integrative_systems_biology_biologie_intégrative_bio-informatique_manuel_gea_centrale-sante

BMSystems>Highlights>Our approach>Approach 3/6

Prerequisites to meeting this challenge
*Generating new knowledge and applying it efficiently.* To successfully meet this challenge we shall have to abandon the classical Cartesian framework and use complexity as a tool rather than fighting against complexity. A better understanding of biological systems poses four major challenges: The identification, classification and management of the knowledge generated by multiple research teams encompassing a wide variety of nationalities, languages and scientific specialties. The development and implementation of information technologies capable of collecting, structuring and manipulating widely dispersed and heterogeneous data. The capacity to integrate and extract understanding from this apparent complexity without the introduction of a-priori bias, and, The mobilisation of scientific and medical multi-disciplinarian competences to directly test the biological validity of this newly generated knowledge model. Information is not synonymous with knowledge. However, knowledge can be generated by integrating a wide variety of information. Thus, only forms of wide spectrum analyses, capable of integrating non-linearly related complex datasets and produce biological models that provide answers sufficiently detailed to then identify credible targets and drugs will succeed. Bio-Modeling Systems has risen to this challenge.
Predictive Integrative biology. How ?
Biological systems are of a non-linear and integrative nature. The path leading from genes to physiological functions is characterised by at least four series of demultiplications, each of which is associated with discontinuities. Experience demonstrates that, in such a context, a purely computer-driven approach is very unlikely to produce coherent exploitable results. Here, to generate knowledge that can then be directly exploited, a dual approach is required. CADI™ (Computer-Assisted Deductive Integration) is the name we have given to the suite of technologies we have developed to this effect. The efficacy of our methodologies and their capacity to rapidly generate directly exploitable innovative therapeutic applications is demonstrated by the number of independently validated biological models of complex pathologies we have been able to generate (see “our Models”)

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