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Table 2 Comparison of current math models versus CICD model

From: Cancer immune control dynamics: a clinical data driven model of systemic immunity in patients with metastatic melanoma

  Current modeling approaches CICD modeling
Modeling equations Ordinary differential equations Ordinary differential equation
Delayed differential equations
Partial differential equations
Stochastic differential equations
Agent based models
Cellular automata
Multiscale/hybrid
Equations Multiple combinations One large matrix equation
Parameters Multiple, limited by availability Unlimited
Measured experimentally and/or estimated theoretically All are computed
All possible time varying parameters are contained within the unknown value in the Kolmogorov–Gabor Polynomial
Biomarkers Limited, most less than 10 Expandable, currently 50
Data Measurement Measured experimentally and/or estimated theoretically Sequential daily blood draw
Number of Biomarker Relationships Limited by current knowledge Currently 28,605 including all biologically possible relationships
Biomarker Relationships Used Only known relationships are modeled All biologically possible relationships can be included
Results Deterministic or Probabilistic Deterministic
Generalized and/or Individualize relative to parameter and patient data availability Individualize to patient data measurements
  1. A side-by-side comparison highlights the advantages of the flexible CICD approach. These advantages include one simple expandable equation, no estimated parametrization, large numbers of biomarkers and multi-dimensional relationships can be included, and an individualized model of their own immune system generated directly from their own data. As compared to the generalized, parameter dependent modeling found in the current literature