By Yoram Vodovotz Ph.D., Gary An M.D. (auth.), Yoram Vodovotz, Gary An (eds.)
The hassle in attaining powerful translation of uncomplicated mechanistic biomedical wisdom into powerful therapeutics, is the best problem in biomedical study. Nowhere is that this extra obvious than within the reductionist ways to realizing and manipulating the intense inflammatory reaction within the settings of sepsis, trauma/hemorrhage, wound therapeutic, and comparable procedures. This publication discusses complicated platforms and computational biology equipment and techniques that experience complex sufficiently to permit for wisdom iteration, wisdom integration, and medical translation within the settings of complicated illnesses regarding the inflammatory reaction. Well-regulated, self-resolving irritation is important for the proper verbal exchange and determination of an infection and trauma, and for upkeep of right body structure and homeostasis. by contrast, self-sustaining irritation drives the pathobiology of the aforementioned illnesses. it really is now more and more well-known that controlling and reprogramming irritation on the way to benefit from this evolutionarily-conserved approach is most popular to easily abolishing indiscriminately.
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Extra resources for Complex Systems and Computational Biology Approaches to Acute Inflammation
For example, in a recent publication, statistical tools were compared the parameter ensembles of a computational models fit to animals that survived a septic challenge to the distribution obtained from a model fit to data of animals that died . Analyzing parameter distributions rather than data provides insight on mechanisms explaining the difference in outcome, not merely a description of what is different. Although both the “data world” and the “parameter world” can be used to produce predictive models, only a parameter-based description can offer insight on mechanistically motivated therapeutic strategies that may alter outcome.
Interventions in acute care, pharmacologic or physical organ support, interact directly with mechanisms. Indeed, practitioners constantly select therapies based on perceived mechanisms. Therefore, a mechanistically based decision support system suggesting which mechanisms are particularly important in a given individual at a given time might enhance data interpretation, diagnostic ability, and 24 G. Clermont treatment selection . The recent rise of control-based approaches in cancer pharmacotherapy of glucose control in type I diabetes offers a second application for equation-based models applied to personalized care.
Representation of Spatial Relationships Agent-based models (ABMs) readily incorporate spatial relationships, be they manifest in an actual spatial topology or a topological interaction neighborhood linking 34 S. Christley and G. An individual agents. In an ABM agent, behavior is driven by interactions determined by agent neighborhoods defining the communication and interaction network for each agent. An agent neighborhood can be represented as a two-dimensional square grid (very common), a three-dimensional cubic space [7, 12], two- or three-dimensional hexagonical space [18, 19] or as a network topology, as a neighborhood does not necessarily mean physical proximity but rather the configuration of some set of other agents with whom an agent can interact.