMenten and Hill kinetics, the influence graph is thus independent of the precise kinetic expressions, and is computable in linear time in the number of reactions. We apply these results to Kohn’s map of the mammalian cell cycle and to the MAPK signalling cascade. Then we propose a syntax for denoting antagonists in reaction rules and generalize our results to this setting.
Formal Cell Biology in Biocham
"... Abstract. Biologists use diagrams to represent interactions between molecular species, and on the computer, diagrammatic notations are also employed in interactive maps. These diagrams are fundamentally of two types: reaction graphs and activation/inhibition graphs. In this tutorial, we study these ..."
Abstract

Cited by 16 (6 self)
 Add to MetaCart
(Show Context)
Abstract. Biologists use diagrams to represent interactions between molecular species, and on the computer, diagrammatic notations are also employed in interactive maps. These diagrams are fundamentally of two types: reaction graphs and activation/inhibition graphs. In this tutorial, we study these graphs with formal methods originating from programming theory. We consider systems of biochemical reactions with kinetic expressions, as written in the Systems Biology Markup Language (SBML), and interpreted in the Biochemical Abstract Machine (Biocham) at different levels of abstraction, by either an asynchronous boolean transition system, a continuous time Markov chain, or a system of Ordinary Differential Equations over molecular concentrations. We show that under general conditions satisfied in practice, the activation/inhibition graph is independent of the precise kinetic expressions, and is computable in linear time in the number of reactions. Then we consider the formalization of the biological properties of systems, as observed in experiments, in temporal logics. We show that these logics are expressive enough to capture semiqualitative semiquantitative properties of the boolean and differential semantics of reaction models, and that modelchecking techniques can be used to validate a model w.r.t. its temporal specification, complete it, and search for kinetic parameter values. We illustrate this modelling method with examples on the MAPK signalling cascade, and on Kohn’s map of the mammalian cell cycle. 1
On the analysis of numerical data time series in temporal logic
 In Proc. CMSB 2007. LNCS/LNBI 4695
, 2007
"... Abstract. Temporal logics and modelchecking techniques have proved successful to respectively express biological properties of complex biochemical systems, and automatically verify their satisfaction in both qualitative and quantitative models. In this paper, we propose a finite time horizon model ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
(Show Context)
Abstract. Temporal logics and modelchecking techniques have proved successful to respectively express biological properties of complex biochemical systems, and automatically verify their satisfaction in both qualitative and quantitative models. In this paper, we propose a finite time horizon modelchecking algorithm for the existential fragment of LTL with numerical constraints over the reals, with the ability to compute the range of values of the real variables occurring in a formula that makes it true in a model. We illustrate this approach for the analysis of biological data time series, provide a set of biologically relevant patterns of formulas, and evaluate them on models of the cell cycle control and MAPK signal transduction. 1
The pathway logic assistant
 Third Int. Workshop on Comp. Methods in Systems Biology
, 2005
"... This paper describes representations of biological processes based on Rewriting Logic and Petri net formalisms and mappings between these representations used in the Pathway Logic Assistant. The mappings are shown to preserve properties of interest. In addition a relevant subnet transformation is de ..."
Abstract

Cited by 12 (5 self)
 Add to MetaCart
This paper describes representations of biological processes based on Rewriting Logic and Petri net formalisms and mappings between these representations used in the Pathway Logic Assistant. The mappings are shown to preserve properties of interest. In addition a relevant subnet transformation is defined, that specializes a Petri net model to a specific query to reduce the number of transitions that must be considered when answering the query. The transformation is shown to preserve the query in the sense that no answers are lost.