Reverse engineering and identification in systems biology: strategies, perspectives and challenges.
The interplay of mathematical modelling with experiments is one of the central elements in systems biology. The aim of reverse engineering is to infer, analyse and understand, through this interplay, the functional and regulatory mechanisms of biological systems. Reverse engineering is not exclusive of systems biology and has been studied in different areas, such as inverse problem theory, machine learning, nonlinear physics, (bio)chemical kinetics, control theory and optimization, among others. However, it seems that many of these areas have been relatively closed to outsiders. In this contribution, we aim to compare and highlight the different perspectives and contributions from these fields, with emphasis on two key questions: (i) why are reverse engineering problems so hard to solve, and (ii) what methods are available for the particular problems arising from systems biology?
['Villaverde|Alejandro F|AF|', 'Banga|Julio R|JR|']
D001185:Artificial Intelligence D001331:Automation D001499:Bayes Theorem D057005:Bioengineering D001703:Biophysics D008390:Markov Chains D008954:Models, Biological D015233:Models, Statistical D009010:Monte Carlo Method D049490:Systems Biology
reverse engineering identification systems biology inference dynamic modelling