Andrei S. Rodin, Ph.D.

[email protected]
626-256-4673
Andrei S. Rodin, Ph.D.
Professor, Department of Computational and Quantitative Medicine
Academic Appointments
  • Dr. Susumu Ohno Chair in Theoretical Biology
Research Teams
Computational and Quantitative Medicine

Andrei S. Rodin, Ph.D., is a Professor in the Department of Computational and Quantitative Medicine at the Beckman Research Institute of City of Hope, where he holds the Dr. Susumu Ohno Endowed Chair in Theoretical Biology. He also serves as Co-Director of the Division of Mathematical Oncology and Computational Systems Biology.

Dr. Rodin’s research focuses on computational systems biology, probabilistic graphical modeling and network-based machine learning, causal discovery, interpretable artificial intelligence and hybrid AI architectures that integrate probabilistic, statistical, neural and symbolic approaches. Research in Dr. Rodin’s laboratory encompasses both the development of novel computational methodologies and their collaborative application across diverse biomedical domains. Another longstanding area of interest is the mathematical modeling of molecular evolution.

Dr. Rodin earned his Ph.D. from the University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, where he specialized in human and molecular genetics and mathematical biology. Before joining City of Hope, he held tenured faculty appointments in Biostatistics and Epidemiology at the University of Texas UTHealth Houston School of Public Health.

 

View Dr. Rodin's lab

Location

Education & Experience

Research

Our three primary research interests are:

  1. Development of mathematically rigorous computational systems biology methodology and software (Bayesian networks, mixed mutual information, machine learning, and artificial intelligence in general) that is directly applicable to the high-dimensional heterogeneous data being routinely generated within the current biomedical research pipelines.
  2. Collaborations with both internal (City of Hope) and external investigators, to apply the above methodology to the big datasets that are increasingly overwhelming in the context of secondary data analysis. Our application areas are diverse, ranging from immune signaling in immuno-oncology to -omics of Alzheimer’s to molecular dynamics to medical imaging.
  3. Theoretical evolutionary biology. As Dobzhansky famously stated, “Nothing in Biology Makes Sense Except in the Light of Evolution”. We concur. We are currently working on several mathematical models combining epigenetic factors, transposons, tRNAs, and other non-canonical (i.e. not just primary genomic sequences) molecular evolution data.

Publications

  • Gogoshin G, Rodin AS. Bayesian network structure learning: the new calibrated minimum uncertainty criterion and a statistical error estimation framework. bioRxiv. 2026 Aug 23. doi:10.1101/2025.10.13.681946.
  • Wei W, Del Mundo R, Yang T, Mukhaleva E, Sivaraj IR, Shanmugasundaram V, Branciamore S, Rodin AS, Sivaramakrishnan S, Ma N, Vaidehi N. Protein frustration reveals orthosteric and allosteric active sites in GPCR:G protein complexes. J Chem Inf Model. 2026 Jul 27;66(14):8410-8422. doi:10.1021/acs.jcim.6c00203.
  • Wei W, Taylor HD, Ma N, Rodin AS, Branciamore S, Dohlman HG, Vaidehi N. Conserved residues in the Gα interface show subtype specificity in Gβγ coupling. J Biol Chem. 2026 Jul;302(7):113231. doi:10.1016/j.jbc.2026.113231.
  • Craig DW, Rodin AS. Extrachromosomal DNA as a causal instrument for spatial multi-omics. bioRxiv. 2026 Jun 5. doi:10.64898/2026.06.02.729483.
  • Manookian B, Mukhaleva E, Gogoshin G, Bhattacharya S, Vaidehi N, Rodin AS, Branciamore S. DRUMBEAT temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics. Commun Biol. 2026 Apr 15;9(1):818. doi:10.1038/s42003-026-09995-z.
  • Mukhaleva E, Sánchez Rivas EJ, Branciamore S, Rodin AS, Sivaramakrishnan S, Vaidehi N. Allostery between distant structural regions dictates selectivity in GPCR:G protein coupling. Biochemistry. 2026 Mar 17;65(6):704-716. doi:10.1021/acs.biochem.5c00570.
  • Wiley HS, Lopez CF, Rodin AS, Rockne RC, Yankeelov TE, Sauro HM, Hassan G, Prabhakaran S, Demir E, Hu M, Fuxman Bass J, Luddy KA, Newman H, Mochel JP, Costello JC, Xing J, Afify SM, Acar A, Haden Gephart M, Feng S, Banks O, Banerjee J, Clayton A, Hill DE, Sweis RF, Umeh-Garcia MC, Su Y, Meyer AS. A roadmap for the future of systems biology in cancer research. Cancer Res. 2025 Dec 15;85(24):4880-4889. doi:10.1158/0008-5472.CAN-25-0700.
  • Manookian B, Mukhaleva E, Gogoshin G, Bhattacharya S, Sivaramakrishnan S, Vaidehi N, Rodin AS, Branciamore S. Temporally resolved and interpretable machine learning model of GPCR conformational transition. Nat Commun. 2025 Dec 6;17(1):257. doi:10.1038/s41467-025-66958-4.
  • Gogoshin G, Rodin AS. Minimum uncertainty as Bayesian network model selection principle. BMC Bioinformatics. 2025 Apr 8;26(1):100. doi:10.1186/s12859-025-06104-5.
  • Mukhaleva E, Manookian B, Chen H, Sivaraj IR, Ma N, Wei W, Urbaniak K, Gogoshin G, Bhattacharya S, Vaidehi N, Rodin AS, Branciamore S. BaNDyT: Bayesian network modeling of molecular dynamics trajectories. J Chem Inf Model. 2025 Feb 10;65(3):1278-1288. doi:10.1021/acs.jcim.4c01981.
  • Branciamore S, Rodin AS, Riggs AD. Stochastic epigenetic modification and evolution of sex determination in vertebrates. J Mol Evol. 2024 Dec;92(6):861-873. doi:10.1007/s00239-024-10213-9.
  • Hilliard S, Mosoyan K, Branciamore S, Gogoshin G, Zhang A, Simons DL, Rockne RC, Lee PP, Rodin AS. Bow-tie architectures in biological and artificial neural networks: implications for network evolution and assay design. iScience. 2023 Jan 25;26(2):106041. doi:10.1016/j.isci.2023.106041.
  • Gogoshin G, Branciamore S, Rodin AS. Synthetic data generation with probabilistic Bayesian networks. Math Biosci Eng. 2021 Oct 9;18(6):8603-8621. doi:10.3934/mbe.2021426.
  • Le J, Park JE, Ha VL, Luong A, Branciamore S, Rodin AS, Gogoshin G, Li F, Loh YE, Camacho V, Patel SB, Welner RS, Parekh C. Single-cell RNA-seq mapping of human thymopoiesis reveals lineage specification trajectories and a commitment spectrum in T cell development. Immunity. 2020 Jun 16;52(6):1105-1118.e9. doi:10.1016/j.immuni.2020.05.010.
  • Wang X, Branciamore S, Gogoshin G, Rodin AS. Dependency between protein-protein interactions and protein variability and evolutionary rates in vertebrates: observed relationships and stochastic modeling. J Mol Evol. 2019 Jul;87(4-6):184-198. doi:10.1007/s00239-019-09899-z.
  • Zhang X, Branciamore S, Gogoshin G, Rodin AS, Riggs AD. Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling. Proc Natl Acad Sci U S A. 2017 Nov 28;114(48):E10359-E10368. doi:10.1073/pnas.1620425114.
  • Gogoshin G, Boerwinkle E, Rodin AS. New algorithm and software (BNOmics) for inferring and visualizing Bayesian networks from heterogeneous big biological and genetic data. J Comput Biol. 2017 Apr;24(4):340-356. doi:10.1089/cmb.2016.0100.