In application, observables informing on composite aspects of dynamical systems are frequently usedfor prognosis, on such basis deciding on external interference for eventually optimizing readout on asystems level. Medical care lives along these lines: Clinical phenotyping diagnoses a pathophysiology,assigns on the basis of empirical estimates from background data disease progression characteristicsfollowed by deciding on optimal treatment, all being derived on a cohort level. Shortcomings areevident, expressed as attrition in drug R&D and Number Needed to Treat in clinical practice. Whileutilization of big data (both on a molecular (omics) as well as clinical data side) is considered as stepforward in improving precision we see fundamental conceptual issues, specifically when it comes tooptimizing drug response in the realm of Precision Medicine.Observables used for decision support, be it molecular or clinical parameters, ground in moleculararchitecture (a pathophysiology) implicitly encoding internal (age, genetic predisposition, etc.) andexternal factors (behavioral aspects, drug mechanism of action, etc.). In our view such architecture is tobe expressed as composition of molecular processes, in hierarchical organization developing higherorder function as source for higher order readout (hence, observables). We propose a framework forlinking observables on the level of clinical state-trajectory maps and feasible molecular architecture(process-of-processes) transitions as structure preserving functors of respective categories.Such approach promises improved analytical access for estimating drug response in clinical R&D, and isopen for discussion in general dynamical systems optimization challenges.
I received my PhD in Molecular Biology from the University of Vienna in 1995, and the venia docendi in
Structural Biology in 2004 associated with the Institute for Theoretical Chemistry, University of Vienna.
After PostDoc positions exploring foundations of molecular self- assembly in Vienna, Bologna and Los
Alamos I co-founded Vienna-based SME emergentec biodevelopment GmbH in 2002. With initial focus
on establishing big data integration and analytics platforms we turned toward constructional principles
of higher order function, specifically composition in hierarchical organization, on this basis developing
application scenarios of object-in-context systems in reverse engineering and technical designs.