An overview of the translational dilemma and the need for model-based precision medicine

The translational dilemma, that is, the difficulty in achieving effective translation of basic mechanistic biomedical knowledge into effective therapeutics, remains the greatest challenge in biomedical research. Nowhere is this more apparent than in the reductionist approaches to understanding and manipulating the acute inflammatory response in the settings of sepsis, trauma/hemorrhage, wound healing, and related processes such as host-pathogen interactions. Despite numerous advances in defining novel molecules, pathways, and mechanisms, these advances remain, in general, in scientific silos that are poorly connected and lacking interoperability, reflected in the dearth of available therapeutics for these deadly diseases. Recently, an array of computational informatics methods falling under the rubric of "machine learning" or the more colloquial "Artificial Intelligence" have come to the fore. These methods, while representing a step forward from the reductionist paradigm they are supplanting, also suffer from various pitfalls. We suggest that mechanistically oriented complex systems and computational biology methods and approaches have advanced sufficiently to allow for knowledge generation, knowledge integration, and clinical translation in the settings of complex diseases related to the inflammatory response and could be integrated with machine learning approaches. This book brings together the current state of the art in complex systems and computational biology as applied to inflammatory diseases and lays out a paradigm for Model-Based Precision Medicine as a distinct pathway from what is commonly termed "Precision Medicine.