For Hepatitis C, mathematical modeling was used to quantitatively analyze the antiviral effect of interferon-alpha-2b (IFN) and the viral decline in patients during therapy.
Most diseases are multifactorial, involving complex network perturbations and interactions that occur across widely different scales of time and space within differing biological subsystems. Consequently, they show highly nonlinear dynamics that exceed our traditional ways of understanding. In order to harness this knowledge and to develop improved therapies and prevention strategies, new approaches must be developed that can integrate temporal dimensions and biological rhythms (e. g. chronotherapy according to circadian clocks).
During the last decade multidisciplinary approaches that combine the skills of mathematicians, physicists and engineers with those of traditional experimental biologists have started to tackle successfully the challenges of biological complexity through computational methods.
Mathematical modeling and computational tools have helped to develop systems approaches to display complex functional and regulatory networks describing the behavior of biological systems (Auffray et al., 2009; PMID: 19348689; Cesario and Marcus [Eds.] - Cancer Systems Biology, Bioinformatics and Medicine, Springer, ISBN: 978-94-007-1566-0). These techniques can now, indeed must, be applied to genuine and precisely defined clinical problems.
The development of drug resistance is a major and as it seems inevitable obstacle in cancer therapy. Drug resistance can be caused by many mechanisms, most prominently the overexpression and activation of transporters that pump drugs out of the target cells, and the acquisition and selection of mutations that obliterate drug efficacy.
As outlined above Systems Medicine has great potential to improve healthcare delivery and medical research and clinical practice. However, these benefits largely remain to be proven in practice. Therefore, a series of proof of principle studies will need to be designed that demonstrate the efficient application of Systems Medicine. The execution of these studies will not be part of CASyM, but we will inform their design, monitor ongoing relevant projects and assess the most promising medical fields, crucial for the acceptance and further implementation of Systems Medicine.
Therefore, such application areas will be carefully chosen to maximise feasibility and impact. Within CASyM, the selected areas will be elaborated by expert working groups as described in WP1 and will form part of the proposed roadmap.