Abstract
In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.
Highlights
For the reasons of their adoption and a short list, see [38,39,40]. They range from the basic one, e.g. for the storage of databanks information in alternative formats that can improve traditional flat-file management, and for the description and archiving of results of main analysis tools, to the most complex, like those used in specialized knowledge domains (e.g., the Polymorphism Markup Language (PML) [41] that has been developed as a common data exchange format to overcome the heterogeneity of SNPs databases
In this paper we reviewed some of principal concepts that, in our opinion, will characterize the future of Systems Biology and of interdisciplinary research in biomedicine
A key point emerging from this review is the characterization and definition of complex biomedical system
Summary
New “omics” technologies applied to molecular genetics analysis are producing huge amounts of raw data. There is a great motivation, within the area of Computational Systems Biology, to fully define and propagate all sources of uncertainty in model-based reasoning, with reference to the genetic, biochemical and cellular mechanisms initiating and regulating fundamental biological processes These systems are non-linear, nonsteady state, and contain many unknown parameters. For the reasons of their adoption and a short list, see [38,39,40] They range from the basic one, e.g. for the storage of databanks information in alternative formats that can improve traditional flat-file management, and for the description and archiving of results of main analysis tools, to the most complex, like those used in specialized knowledge domains (e.g., the Polymorphism Markup Language (PML) [41] that has been developed as a common data exchange format to overcome the heterogeneity of SNPs databases. This improvement is achieved using, inside the membranes of τ-DD, a modified tau-leaping algorithm, which gives the possibility to simulate the time evolution of every volume as well as that of the entire system
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