Fig. 1

CICD engineering approach: a Representation of the multi-dimensional cause and effect dynamic interaction network. Multiple cell and cytokine relationships cause the effect of dynamic population changes in the measurements of the peripheral blood biomarkers of immune function over time. The CICD math model assumes that the rate of change of the effects, i.e. the observed biomarkers’ populations, is equal to the sum of all causes, i.e. the biomarker relationships that produce a change in a biomarker population. With this time series data CICD is able to solve the inverse problem to quantify specific active relationships that manifest in the oscillations of biomarker concentrations. b Foundational architecture for CICD analysis process. Daily blood draws were performed to obtained daily measurements of both cell and cytokine immune biomarkers. CICD’s system identification process then creates a system characterization matrix to provide a dynamic system math model with biomarker data, predator–prey equations, a truncated Kolmogorov–Gabor polynomial and thousands of possible biomarker relationships. Reverse engineering analysis uses a Singular Value Decomposition algorithm (SVD) to solve the inverse problem and identify a solution profile of the active biomarker relationships. With CICD modeling capabilities, the complexity of the immune system is mathematically quantified through thousands of possible interactions between multiple biomarkers