Noemi Andor (PI)
My Ph.D. in Bioinformatics was under the supervision of Hans Werner Mewes from the Technical University in Munich and Claudia Petritsch from the University of California, San Francisco. Together we developed one of the first algorithms that deconvolutes a tumor’s sequencing data into clones that coexist in the tumor biopsy. As a postdoctoral fellow, together with Hanlee Ji and Carlo Maley, I quantified intra-tumor heterogeneity in >1000 primary tumors to find that coexistence of multiple clones in the same tumor is indeed the norm. As an Instructor at Stanford in Prof. Ji’s lab, I integrated bulk- and single-cell sequencing approaches to zoom into different perspectives of intra-tumor heterogeneity. The newly gained resolution on coexisting clones and their microenvironment puts us in the yet best position to control and steer subclonal evolution.
Tommy is a PhD candidate interested in evolutionary game theory (EGT). He joined the lab in February 2020 as part of the Integrated Mathematical Oncology Doctoral Training Program at USF. His work combines EGT and single-cell sequencing to understand how cell-cell interactions shape population structure in gastric cancers. Before joining the lab, Tommy completed his undergraduate degree in Mathematics at USF. With his PhD work, he hopes to contribute to a better understanding of how divergent populations within a tumor compete and cooperate while exposed to variable environments, such as DNA damaging therapies.
Gregory is an Applied Research Scientist in both, the Altrock and Andor lab at the IMO. He holds a B.A. in Mathematics, a B.S. in Statistics; with Minors in Physics and Actuarial Science, from University of Florida, and an M.S. and Ph.D. in Engineering Sciences and Applied Mathematics from Northwestern University. Gregory brings along a deep understanding of analytical and computational modeling of physical, bio-physical, and cell biological systems. His interests include modeling cellular movement and selection in response to energy gradients. This has led to a better understanding of how nutrient efficacy and chemotactic response impact invasion and spatial segregation of polyploid cancer populations. Current projects include studying how chromosome mis-segregations contribute to diversify ploidy of a population, and modeling how oxygen supply and demand shape the histopathology of Glioblastoma.
Dr. Saeed Alahmari is an Assistant Professor of Computer Science and the director of Scientific and Engineering research at Najran University, Najran- Kingdom of Saudi Arabia. He received his PhD in Computer Science from University of South Florida (USF) in 2020 and his Master degree of Computer Science from University of Dayton, Ohio in 2015. Dr. Alahmari has authored several journals and conference papers. His research interests include medical image processing, deep learning, machine learning and computer vision.
Email: firstname.lastname@example.org; webpage http://saeed3.myweb.usf.edu/
Dr. Maksin received his PhD in medicine from the Department of Clinical Pathology at Poznań University of Medical Sciences in Poland. In his doctoral dissertation he used DNA Damage Markers to assess the Parenchyma of Papillary Thyroid Cancer and to inform the dosage of radioiodine therapy. Konstantin is intrigued by the parallels between cell aging and cancer growth, and his main research interests include the role of epithelial-mesenchymal transition in neoplastic progression as well as the mutual influence between tumor cells, parenchyma, and vasculature. Dr. Maksin’s daily practical experience as a pathologist informs design of our mathematical models. His expertise betters our understanding of oxygen supply and demand in glioblastoma, with the goal to model how hypoxia-induced phenotypes influence response to second-line therapy in Glioblastoma.
Dr. Siddiqui is a physician-scientist in radiation oncology at University of Pittsburgh Medical Center. His training in deep learning models made him university ambassador for Nvidia to teach Fundamentals of Computer Vision. He was awarded a training grant by the Radiological Society of North America to perform research using deep learning models for radiation oncology. Zaid is part of a long-term project aiming to develop a new class of machine learning methods that leverage the complementary spatial and temporal resolutions of genotypic and phenotypic measurements.
Brad joined the lab as an intern in January 2020. His project explores the hypothesis of a tradeoff between a clone’s intrinsic growth rate under ideal, nutrient-rich conditions and its ability to grow under suboptimal conditions. Using a system of coupled ODEs, he models the change in a cell line’s clonal composition as a function of different timings of splitting and passaging cells and of different seeding densities. He is pursuing a degree in mathematics at Dartmouth College and plans on going to Med school after he graduates.
Martina is a Research Associate and the propelling force of the lab’s experimental domain, it “is where (her) heart and brain want to be!” Her project management and organizational skills set the foundation for smooth interactions between experimental and computational domains in the lab. She is trained in Molecular and Cellular Biology, and brings along a Master’s degree from University of Catania, Italy as well as industry experience from working at a drug discovery company – KCAS Bioanalytical. Martina’s long-term project involves tracking the pedigree of evolving cell lines along with potentially changing cell culture habits to identify long-term trends in the clonal evolution of cell lines. She is also involved in testing the potential of genomic instability as biomarker of DNA damage therapy sensitivity.