Stochastic Simulation of Process Calculi for Biology

Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

Scaling up Systems Biology: Model Construction, Simulation and Visualization

Being a multi-disciplinary field of research, Systems Biology struggle to have a common view and a common vocabulary, and inevitably people coming from different backgrounds see and care about different aspects. Scientists have to work hard to comprehend each other and to take advantage of each others work, but on the other hand they can provide unexpected and beautiful new insight to the problems we have to face, enabling cross-fertilization among different disciplines. However, Systems Biology scientists all share one main goal, in the end: comprehend how a system as complex as a living creature can work and exists. Once we really understand how and why a biological system works, we can answer other important questions: can we fix it when it breaks down; can we enhance it and make it more resistant, correct its flaws; can we reproduce its behaviour and take it as inspiration for new works of engineering; can we copy it to make our everyday work easier and our human-created systems more reliable. The contribution of this thesis is to push ahead the current state of art in different areas of information technology and computer science as applied to systems biology, in a way that could lead, one day, to the understanding of a whole, complex biological system. In particular, this thesis builds upon the current state of art of different disciplines, like programming languages theory and implementation, parallel computing, software engineering and visualization. Work done in these areas is applied to Systems Biology, in the effort to scale up the dimension of the problems that is possible to tackle with current tools and techniques

Modeling and simulating bio-molecule diffusion in non-homogeneous solutions. Diffusive spatial effects on chaperone-assisted protein folding: a case study Modeling and simulating bio-molecule diffusion in non-homogeneous solutions. Diffusive spatial effec

Abstract In this report we present a new stochastic algorithm to simulate reaction-diffusion systems and its application to the simulation of diffusive spatial effects on the chaperone-assisted protein folding in cytoplasm

Stochastic simulation of the spatio-temporal dynamics of reaction-diffusion systems: the case for the bicoid gradient

Reaction-diffusion systems are mathematical models that describe how the concentrations of substances distributed in space change under the influence of local chemical reactions, and diffusion which causes the substances to spread out in space. The classical representation of a reaction-diffusion system is given by semi-linear parabolic partial differential equations, whose solution predicts how diffusion causes the concentration field to change with time. This change is proportional to the diffusion coefficient. If the solute moves in a homogeneous system in thermal equilibrium, the diffusion coefficients are constants that do not depend on the local concentration of solvent and solute. However, in nonhomogeneous and structured media the assumption of constant intracellular diffusion coefficient is not necessarily valid, and, consequently, the diffusion coefficient is a function of the local concentration of solvent and solutes. In this paper we propose a stochastic model of reaction-diffusion systems, in which the diffusion coefficients are function of the local concentration, viscosity and frictional forces. We then describe the software tool Redi (REaction-DIffusion simulator) which we have developed in order to implement this model into a Gillespie-like stochastic simulation algorithm. Finally, we show the ability of our model implemented in the Redi tool to reproduce the observed gradient of the bicoid protein in the Drosophila Melanogaster embryo. With Redi, we were able to simulate with an accuracy of 1% the experimental spatio-temporal dynamics of the bicoid protein, as recorded in time-lapse experiments obtained by direct measurements of transgenic bicoidenhanced green fluorescent protein