Title: From Systems Biology to Systems Pathology: A New Subspecialty in Diagnostic and Personalized Medicine
Volume: 8
Issue: 2
Author(s): Michael J. Donovan, Jose Costa and Carlos Cordon-Cardo
Affiliation:
Keywords:
Biomedical modeling, biotechnology industry, high-dimensional genome analysis, omics biomarkers, postgenomics medicine, systems biology, systems pathology
Abstract: There has been a fundamental shift in biopharmaceutical research from a linear, systematic investigation to a more whole organism, systems-oriented analysis program. The impetus behind this change is an acknowledgment that the current novel target and drug discovery model was flawed due in part to an overly simplified view of disease biology, i.e., a unidirectional path from one gene to one protein to one mechanism of action. Fortunately, recent technological advancements in whole genome analysis, DNA, RNA, proteome sequencing and mathematical modeling have permanently altered the target discovery landscape. These improvements in high throughput knowledge-creation have facilitated the multi-dimensional interrogation of disease in the context of the whole organism. One of the recently described mechanisms for helping to bridge these investigational studies with clinical medicine has been through the introduction of an analytic modeling platform known as “systems pathology”. The derived systems-based pathology models use the patients own clinical data and intact tissue specimens to construct a baseline phenotype for defining a clinical risk state. These biologic-quantitative models also provide a biomarker profile which can be linked to treatment and health outcomes. This paper presents the rationale and implementation of systems biology in the area of translational medicine and a practical clinical application, i.e., systems pathology. By incorporating high dimensional genome analysis and disease modeling efforts such as systems pathology, we illustrate how such advancements have helped bring systems biology to the clinic but have also served to make the medical decision and treatment algorithms a more patient-specific paradigm.
