The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power required by these simulations often exceeds that available on common desktop computers and thus expensive high performance computing solutions are required. An emerging alternative is represented by General-Purpose scientific computing on Graphics Processing Units (GPGPU), which offers the power of a small computer cluster at a cost of around $400. Computing with a GPU requires the development of specific algorithms, since the programming paradigm substantially differs from traditional CPU-based computing. In this paper, we review some recent efforts in exploiting the processing power of GPUs for the simulation of biological systems.
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Proteins have the ability to bind other molecules and to consequently change shape, i.e., interaction capabilities. This basic mechanism enables complex cellular functions that make life possible. Upon this observation, the idea of a protein-based computing device is fascinating, but using real proteins in a lab to execute programs is not yet feasible. Here, our purpose is to explore the computational power of a similar device by simulating protein behaviour through a formal representation. In particular, we refer to the programming language BlenX, designed on the basic protein mechanisms, to sketch the idea of using proteins to encode term rewriting systems.
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Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ODEs or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this paper we present a survey on the progresses of such parallelization efforts describing the most promising results so far obtained.
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BlenX is a programming language explicitly designed for modeling biological processes inspired by Beta-binders. The actual framework assumes biochemical interactions being exponentially distributed, i.e., an underlying Markov process is associated with BlenX programs. In this paper we relax this condition by providing formal tools for managing non-Markovian processes within BlenX.
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This paper proposes the Effective Index, a formal tool to support decision processes in drug discovery. The Effective Index is based on concurrency theory and process calculi to describe incrementally complex biological systems and on Markov process theory to handle quantitative information. A running case study concerning the pathways and the drugs related to hypertension exploits the approach.
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Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri Nets theory, and some other by stochastic processes. A most recent approach consists in interpreting the living entities as terms of process calculi where the behavior of the represented systems can be inferred by applying syntax-driven rules. A comprehensive picture of the state of the art of the process calculi approach to biological modeling is still missing. This paper goes in the direction of providing such a picture by presenting a comparative survey of the process calculi that have been used and proposed to describe the behavior of living entities.
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