Andrei Rodin

Andrei Rodin, Ph.D.

  • Dr. Susumu Ohno Chair in Theoretical Biology
  • Associate Professor, Department of Diabetes Complications and Metabolism

Andrei Rodin, Ph.D.

  • Department of Diabetes Complications & Metabolism

Degrees

  • Ph.D.

Our three primary research interests are:

  1. Development and maintenance of systems biology / computational biology methodology and software, based on the sound mathematical foundation (entropy / Bayesian networks) and directly applicable to the large-scale heterogeneous data being routinely generated within the current biomedical research pipelines.
     
  2. Collaborations with both internal (BRI COH) and external investigators, to apply the above methodology to the big datasets that are increasingly overwhelming in the context of secondary data analysis. It should be mentioned that the bottleneck is not so much the big data / scalability issue as such, but rather the ability to incorporate different data types (Genomics? Epigenomics? Metabolomics? Transcriptomics? Next kind of “-omics”?) within the same framework.
     
  3. Theoretical evolutionary biology. As Dobzhansky famously stated, “Nothing in Biology Makes Sense Except in the Light of Evolution”. We concur. We have developed a number of mathematical models combining epigenetic factors, transposons, tRNAs and other non-canonical (i.e. not just primary genetic sequences) molecular evolution data.


The Bayesian networks software is publically available at  (https://bitbucket.org/uthsph/bnomics/). We are currently working on developing an even more user-friendly version. The software is essentially aimed at the user with “big biological data”. I,e, if an investigator has a complex dataset, BNOmics can likely make sense (automated biological hypotheses generation) out of it. Over the last year, we’ve been successful in modeling in different domains, ranging from evolutionary biology to cancer epidemiology to immunogenetics.

We are predominantly interested in developing and maintaining systems biology / computational biology data analysis methodology and software (with emphasis on mathematical rigour and adaptability to heterogeneous data types) directly applicable to the large-scale heterogeneous data being routinely generated within the current biomedical research pipelines. We collaborate with both internal (COH) and external investigators in applying such methodology to the big datasets ranging from genomic to epigenomic to just about any kind of -omic. We are also interested in developing mathematical models and flexible analysis techniques in the context of molecular evolution research.

  • Rodin AS, Gogoshin G, Litvinenko A, Boerwinkle E (2012) Exploring genetic epidemiology data with Bayesian networks. In: Rao CR, Sen PK, Chakraborty R (eds) Handbook of Statistics. Volume 28 (Bioinformatics). Elsevier, St. Louis, MO
  • Branciamore S, Rodin AS, Riggs AD, Rodin SN (2014) Enhanced evolution by stochastically variable modification of epigenetic marks in the early embryo. Proc Natl Acad Sci USA 111(17):6353-6358
  • Branciamore S, Rodin AS, Gogoshin G, Riggs, AD (2015) Epigenetics and Evolution: Transposons and the Stochastic Epigenetic Modication Model. AIMS Press Genetics Vol. 1
  • Jung M, Jin SG, Zhang X, Xiong W, Gogoshin G, Rodin AS, Pfeifer GP (2015) Characterization of longitudinal and genome-wide epigenetic and gene expression profiles in human aging by 3-component analysis. Nucleic Acids Research 43 (15)
  • Rodin AS, Branciamore S (2016) The Universal Genetic Code and Non-Canonical Variants. In:  Reference Module in Life Sciences. Elsevier, London, UK
  • Gogoshin G, Boerwinkle E, Rodin AS (2016) New algorithm and software (BNOmics) for inferring and visualizing Bayesian networks from heterogeneous “big” biological and genetic data. J Comp Bio., in press
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