Computational biology aims at disentangling the complexity of biological phenomena by providing tools/algorithms to analyse experimental data or models to predict outcomes of the biological system based on experimental and clinical data. Bioinformatics tools are used to analyse and interpret large scale experimental data and to distil useful information necessary to set up mathematical/computational models. In turn, computational models replicate the dynamics of complex biological systems thus offering in silico evaluations of a hypothesis. Models can be used to predict changes in the behaviour of a system over time and further unravel the complexity of the phenomena under study. Together, these methodologies have the potential to reshape our understanding of biology at all levels of details eventually allowing the optimization of existing therapies or even the formulation of new ones. In this article, we present succinct and informal descriptions of the main approaches or frameworks underlying most mathematical and computational methods currently used in quantitative studies of biological systems.
Castiglione, F., Mancini, E., Pedicini, M., Jarrah, A.S.M.A. (2019). Quantitative Modelling Approaches. In M.G. Shoba Ranganathan (a cura di), Encyclopedia of Bioinformatics and Computational Biology (pp. 874-883). Amsterdam : Elsevier [10.1016/B978-0-12-809633-8.20454-8].
Quantitative Modelling Approaches
Castiglione, Filippo;Pedicini, Marco;Jarrah, Abdul Salam Mohammad Aref
2019-01-01
Abstract
Computational biology aims at disentangling the complexity of biological phenomena by providing tools/algorithms to analyse experimental data or models to predict outcomes of the biological system based on experimental and clinical data. Bioinformatics tools are used to analyse and interpret large scale experimental data and to distil useful information necessary to set up mathematical/computational models. In turn, computational models replicate the dynamics of complex biological systems thus offering in silico evaluations of a hypothesis. Models can be used to predict changes in the behaviour of a system over time and further unravel the complexity of the phenomena under study. Together, these methodologies have the potential to reshape our understanding of biology at all levels of details eventually allowing the optimization of existing therapies or even the formulation of new ones. In this article, we present succinct and informal descriptions of the main approaches or frameworks underlying most mathematical and computational methods currently used in quantitative studies of biological systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
