Sayed-Rzgar Hosseini, PhD joined SBMI in February 2022 as an Assistant Professor. Prior to joining UTHealth, he conducted postdoctoral research at EMBL-EBI (2019-2021), and Cancer Research UK, Cambridge Institute (2018-2019). He earned his PhD in Evolutionary Systems Biology from the University of Zürich in Switzerland. Furthermore, he graduated from ETH Zürich with an MSc in Computational Biology and Bioinformatics (CBB), a second MSc in Statistics and a CAS in Computer Science, which complemented his undergraduate background in Medical Biotechnology from the University of Tehran.

In general, Dr. Hosseini is interested in modelling complex intracellular networks to generate holistic insights into the molecular pathology of complex diseases. In particular, he strives to develop new statistical learning frameworks for modelling pharmacogenomic perturbation data to enable context-specific and interpretable prediction of drug response in oncology. Moreover, he is especially interested in modelling cancer evolution and is keen to design methods for single-cell analyses in cancer genomics.

“CRISPR-Cas9 genome editing technique has facilitated generation of pharmacogenomic perturbation data at an unprecedented scale. However, in order to unleash its full potential, a fundamental need for development of novel modelling approaches is felt in the field,” Hosseini said. “Cancer is indeed an evolutionary disorder, so it is essential for us to harness the predictability of cancer evolution in our relentless quest to find patient-specific treatment strategies.”

Education

• PhD, Evolutionary Systems Biology, University of Zürich, 2018
• MSc, Statistics, ETH Zürich, 2018
• CAS, Computer Science, ETH Zürich, 2014
• MSc, Computational Biology and Bioinformatics, ETH Zürich, 2013
• BSc, Biotechnology, University of Tehran, 2010

Areas of Expertise

• Computational Systems Biology
• Precision Cancer Medicine
• Statistical Bioinformatics

Selected Publications:

1. Hosseini, S.-R., and X. Zhou. 2022. CCSynergy: an integrated deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. Brief Bioinform, bbac588.
2. Hosseini, S.-R., Diaz-Uriarte R, Markowetz F, and N. Beerenwinkel. 2019. Estimating the predictability of cancer evolution. Bioinformatics, 35 (14): i389 i397.
3. Hosseini, S.-R., and A. Wagner. 2018. Genomic organization underlying deletional robustness in bacterial metabolic systems. PNAS A:201717243.
4. Hosseini, S.-R., O. Martin, and A. Wagner. 2016. Phenotypic innovation through recombination in genome-scale metabolic networks. Proc. R. Soc. B.
5. Hosseini, S.-R., and A. Wagner. 2015. Exhaustive analysis of a genotype space comprising 1015 central carbon metabolisms reveals an organization conducive to metabolic innovation. PLoS Comput. Biol. 11: e1004329.