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.
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.
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.
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).
Curtis Bunger is a sophomore student in the Honors College. He is interested in medical implementations of research.
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.
Liz DeYoung is a Junior studying Human Biology. She hopes to continue her studies in medical school in the near future.
Keerthana Byreddy is a freshman student in the Honors College. She is interested in computational research.
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
The raw data for the pilot iPOP study has been made publically available as follows:
snyderome contains local repository of iPOP data
Stand-alone helper tool to aid in visualization (MathIOmica Addendum)
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.