CICT: A Novel Framework for Biomedical and Bioengineering Application

In 2013, Computational Information Conservation Theory (CICT) confirmed Newman, Lachmann and Moore's result (in 2004), generating analogous example for 2-D signal (image), to show that even the current, most sophisticated instrumentation system is completely unable to reliably discriminate so called "random noise" from any combinatorially optimized encoded message, which CICT called "deterministic noise". To grasp a more reliable representation of experimental reality and to get stronger physical and biological system correlates,researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergistically articulated by natural coupling. CICT approach brings classical and quantum information theory together in a single framework, by considering information not only on the statistical manifold of model states but also on the combinatorial manifold of low-level discrete, phased generators and empirical measures of noise sources, related to experimental high level overall perturbation. As an example of complex system (hirarchical heterogenous multi-scale system) with important implications, we consider classical relativistic electrodynamics, applied to biological system modeling (e.g. fullwave electromagnetic modeling of brain waves). CICT approach can offer an effective and convenient "Science 2.0" universal framework to develop innovative application and beyond, towards a more sustainable economy and wellbeing, in a global competition scenario.