Computing Cell-Types and Their Lineages Via Endogenous Network Theory

The rapid accumulating large "omics" data in biological sciences provide more information on cancer but also require a new mechanistic underpinning to integrate for rationalizing cancer complexity and to identify collective and individual roles of these players. A unifying and quantitative theory was proposed that cancer is a robust state, or a group of states, of an endogenous molecular-cellular network. This state is not optimized for the whole organism. By examining the nonlinear stochastic dynamics of the core network in the phase space, robust states corresponding to normal physiological and abnormal pathological phenotypes, including cancer, emerge naturally. The nonlinear dynamical model of the network leads to a more encompassing framework integrating existing biological knowledge than the currently prevailing linear-additive thinking in cancer research. It was applied so far to prostate, hepatocellular, gastric cancers, CRC and a leukemia. The quantitative models so constructed from individual molecular biology experiments recapitulate known clinical observations and predict new phenomena. The endogenous molecular-cellular network theory may offer an example of carrying quantitative inquiring spirit beyond statistical approaches: while quantitative approaches can address individual cases, there must be general rules/laws to be discovered in biology and medicine. An overview of our more than 10 years effort will be presented.

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