Linear and Nonlinear Modes and Data Signatures in Dynamic Systems Biology Models

The particulars of stimulus–response experiments performed on dynamic biosystems clearly limit what one can learn and validate about their structural interconnectivity (topology), even when collected kinetic output data are perfect (noise-free). As always, available access ports other limitations rule. For linear systems, exponential modes, visible hidden, play an important role in understanding limitations, embodied we call dynamical signatures the data. We show here how to circumscribe analyze modal response compartmentalizing model structures—so that analysis be used constructively systems biology mechanistic building—for some nonlinear (NL) as well biosystems. do this by developing exploiting basis for hypothetical (perfect) input–output (I-O) associated with a (mechanistic) model—one includes inputs outputs explicitly. methodology establishes dimensionality (size complexity) from particular I-O datasets; helps select among multiple candidate models (model distinguishability); designing new extract “hidden” structure; simplify (reduce) essentials. These tools introduced NL enzyme-regulated protein–protein interaction via normal mode (NNM) quasi-steady state approximation (QSSA) analyses unified invariant 2-dimensional manifolds phase space, properties similarly informative dominant properties. Some automation these highly technical aspects biomodeling is also introduced.