Biology thrives on complexity, and yet our approaches to deciphering complex biologicalsystems have been simple, observational, reductionist, and qualitative. The observationalnature of biology may even seem self-evident, as expressed more than three centuries agoby Robert Hooke, whose work Micrographia of 1665 contained his microscopical investiga-tions that included the first identification of biological cells: “The truth is, the science of Nature has already been too long made only a work of the brain and the fancy. It is nowhigh time that it should return to the plainness and soundness of observations on materialand obvious things.”As we begin to observe, infer, and list the fundamental “parts” out of which biology iscreated, we cannot stop marveling at how these same components and their variants andhomologues interconnect, intertwine, and interact via universal principles that still remain tobe fully deciphered. To unravel this biological complexity, of which we only have a hint sofar, it has become necessary to develop novel tools and approaches that augment andrigorously formalize those human reasoning processes—tools that until now could be usedfor only tiny toy-like subsystems in biology. To this end, the anticipated computational systems biology tools aim to draw upon con-structive mathematical approaches developed in the context of dynamical systems, kineticanalysis, computational theory, and logic. The resulting toolkit aspires to build powerful sim-ulation, analysis, and reasoning facilities that can be used by working biologists for multiplepurposes: in making sense of existing data, in devising new experiments, and ultimately in understanding functional properties of genomes, proteomes, cells, organs, and organisms.If this ambitious program is to ultimately succeed, there are certain critical components thatrequire special attention of computer scientists and applied mathematicians. This chapterstudies the nature of these components, software architecture for integrating them, and illus-trative examples of how such an integrated system may function in practice

Mishra, B., Antoniotti, M., Paxia, S., Ugel, N. (2005). Simpathica: A Computational System Biology Tool within the VALIS Bioinformatics Environment, in Computational Systems Biology. In A. Kriete, R. Elis (a cura di), Computational Systems Biology (pp. 79-102). Academic Press [10.1016/B978-012088786-6/50024-1].

Simpathica: A Computational System Biology Tool within the VALIS Bioinformatics Environment, in Computational Systems Biology

Antoniotti, M
Membro del Collaboration Group
;
2005

Abstract

Biology thrives on complexity, and yet our approaches to deciphering complex biologicalsystems have been simple, observational, reductionist, and qualitative. The observationalnature of biology may even seem self-evident, as expressed more than three centuries agoby Robert Hooke, whose work Micrographia of 1665 contained his microscopical investiga-tions that included the first identification of biological cells: “The truth is, the science of Nature has already been too long made only a work of the brain and the fancy. It is nowhigh time that it should return to the plainness and soundness of observations on materialand obvious things.”As we begin to observe, infer, and list the fundamental “parts” out of which biology iscreated, we cannot stop marveling at how these same components and their variants andhomologues interconnect, intertwine, and interact via universal principles that still remain tobe fully deciphered. To unravel this biological complexity, of which we only have a hint sofar, it has become necessary to develop novel tools and approaches that augment andrigorously formalize those human reasoning processes—tools that until now could be usedfor only tiny toy-like subsystems in biology. To this end, the anticipated computational systems biology tools aim to draw upon con-structive mathematical approaches developed in the context of dynamical systems, kineticanalysis, computational theory, and logic. The resulting toolkit aspires to build powerful sim-ulation, analysis, and reasoning facilities that can be used by working biologists for multiplepurposes: in making sense of existing data, in devising new experiments, and ultimately in understanding functional properties of genomes, proteomes, cells, organs, and organisms.If this ambitious program is to ultimately succeed, there are certain critical components thatrequire special attention of computer scientists and applied mathematicians. This chapterstudies the nature of these components, software architecture for integrating them, and illus-trative examples of how such an integrated system may function in practice
Capitolo o saggio
Systems Biology, Computational Biology, software tool
English
Computational Systems Biology
Kriete, A; Elis, R
2005
978-012088786-6
Academic Press
79
102
Mishra, B., Antoniotti, M., Paxia, S., Ugel, N. (2005). Simpathica: A Computational System Biology Tool within the VALIS Bioinformatics Environment, in Computational Systems Biology. In A. Kriete, R. Elis (a cura di), Computational Systems Biology (pp. 79-102). Academic Press [10.1016/B978-012088786-6/50024-1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/28639
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