Precision Medicine: Personalized Approach

Our personalized medicine investigations extend the concepts of precision medicine to the individual, assessing multiple omics during longitudinal profiling.

Multi-Omics Integration

Omics analyses and integration requires extensive processing. MathIOmica provides a user-friendly framework for handling downstream analysis and visualization.

Scientific Community Resources

Creating models necessitates robust omics datasets, particularly for time series analyses and network inference. We are creating multiple such sets available to the community.


View George Mias's profile on LinkedIn

Michigan State University

East Lansing, MI 48824
  • Assistant Professor of Biochemistry and Molecular Biology
  • Adjunct Assistant Professor of Physics and Astronomy
  • Adjunct Assistant Professor of Pediatrics and Human Development


Yale University, New Haven, CT 06520
  • Ph.D. in Physics, 2007
  • M. Phil. in Physics, 2003
  • B.S. & M.S. in Physics, 2001 (Magna Cum Laude with Distinction in Physics)
Stanford University, Palo Alto, CA 94301
  • Postdoctoral Scholar in Genetics 2009-2014

Professor Mias joined MSU in 2014, conducting research in Personalized Medicine. The current research of the G.Mias lab focuses on the analysis and integration of existing (and developing) -omics technologies, their application to monitoring individuals as they transition through various physiological states, and their implementation towards personalized health. Professor Mias’ research is currently funded by an NIH Pathway To Independence Award (K99\&R00) from the National Human Genome Research Institute. He is interested in systems medicine and particularly focusing on future implementation of personalized/precision medicine and genetics.

Prior to joining MSU, Professor Mias studied at Yale University, completing a combined BS/MS (magna cum laude with Distinction in Physics, 2001), MPhil (2003) and PhD in theoretical Physics (2007), while concentrating on statistical physics, quantum dynamics and critical phenomena. Following graduate school, he was a Lecturer/Assistant in Instruction at Yale University before joining the Laboratory of Dr. Michael Snyder as a Postdoctoral Scholar with the Department of Genetics at Stanford University.

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    Refereed Journals

    *contributed equally
    1. A. Marcobal, T. Yusufaly, S. Higginbottom, M. Snyder, J.L. Sonnenburg, George I. Mias, Metabolome progression during early gut microbial colonization of gnotobiotic mice Scientific Reports 5, 11589; (2015)
      doi: 10.1038/srep11589
    2. E. Kolker, V. Özdemir, L. Martens, W. Hancock, G. Anderson,…,George I. Mias(37/61; alphabetic order),…, G. Yandl, Towards more transparent and reproducible omics studies through a common metadata checklist and data publications, OMICS: A Journal of Integrative Biology, 18(1): 81-85, (2014)
    3. M. Snyder, George I. Mias, L.I. Stanberry, E. Kolker, Metadata checklist for the integrated personal omics study: proteomics and metabolomics experiments­, OMICS: A Journal of Integrative Biology, 18(1) p81 (2014)
    4. L.I. Stanberry, George I. Mias, W. Haynes, R. Higdon, M. Snyder, E. Kolker Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile, Metabolites, 3(3) p741 (2013)
    5. George I. Mias*, R. Chen*, Y. Zhang, K. Sridhar, D. Sharon, L. Xiao, H. Im, M.P. Snyder, P.L. Greenberg, Specific Plasma Autoantibody Reactivity in Myelodysplastic Syndromes, Scientific Reports 3, 3311; (2013)
    6. R. Chen, S. Giliani, G. Lanzi, George I. Mias, S. Lonardi, K. Dobbs, J. Manis, H. Im, J.E. Gallagher, D.H. Phanstiel, G. Euskirchen, P. Lacroute, K. Bettinger, D. Moratto, K. Weinacht, D. Montin, E. Gallo, G. Mangili, F. Porta, L.D. Notarangelo, S. Pedretti, W. Al-Herz, W. Alfahdli, A.M. Comeau, R.S. Traister, S. Pai, G. Carella, F. Facchetti, K.C. Nadeau, M. Snyder, L.D. Notarangelo, Whole Exome Sequencing Identifies TTC7A Mutations for Combined Immunodeficiency with Intestinal Atresia, Journal of Allergy and Clinical Immunology, 132(3) p656 (2013)
    7. George I. Mias, M. Snyder, Personal Genomes, Quantitative Dynamic Omics and Personalized Medicine, Quantitative Biology 1(1) p71 (2013)
      Featured Editor Selection
      Cover Story; Designed Inaugural Cover and Cover Blurb.
    8. S. Liu, H. Im, A. Bairoch, M. Cristofanilli, R. Chen, S.Dalton, E. Deutsch, D. Fenyo, S.Fanayan,C. Gates, P .Gaudet; M. Hincapie, S. Hanash, H. Kim, S. Jeong, E. Lundberg, George I. Mias, R. Menon, Z. Mu, E. Nice, Y. Paik, M. Uhlén, L. Wells, W. Lance, S. Wu, F. Yan, F. Zhang, Y. Zhang, M. Snyder, G. Omenn, R. Beavis, H. Ronald, W. Hancock, A Chromosome-Centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17, Journal of Proteome Research 12(1), p45 (2013), PMID: 23259914
    9. George I. Mias, M. Snyder, Multimodal dynamic profiling of healthy and diseased states for personalized healthcare, Clinical Pharmacology and Therapeutics 93, p29 (2013), PMID: 23187877
    10. R. Chen*, George I. Mias*, J. Li-Pook-Than*, L. Jiang*, H.Y.K. Lam, R. Chen, E. Miriami, K.J. Karczewski, M. Hariharan, F.E. Dewey, Y. Cheng, M.J. Clark, H. Im, L. Habegger, S. Balasubramanian, M. O'Huallachain, J.T. Dudley, S. Hillenmeyer, R. Haraksingh, D. Sharon, G. Euskirchen, P. Lacroute, K. Bettinger, A.P. Boyle, M. Kasowski, F. Grubert, S. Seki, M. Garcia, M. Whirl-Carrillo, M. Gallardo, M.A. Blasco, P.L. Greenberg, P. Snyder, T.E. Klein, R.B. Altman, A.J. Butte, E.A. Ashley, M. Gerstein, K.C. Nadeau, H. Tang, M. Snyder, Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes, Cell 148(6) , p1293 (2012), PMID:22424236
      Featured as Genome Advance of the Month by National Human Genome Research Institute (NHGRI)
    11. George I. Mias, Nigel R. Cooper and S. M. Girvin, Quantum Noise, Scaling and Domain Formation in a Spinor BEC, Physical Review A 77 023616 (2008)
    12. George I. Mias and S. M. Girvin, Absence of Domain Wall Roughening in a Transverse-Field Ising Model With Long-Range Interactions, Physical Review B 72, 064411 (2005)

    In Preparation/Submission

    1. R. Chen*, J.A. Jenks, V. Singh, S. Lyu, S. Runyon, J. Li-Pook-Than, G. Euskirchen, P. Lacroute, George I. Mias, K. Nadeau, M. Snyder, An Omics View of Asthma through Discordant Monozygotic Twins

    Selected Proceedings and Conferences

    1. George I. Mias, H. Im, E. Mitsunaga, R. Chen R, J. Li-Pook-Than, L. Jiang, M. Snyder, Network Inference, Integrative Dynamic Omics and Personalized Medicine, American Society for Human Genetics 12th Annual Meeting, San Francisco, CA (2012)
    2. George I. Mias, R. Chen, J. Li-Pook-Than, L. Jiang, H. Tang, M. Snyder, Personalized Medicine Through Integrative Dynamic Omics , Human Proteome Organization HUPO 11th Annual World Congress, Boston, MA (2012)
    3. George I. Mias*, R. Chen*, J. Li-Pook-Than*, L. Jiang*, H. Lam, H. Tang, M. Snyder., Personalized Medicine Through Integrative Dynamic Omics, Biology of Genomes, Cold Spring Harbor Laboratory, NY (2012)
    4. R. Chen*,George I. Mias*, J. Li-Pook-Than*, L. Jiang*, et al., Integrative Personalized Omics Profiling Reveals Complex Molecular Phenotypes and Monitorable Medical Risks US HUPO, San Francisco, CA, (2012).
    5. George I. Mias, R. Chen.,Y. Zhang, D. Sharon, L. Xiao, K. Sridhar, M.P. Snyder, P.L. Greenberg, Proteomic Screening for Plasma Autoantibody Biomarkers in MDS Using Protein Microarrays, Leukemia Research 35, Supplement 1, S23, (2011)
    6. George I. Mias, S. M. Girvin, Bose-Einstein S=1 Spinor Condensates, Dynamics, Noise, Statistics and Scaling, Bulletin of the American Physical Society (2007)
    7. George Mias, S. Girvin, Domain Walls and Roughening Transition Possibilities in a Transverse-field Ising Model with Long-range Interactions,  Bulletin of the American Physical Society (2005)

    Internal: Yale Physics Department

    1. George I. Mias, Domains of Quantum Magnetism, Doctoral Dissertation; (2007), ISBN 978-0-549-37286-8
    2. George I. Mias, Nuclear Structure: Differences in R4/2 Ratios in Isotones and Isotopes, Undergraduate thesis (2000)

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Vikas Singh received Ph.D. in Life Sciences from the Dept. of Molecular & Human Genetics at Banaras Hindu University, Varanasi, India in 2014. He is currently working on experimental omics applications towards precision medicine.

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Lavida Brooks received a B.Sc. in Biology from the University of the Virgin Islands in May 2014. She enrolled in the Michigan State University Microbiology & Molecular Genetics PhD Program in the fall of 2014. Lavida is currently working on statistical methodology to process DNA and RNA sequencing data, including assessment for quality control and improvement of mapping algorithms. Lavida is supported by MSU AAGA and CNS fellowships.

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Raeuf Roushangar received a B.Sc in Biochemistry and Molecular Biology from Michigan State University in the summer of 2014. He enrolled in the Michigan State Universty Biochemistry Ph.D. program in the fall of 2014. Raeuf is currently working on building software tools for proteomics and metabolomics and other omics. He is a Paul and Daisy Soros Fellowship recipient (2015).

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Curtis Bunger is a sophomore student in the Honors College. He is interested in medical implementations of research.

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Hannah Rice is an undergraduate in the Honors College studying Fisheries and Wildlife with a concentration in Disease Ecology. She is interested in epidemiology and species conservation.

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Liz DeYoung is a Junior studying Human Biology. She hopes to continue her studies in medical school in the near future.

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Keerthana Byreddy is a freshman student in the Honors College. She is interested in computational research.

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Brian Gutermuth is an undergraduate majoring in Biochemistry


MathIOmica: a unique platform for omics currently under beta testing.

"Personalized medicine is expected to benefit from combining genomic information with regular moni- toring of physiological states by multiple high- throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type II diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, discovered extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and disease states by connecting genomic information with additional dynamic omics activity."

From Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Cell, Volume 148, Issue 6, 1293-1307, 16 March 2012

The raw data for the pilot iPOP study has been made publically available as follows:


snyderome contains local repository of iPOP data



Coming soon!

Stand-alone helper tool to aid in visualization (MathIOmica Addendum)

  1. George I. Mias, M. Snyder, Personal Genomes, Quantitative Dynamic Omics and Personalized Medicine, Quantitative Biology 1(1) (2013),
    doi:10.1007/s40484-013-0005-3   Offers examples of computational tools as an introduction to integrative dynamic omics.
  1. variety of Mass Spectrometry Utilities
  2. The Tuxedo Suite offers great tools for sequence analysis, such as Bowtie, TopHat and Cufflinks.
  3. NCBI tools
  1. Enthought has a great python distribution.
  2. Python
  3. Perl
  4. Stack Overflow has answers to a lot of programming questions.
  1. The Feynman Lectures on Physics has Richard Feynman's fantastic pedagogical lecture notes on Physics.

About Us

Our main interests lie in exploring further the integration of omics technologies and their application in personalized medicine. We believe that such combined high throughput information, in conjunction with monitoring dynamically changing physiological states will benefit the rapidly evolving field of personalized medicine. The integrative approaches will aid in the prediction, diagnosis and treatment of diseases as well as understanding disease state dynamics, namely their onset and progression. Furthermore, the integration of omics information will necessitate the development of novel efficient techniques for multiple omics data analysis and integration, including how to extract meaningful information from such dynamic data that is medically relevant.

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Contact Us

G. Mias Lab
603 Wilson Rd, Biochemistry Rm 120
East Lansing, MI, 48824