Perspectives of a Systems Biology of the Brain: The Big Data Conundrum Understanding Psychiatric Diseases

Abstract

Psychiatric diseases provoke human tragedies. Asocial behaviour, mood imbalance, uncontrolled affect, and cognitive malfunction are the price for the biological and social complexity of neurobiology. To understand the etiology and to influence the onset and progress of mental diseases remains of upmost importance, but despite the much improved care for the patients, more then 100 years of research have not succeeded to understand the basic disease mechanisms and enabling rationale treatment. With the advent of the genome based technologies, much hope has been created to join the various dimension of -omics data to uncover the secrets of mental illness. Big Data as generated by -omics do not come with explanations. In this essay, I will discuss the inherent, not well understood methodological foundations and problems that seriously obstacle in striving for a quick success and may render lucky strikes impossible.

Key words

mental disease - brain imaging - complex genetics - epistemology

• References

• 1 Noble D. The Music of Life. Oxford University Press; USA: 2008
• 2 IDC 10 Classification of Mental and Behavioural Disorders Available from http://www.who.int/classifications/icd/en/bluebook.pdf
• 3 Petsko GA. Shadows on the wall. Genome Biol 2010; 11: 136
• 4 Insel TR, Sahakian BJ. Drug research: a plan for mental illness. Nature 2012; 483: 269
• 5 Westerhoff HV, Kell DB. The methodology of systems biology. Systems Biology: Philosophical Foundations. Elsevier; 2007. 23-70
• 6 Mewes HW, Wachinger B, Stumpflen V. Perspectives of a systems biology of the synapse: how to transform an indefinite data space into a model?. Pharmacopsychiatry 2010; 43 (Suppl. 01) S2-S8
• 7 Morse A. Searching for the Holy Grail: the Human Genome Project and its implications. J Law Health 1998; 13: 219-256
• 8 Barabasi AL. Network science: Luck or reason. Nature. 2012. 489. 507-508
• 9 Oltvai ZN, Barabasi AL. Systems biology. Life’s complexity pyramid. Science 2002; 298: 763-764
• 10 Bulik-Sullivan BK, Sullivan PF. The authorship network of genome-wide association studies. Nature Genetics 2012; 44: 113
• 11 Glauche I, Herberg M, Roeder I. Nanog variability and pluripotency regulation of embryonic stem cells – insights from a mathematical model analysis. PLoS One 2010; 5: e11238
• 12 Marr C, Strasser M, Schwarzfischer M et al. Multi-scale modeling of GMP differentiation based on single-cell genealogies. Febs J 2012; 279: 3488-3500
• 13 The human genome at ten. Nature 2010; 464: 649-650
• 14 Krumsiek J, Marr C, Schroeder T et al. Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PLoS One 2011; 6: e22649
• 15 Latour B. Networks, Societies, Spheres: reflections of an Actor-network Theorist. Keynote speech ed. Los Angeles: 2010. 18
• 16 Heinemann M, Sauer U. From good old biochemical analyses to high-throughput omics measurements and back. Curr Opin Biotechnol 2011; 22: 1-2
• 17 Ecker JR, Bickmore WA, Barroso I et al. Genomics: ENCODE explained. Nature 2012; 489: 52-55
• 18 Alberts B. The end of “small science”?. Science 2012; 337: 1583
• 19 Gladwell M. Enron, Intelligenc, and the Perils of Too Much Information What the Dog Saw: Little, Brown and Company. 2002. 151-176
• 20 Goffeau A, Barrell BG, Bussey H et al. Life with 6000 genes. Science 1996; 274: 546
• 21 Pentland A. Reinventing Society in the Wake of Big Data. 2013 Available from http://www.edge.org/conveersation/reinventing-society-in-the-wake-of-big-data
• 22 Visscher PM. Sizing up human height variation. Nature Genetics 2008; 40: 489-490
• 23 Pfeufer A. Number of QT-intervall related loci. 2013;
• 24 Haxby JV, Gobbini MI, Furey ML et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 2001; 293: 2425-2430
• 25 Sporns O, Tononi G, Kotter R. The human connectome: A structural description of the human brain. PLoS computational biology 2005; 1: e42
• 26 Seung S I am my connectome 2011 Available from: http://www.ted.com/talks/sebastian_seung.html
• 27 Gur RE, Gur RC. Functional magnetic resonance imaging in schizophrenia. Dialogues Clin Neurosci 2010; 12: 333-343
• 28 Sun S, Zhang C, Yu G. A Bayesian Network Approach to Traffic Flow Forecasting. IEEE Trans on Intelligent Transportation Systems 2006; 7: 9
• 29 van den Heuvel MP, Kahn RS, Goni J et al. High-cost, high-capacity backbone for global brain communication. Proc Natl Acad Sci USA 2012; 109: 11372-11377
• 30 Meyer-Lindenberg A. From maps to mechanisms through neuroimaging of schizophrenia. Nature 2010; 468: 194-202
• 31 Singer W, Matthieu R. Hirnforschung und Meditation. Suhrkamp Verlag; 2008
• 32 Stefansson H, Ophoff RA, Steinberg S et al. Common variants conferring risk of schizophrenia. Nature 2009; 460: 744-747
• 33 Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature 2009; 461: 218-223
• 34 Deng C, Pan B, Engel M et al. Neuregulin-1 signalling and antipsychotic treatment: Potential therapeutic targets in a schizophrenia candidate signalling pathway. Psychopharmacology (Berl) 2013;
• 35 Kendler KS. The dappled nature of causes of psychiatric illness: replacing the organic-functional/hardware-software dichotomy with empirically based pluralism. Mol Psychiatry 2012; 17: 377-388
• 36 Lanktree MB, Guo Y, Murtaza M et al. Meta-analysis of dense genecentric association studies reveals common and uncommon variants associated with height. Am J Hum Genet 2011; 88: 6-18