Personalized medicine beyond genomics: alternative futures in big data-proteomics, environtome and the social proteome.

Özdemir V1,2, Dove ES3, Gürsoy UK4, Şardaş S5, Yıldırım A6, Yılmaz ŞG7, Ömer Barlas I8, Güngör K7,9, Mete A9, Srivastava S10.

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Abstract

No field in science and medicine today remains untouched by Big Data, and psychiatry is no exception. Proteomics is a Big Data technology and a next generation biomarker, supporting novel system diagnostics and therapeutics in psychiatry. Proteomics technology is, in fact, much older than genomics and dates to the 1970s, well before the launch of the international Human Genome Project. While the genome has long been framed as the master or "elite" executive molecule in cell biology, the proteome by contrast is humble. Yet the proteome is critical for life-it ensures the daily functioning of cells and whole organisms. In short, proteins are the blue-collar workers of biology, the down-to-earth molecules that we cannot live without. Since 2010, proteomics has found renewed meaning and international attention with the launch of the Human Proteome Project and the growing interest in Big Data technologies such as proteomics. This article presents an interdisciplinary technology foresight analysis and conceptualizes the terms "environtome" and "social proteome". We define "environtome" as the entire complement of elements external to the human host, from microbiome, ambient temperature and weather conditions to government innovation policies, stock market dynamics, human values, political power and social norms that collectively shape the human host spatially and temporally. The "social proteome" is the subset of the environtome that influences the transition of proteomics technology to innovative applications in society. The social proteome encompasses, for example, new reimbursement schemes and business innovation models for proteomics diagnostics that depart from the "once-a-life-time" genotypic tests and the anticipated hype attendant to context and time sensitive proteomics tests. Building on the "nesting principle" for governance of complex systems as discussed by Elinor Ostrom, we propose here a 3-tiered organizational architecture for Big Data science such as proteomics. The proposed nested governance structure is comprised of (a) scientists, (b) ethicists, and (c) scholars in the nascent field of "ethics-of-ethics", and aims to cultivate a robust social proteome for personalized medicine. Ostrom often noted that such nested governance designs offer assurance that political power embedded in innovation processes is distributed evenly and is not concentrated disproportionately in a single overbearing stakeholder or person. We agree with this assessment and conclude by underscoring the synergistic value of social and biological proteomes to realize the full potentials of proteomics science for personalized medicine in psychiatry in the present era of Big Data.