including the capability to produce pathway-controlling enzymes, is one of the most challenging aspects of making such simulations relevant.

Coupling models will require a computational platform that can support multiple interacting components that can be combined into larger and more complex models. Such a platform must not only support parallel operation of the analytical processes but also allow assembly of hierarchical simulation and information structures, dynamically built, exploited, modified when possible on the basis of individual patient data and statistical aggregates thereof, and abandoned when no longer effective. At the supporting levels, multiple processing alternatives will exist. Specific, detailed simulations will provide the most specific and current results. Cached results can greatly reduce the computational effort for repeated sub-analyses. Where no analytical methods exist, results from biological or clinical trials or clinician assessments can be provided. Search and interpretation can provide yet another set of inputs. Being able to operate with a variety of computational paradigms in one setting can greatly enhance collaboration among communities that have similar objectives but that now ignore each other. Yet another challenge in modeling is building multilevel models that can successfully couple highly detailed physiologic models to the much looser clinical “models” that typically are based more on phenomenological relationships than on true underlying causes.

Finally, keeping records of predictions and actual patient outcomes will allow incremental tuning of the approach. It will take much experience as well as careful approaches to do so in a way that converges on a stable and more optimal outcome. The actual determination of patient treatment will remain in the hands and minds of the clinician. But the feedback that can be provided by bringing data collections, metabolic models, and their processing to an interactive care setting is essential to extract value out of the many technology investments that are in process or being planned.

Box 5.3 describes some of the technical research challenges for modeling organized by quadrant.


The technical definitions of automation allow for multiple forms, depending on the degree of intelligence and autonomy exhibited. Systems that are completely automatic and that can be trusted to work properly without any need for human oversight or attention have proven to be effective and valuable. Systems that require human oversight or control, which in actuality is almost any complex system, fall under the category

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