Simulating bacterial transcription and translation in a stochastic pi-calculus

International audienceStochastic simulation of genetic networks based on models in the stochastic pi-calculus is a promising recent approach. This paper contributes an extensible model of the central mechanisms of gene ex- pression i.e. transcription and translation, at the prototypical instance of bacteria. We reach extensibility through object-oriented abstractions, that are expressible in a stochastic π-calculus with pattern guarded inputs. We illustrate our generic model by simulating the effect of translational bursting in bacterial gene expression

Theoretical basis of the community effect in development

Peer reviewedPublisher PD

Models of stochastic gene expression and Weyl algebra

International audienceThis paper presents a symbolic algorithm for computing the ODE systems which describe the evolution of the moments associated to a chemical reaction system, considered from a stochastic point of view. The algorithm, which is formulated in the Weyl algebra, seems more efficient than the corresponding method, based on partial derivatives. In particular, an efficient method for handling conservation laws is presented. The output of the algorithm can be used for a further investigation of the system behaviour, by numerical methods. Relevant examples are carried out

Rule based modeling of gene regulation and biosynthesis of tryptophan in E. coli

The genetic regulation of the Trp operon in the bacterium E. coli relies on a sophisticated control mechanism. It tightly couples the advance of transcribing RNA polymerase to the efficiency of the contemporaneous translation of the nascent transcript by a ribosome. The concurrent control of this process involves interdependencies between multiple molecular actors. Within process algebra based modeling languages focused on pairwise interaction, its representation required sophisticated coding tricks. In this work, we abstract the mechanism of transcriptional attenuation within a novel rule base modeling language. It allows non-trivial concurrent control by representing molecules as parametrized terms

Modélisation de l'expression génétique bactérienne dans un pi-calcul stochastique à objets concurrents

Systems biology seeks to understand the cellular-level dynamics arising from the interaction of cellular constituents over time. Modeling and simulation are essential techniques of the field. We follow Regev and Shapiro (2002) in using the stochastic pi-calculus as a formal representation language for biomolecular knowledge. We elaborate molecular level modeling studies for bacterial gene expression, which is relevant to higher organisms as well. Our starting point are concrete case studies on regulation at the lambda switch, transcription and translation.%\\ These reveal the usefulness of programming concepts such as concurrent objects, and motivate an extension of the stochastic pi-calculus with input patterns. We present a semantics for this language that maps programs to continuous time Markov chains. We validate our models through exhaustive stochastic simulation.La biologie systémique cherche à comprendre la dynamique cellulaire qui émerge des interactions des constituantes cellulaires au cours du temps. La modélisation et la simulation sont des méthodes fondamentales de ce domaine. Nous nous inspirons de la proposition de Regev et Shapiro (2002) consistant à appliquer le pi-calcul stochastique comme langage formel de représentation de connaissances biomoléculaires. Nos études portent sur la modélisation à l´échelle moléculaire de l'expression génétique bactérienne et s'avèrent pertinentes pour des organismes supérieurs. Nos points de départs sont des études de cas concrets de la régulation de la bascule génétique du phage lambda, de la transcription et de la traduction. Ces études révèlent l'utilité de concepts de programmation tels que les objets concurrents et motivent une extension du pi-calcul stochastique avec des motifs de réception. Nous présentons une sémantique pour ce langage qui attribue des chaînes Markoviennes en temps continu aux programmes. Nous validons nos modèles par des simulations stochastiques

Simulating bacterial transcription and translation in a stochastic pi-calculus

International audienceStochastic simulation of genetic networks based on models in the stochastic pi-calculus is a promising recent approach. This paper contributes an extensible model of the central mechanisms of gene ex- pression i.e. transcription and translation, at the prototypical instance of bacteria. We reach extensibility through object-oriented abstractions, that are expressible in a stochastic π-calculus with pattern guarded inputs. We illustrate our generic model by simulating the effect of translational bursting in bacterial gene expression

Biomolecular agents as multi-behavioural concurrent objects

International audiencen recent years, there has been increasing interest in computational models of biological systems based on various calculi of communicating processes, such as the stochastic pi-calculus. These models make it possible to simulate and eventually visualize the dynamic evolution of complex biosystems in time and under varying environmental conditions. While the elegance of the pi-calculus lies in its minimality, this is also a drawback when it comes to modeling because much effort must be devoted to encoding high-level ideas into the low-level means that the language affords us. In this paper, we describe an on-going effort to design a new higher-level programming language that provides direct ontological support for the concepts which are used to formulate, organize and structure models of biomolecular systems. Our language has an object-oriented flavour where we view molecular components as agents with finite sets of behaviours (states). Reactions are modeled as exchanges over connected ports that may cause agents to switch states

Multi-level modeling in systems biology by discrete event approaches

International audienc

Self-organized patterning by diffusible factors: roles of a community effect

International audienceFor decades, scientists have sought to elucidate self-organized patterning in development. One of the key questions in animal development is how a group of cells of one type keeps its identity and differentiates co-ordinately while surrounded by others. It has been shown that in certain cases, cells interact with their neighbours by diffusible factors in order to establish and maintain a common identity. This developmental process is called a community effect. In this work, we examine the dynamics of a community effect in space and investigate its roles in two other processes of self-organized patterning by diffusible factors: Turing's reaction-diffusion systems and embryonic induction by morphogens. Our major results are the following. First, we show that, starting from a one-dimensional model with the simplest feedback loop, a community effect spreads in an unlimited manner. Second, this unrestricted expansion of a community effect can be avoided by additional negative feedback. In Turing's reaction-diffusion system with a built-in community effect, if induction is localized, sustained activation also remains localized. Third, when a simple cross-repression gene circuitry is combined with a community effect loop, the system self-organizes. A gene expression pattern with a well-demarcated boundary appears in response to a transient morphogen gradient. Surprisingly, even when the morphogen distribution eventually becomes uniform, the system can maintain the pattern. The regulatory network thus confers memory of morphogen dynamics