Showing papers in "Transactions on Computational Systems Biology in 2006"

Gene regulation in the pi calculus: simulating cooperativity at the lambda switch

Abstract: We propose to model the dynamics of gene regulatory networks as concurrent processes in the stochastic pi calculus. As a first case study, we show how to express the control of transcription initiation at the lambda switch, a prototypical example where cooperative enhancement is crucial. This requires concurrent programming techniques that are new to systems biology, and necessitates stochastic parameters that we derive from the literature. We test all components of our model by exhaustive stochastic simulations. A comparison with previous results reported in the literature, experimental and simulation based, confirms the appropriateness of our modeling approach.

A compositional approach to the stochastic dynamics of gene networks

Abstract: We propose a compositional approach to the dynamics of gene regu-latory networks based on the stochastic π-calculus, and develop a representation of gene network elements which can be used to build complex circuits in a transparent and efficient way. To demonstrate the power of the approach we apply it to several artificial networks, such as the repressilator and combinatorial gene circuits first studied in Combinatorial Synthesis of Genetic Networks [1]. For two examples of the latter systems, we point out how the topology of the circuits and the interplay of the stochastic gate interactions influence the circuit behavior. Our approach may be useful for the testing of biological mechanisms proposed to explain the experimentally observed circuit dynamics.

A graphical representation for biological processes in the stochastic pi-calculus

Abstract: This paper presents a graphical representation for the stochastic π-calculus, which is formalised by defining a corresponding graphical calculus. The graphical calculus is shown to be reduction equivalent to stochastic π, ensuring that the two calculi have the same expressive power. The graphical representation is used to model a couple of example biological systems, namely a bistable gene network and a mapk signalling cascade. One of the benefits of the representation is its ability to highlight the existence of cycles, which are a key feature of biological systems. Another benefit is its ability to animate interactions between system components, in order to visualise system dynamics. The graphical representation can also be used as a front end to a simulator for the stochastic π-calculus, to help make modelling and simulation of biological systems more accessible to non computer scientists.

Simulating bacterial transcription and translation in a stochastic π calculus

Abstract: Stochastic simulation of genetic networks based on models in the stochastic π-calculus is a promising recent approach. This paper contributes an extensible model of the central mechanisms of gene expression 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.

Property-Driven statistics of biological networks

Abstract: An analysis of heterogeneous biological networks based on randomizations that preserve the structure of component subgraphs is introduced and applied to the yeast protein-protein interaction and transcriptional regulation network. Shuffling this network, under the constraint that the transcriptional and protein-protein interaction subnetworks are preserved reveals statistically significant properties with potential biological relevance. Within the population of networks which embed the same two original component networks, the real one exhibits simultaneously higher bi-connectivity (the number of pairs of nodes which are connected using both subnetworks), and higher distances. Moreover, using restricted forms of shuffling that preserve the interface between component networks, we show that these two properties are independent: restricted shuffles tend to be more compact, yet do not lose any bi-connectivity. Finally, we propose an interpretation of the above properties in terms of the signalling capabilities of the underlying network.