2. How do I decompose a complex system intro tractable subsystems?
3. Given an understanding of subsystems, how can one integrate these data and models into an understanding of the system as a whole?
Can anyone give me some suggestions with examples?
Dear Sanjay, the ral nature of systems biology resides in the emphasis on the study of the RELATIONAL STRUCTURE of studied systems, thus the classical 'divide et impera' reductionist way does not hold. You must not fragment the system into 'tractable units' but do exactly the opposite : you must look for 'the presence of global relation structures partially independent of the costituent parts.
Thus the approaches are all the approaches aimed at studying correlation matrixes, graphs, complex networks, covariance matrixes that after all are different names for the same basic form of data.
In this real eigenvector/eigenvalue methods are very useful (depending on your tastes you can refer to them as SVD, SSA, PCA,, Complex Network Analysis) and the same can be said of all tose discrete mathematics methods that do not impose any to strong pre-defined mathematical structure to the data.
Dear Sanjay, the ral nature of systems biology resides in the emphasis on the study of the RELATIONAL STRUCTURE of studied systems, thus the classical 'divide et impera' reductionist way does not hold. You must not fragment the system into 'tractable units' but do exactly the opposite : you must look for 'the presence of global relation structures partially independent of the costituent parts.
Thus the approaches are all the approaches aimed at studying correlation matrixes, graphs, complex networks, covariance matrixes that after all are different names for the same basic form of data.
In this real eigenvector/eigenvalue methods are very useful (depending on your tastes you can refer to them as SVD, SSA, PCA,, Complex Network Analysis) and the same can be said of all tose discrete mathematics methods that do not impose any to strong pre-defined mathematical structure to the data.
Dear Sanjay,
I totally agree with Alessandro Giuliani. The reductionist approach is still present in many sciences and especially in biomedical research. Unfortunately, the global or integrated or holistic approach has difficulties to be used, both for the still poor knowledge of its logic, both for mental hesitation of the researcher. It is rather a philosophical approach in applying the scientific method, using a multidisciplinary or interdisciplinary tactic, in contrast to the analytic tradition or reductionism that tried to understand complex systems by analyzing the different components separately and studying their properties. Moreover, the need of the computer to process the data (often massive) makes this approach disliked by many, especially to older researchers. I strongly recommend a book, of simple and smooth reading, but that makes it very well the significance of the global approach to science. You'll find that universal laws pervade many different disciplines, unifying their logic of study. The book is: Linked: The New Science of Networks by A. L. Barabasi.
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