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Multifactorial disease: network disease

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With the tremendous advances in the Life Sciences one should have expected much of disease to be understood and perhaps even amenable to definitive therapy. Instead, most diseases are only understood in fairly vague terms, and therapies are ameliorating, palliative, or just buying time. The pharmaceutical ...

With the tremendous advances in the Life Sciences one should have expected much of disease to be understood and perhaps even amenable to definitive therapy. Instead, most diseases are only understood in fairly vague terms, and therapies are ameliorating, palliative, or just buying time. The pharmaceutical industry seems to be running out of successful block buster drugs. Since humanity has been at this problem for over a century now, waiting for improvement to emerge, may not be the sole advisable strategy.
A different strategy may be to consider whether we have been working along the right paradigm. Historically starting by looking at physiology, i.e. the entire patient, the study of disease has shifted to the world of molecules, genes. This has been somewhat successful as drugs are molecules, and many diseases are caused by molecules, either by ‘wrong’ molecules because of gene mutations, or because of molecules entering the body from the environment. Functional genomics has led to an apparently unlimited potential: we can now measure almost all molecules that might be relevant for disease. However, this does not seem to help.
Biological function is always produced by networks of molecules, where the functional properties emerge in the nonlinear interactions between the molecules in the network. Dysfunction, such as in disease, is largely due to a different mode of functioning of the network. Single-molecules targeted research will overlook this: it reveals the trees but not the forest.
The topic addressed here stems from the thesis that the most important diseases of mankind are multifactorial and hence in fact network diseases, or systems biology diseases. Network problems should be analyzed using network methodologies, i.e. by systems biology.
Taking a number of diseases, diagnostic tools and therapies as examples this review series will examine whether indeed the relevant diseases are systems biology diseases and how they could then be best addressed by systems biology and systems biomedicine.


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