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  • Publicado : 1 de mayo de 2011
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Josue Lechuga Martinez
Modelling the Dynamics of Biosystems
A triggering event for the speed-up of the convergence between life sciences and computer sciences as well as the new development in bioinformatics is the paradigm shift in biological investigation. Bioinformatics was first defined by Hwa Lim as the study of the information content and information flow in biologicalprocesses and systems. The new definition developed by the NCBI is the science in which biology and computer science join together to ease new biological discovery and to define new computational paradigms inspired by living systems.
Systems biology is defined as the building of models of biological systems and the tuning/validating them via experiments that provide feedback. It is explicitly addressingthe information flow that governs the whole behavior of systems and assumes that thousands of simultaneous threads of computations are active in a system. The effect of an interaction between the components of a system can change the future behavior of the whole system.
Computational developments are developed and used to model and study molecular networks. The main ones are biochemical kineticmodels, generalized models of regulation, functional object-oriented databases, exchange languages, approaches on formal methods, and integrated frameworks with GUI. The biochemical kinetic models describe different molecular systems from a biological perspective; these use continuous, mass-action differential equations or stochastic models to simulate and analyze molecular pathways. Generalizedmodels of regulation describe and simulate gene regulatory circuits, using binary Boolean networks; these approaches allow the study of large networks. Functional object-oriented databases store information on molecular pathways, uses sophisticated object-oriented schemas as a hierarchical view of molecular entities and graph-based visualization tools. Exchange languages promote the integrationof models and tools from various sources; they currently often lack in expressivity and are not easily readable. Approaches based on formal methods are used for representation, simulation and analysis of metabolic pathways; statecharts provide a rich and expressive language; they are limited in their ability either to handle quantitative data for building and analyzing the model. Integratedframeworks with GUI promote exchange and integration of independent and separate solutions; develop extensive databases equipped with sophisticated tools for visualization and querying. In conclusion, each of these approaches captures some of the information, but none of them provides both a comprehensive, quantitative, dynamic and biologist-friendly language and serious tools for analysis andsimulation.
Process algebras for mobility may encompass most of the limitations of the approaches already mentioned. Mimicking the description of mobile and distributed systems in a biological domain, it can be stated that processes are the biological components.
π calculus was the first mobile process algebra, now it models rigorously mobile systems, they are made up of few operators to composeelementary actions. These operators are: sequentialisation (α.P), parallel composition (P|Q), name declaration (v x), and recursion (rec x.P). Sequentialisation is that the atomic action α is the first that the process α.P can execute. The v operator in (vx)P declares x to be private to P. The parallel composition P|Q allows the processes P and Q to be executed independently of one another.The dynamic behavior of systems is expressed in terms of transition systems, oriented and labeled graphs where the nodes are the states of the system. Process algebras have been extensively used in the computer science community for the specification and verification of concurrent and mobile systems. An important property of mobile process algebras is compositionality. The meaning or the...
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