Christina Theodoris, MD, PhD

Our lab leverages cutting-edge machine learning and experimental genomics to map the gene regulatory networks disrupted in cardiovascular disease and discover network-correcting therapeutics. We develop machine learning models that leverage the unprecedented volume of transcriptomic and epigenomic data now available to gain a fundamental understanding of network dynamics that can be democratized to a vast array of downstream applications. Investigating the consequences of network rewiring that occurs in disease states uncovers the key mechanisms that coordinate gene transcription to ensure normal development and tissue maintenance. Furthermore, mapping the network dysregulation driving disease allows targeting normalization of central elements to treat the core disease mechanism rather than merely managing symptoms. We apply an innovative network-based framework for therapeutic discovery to cardiovascular disease to accelerate development of much-needed treatments for patients as well as to advance our fundamental understanding of the regulatory circuitry governing human development and disease.
Education
postdoc, 09/2022 - Computational Biology, Broad Institute of MIT and Harvard; Dept. of Data Science, Dana-Farber Cancer Institute
residency, 06/2022 - Pediatrics-Medical Genetics Residency, Boston Children's Hospital
MD, PhD, 06/2017 - Developmental and Stem Cell Biology, University of California, San Francisco
BS, 06/2009 - Biology, California Institute of Technology
Websites

  1. Bunne C, Roohani Y, Rosen Y, Gupta A, Zhang X, Roed M, Alexandrov T, AlQuraishi M, Brennan P, Burkhardt DB, Califano A, Cool J, Dernburg AF, Ewing K, Fox EB, Haury M, Herr AE, Horvitz E, Hsu PD, Jain V, Johnson GR, Kalil T, Kelley DR, Kelley SO, Kreshuk A, Mitchison T, Otte S, Shendure J, Sofroniew NJ, Theis F, Theodoris CV, Upadhyayula S, Valer M, Wang B, Xing E, Yeung-Levy S, Zitnik M, Karaletsos T, Regev A, Lundberg E, Leskovec J, Quake SR. How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell 2024. PMID: 39672099


  2. Theodoris CV. Learning the language of DNA. Science (New York, N.Y.) 2024. PMID: 39541478


  3. Bunne C, Roohani Y, Rosen Y, Gupta A, Zhang X, Roed M, Alexandrov T, AlQuraishi M, Brennan P, Burkhardt DB, Califano A, Cool J, Dernburg AF, Ewing K, Fox EB, Haury M, Herr AE, Horvitz E, Hsu PD, Jain V, Johnson GR, Kalil T, Kelley DR, Kelley SO, Kreshuk A, Mitchison T, Otte S, Shendure J, Sofroniew NJ, Theis F, Theodoris CV, Upadhyayula S, Valer M, Wang B, Xing E, Yeung-Levy S, Zitnik M, Karaletsos T, Regev A, Lundberg E, Leskovec J, Quake SR. How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities. ArXiv 2024. PMID: 39398201


  4. Chen H, Venkatesh MS, Ortega JG, Mahesh SV, Nandi TN, Madduri RK, Pelka K, Theodoris CV. Quantized multi-task learning for context-specific representations of gene network dynamics. bioRxiv : the preprint server for biology 2024. PMID: 39229018


  5. Wen DJ, Theodoris CV. Interpretable model of CRISPR-Cas9 enzymatic reactions. Volume 3 of Issue 12. Nature computational science 2023. PMID: 38177728


  6. Rosen RH, Epee-Bounya A, Curran D, Chung S, Hoffmann R, Lee LK, Marcus C, Mateo CM, Miller JE, Nereim C, Silberholz E, Shah SN, Theodoris CV, Wardell H, Winn AS, Toomey S, Finkelstein JA, Ward VL, Starmer A, BOSTON CHILDREN’S HOSPITAL RACE, ETHNICITY, AND ANCESTRY IN CLINICAL PATHWAYS WORKING GROUP. Race, Ethnicity, and Ancestry in Clinical Pathways: A Framework for Evaluation. Volume 152 of Issue 6. Pediatrics 2023. PMID: 37974460


  7. Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS, Ellinor PT. Transfer learning enables predictions in network biology. Volume 618 of Issue 7965. Nature 2023. PMID: 37258680


  8. Lynch AW, Theodoris CV, Long HW, Brown M, Liu XS, Meyer CA. MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Volume 19 of Issue 9. Nature methods 2022. PMID: 36068320


  9. Theodoris CV, Zhou P, Liu L, Zhang Y, Nishino T, Huang Y, Kostina A, Ranade SS, Gifford CA, Uspenskiy V, Malashicheva A, Ding S, Srivastava D. Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease. Science (New York, N.Y.) 2020. PMID: 33303684


  10. Theodoris CV, Mourkioti F, Huang Y, Ranade SS, Liu L, Blau HM, Srivastava D. Long telomeres protect against age-dependent cardiac disease caused by NOTCH1 haploinsufficiency. The Journal of clinical investigation 2017. PMID: 28346225


  11. Barruet E, Morales BM, Lwin W, White MP, Theodoris CV, Kim H, Urrutia A, Wong SA, Srivastava D, Hsiao EC. The ACVR1 R206H mutation found in fibrodysplasia ossificans progressiva increases human induced pluripotent stem cell-derived endothelial cell formation and collagen production through BMP-mediated SMAD1/5/8 signaling. Stem cell research & therapy 2016. PMID: 27530160


  12. White MP, Theodoris CV, Liu L, Collins WJ, Blue KW, Lee JH, Meng X, Robbins RC, Ivey KN, Srivastava D. NOTCH1 regulates matrix gla protein and calcification gene networks in human valve endothelium. Journal of molecular and cellular cardiology 2015. PMID: 25871831


  13. Theodoris CV, Li M, White MP, Liu L, He D, Pollard KS, Bruneau BG, Srivastava D. Human disease modeling reveals integrated transcriptional and epigenetic mechanisms of NOTCH1 haploinsufficiency. Cell 2015. PMID: 25768904


  14. Smith J, Kraemer E, Liu H, Theodoris C, Davidson E. A spatially dynamic cohort of regulatory genes in the endomesodermal gene network of the sea urchin embryo. Volume 313 of Issue 2. Developmental biology 2007. PMID: 18061160


  15. Smith J, Theodoris C, Davidson EH. A gene regulatory network subcircuit drives a dynamic pattern of gene expression. Volume 318 of Issue 5851. Science (New York, N.Y.) 2007. PMID: 17975065