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Mission and Scope:

​We, at the Center for Network Medicine (CNM) continue to strive towards a goal of understanding the fundamental principles that allow eukaryotic cells to do their business, i.e., iteratively sense, decide, act and learn/adapt. We hypothesize that they do so through an ad hoc molecular interaction network whose architectural principles and computational rules are similar to those used in the layered communication systems in the engineering world. These rules enable noise reduction, error minimization, shielding and encapsulation of breakdowns, control of delays and state explosion, etc. Success in decoding these fundamental rules should not only enable us to complete and 'clean up' the existing incomplete and messy biological networks, but also use these network for simulating and predicting cell behavior. This will usher a new era in network-based diagnostic tools and network-resetting therapeutics for major diseases. On the flip side, knowing how a cell autonomously carries out its business will provide insights into designing new kinds of self-organizing networks (SON) and provide a novel paradigm (beyond artificial neural networks) for developing Artificial Intelligence (AI).

Benchmarking Success:

The proof of whether or not we are right in our thinking can come from two angles—

1) Engineering: The ability to emulate a cell, i.e., its heterogeneous systems design, a robust and flexible communication network, its self-organizing principles, its ability to tide over energy crisis, stressful periods, etc. that underwent iterative optimization during the process of evolution lasting billions of years must help solve some/many intractable problem(s) in engineering. Those include, but are not limited to-- power conservation to allow on-device computing, designig intelligent self-organizing networks (SONs) that are better at decision-making, learning and adaptation, develop artificial intelligence (AI) solutions that are closer to human intelligence (HI), etc. 

2) Medicine: Knowing the architecture and the behavior of the cell's signaling network, and the rules that impart such behavior should allow prediction of outcome, should enhance effectiveness and precision in both diagnostics and therapeutics. These insights should empower us to solve the hitherto unsolvable problems in medicine and biology, i.e., find 'cures' for the incurable (i.e., Darwinian diseases) like the autoimmune diseases and cancers. If we can find actionable therapeutic targets that are logical, invariant and able to re-set the diseased network to physiology and prevent the diseased cells from evolving. If we can deliver drugs that have high efficacy despite disease heterogeneity and population variation (noise).