Mathematical Oncology Members
2019 Left to right: Ricardo Espinosa, MiHyun (Amy) Jang, Soham Bose, Prativa Sahoo, David Frankhouser, Russell Rockne, Sergio Braciamore, Daniel Abler, Davide Maestrini, Michelle Morales (administrative assistant), Lisa Costan (grants administration, financial analyst). Not shown: Vikram Adhikarla, Syed Rahmanuddin.
Affiliate Faculty
She obtained MSc in metal-organic chemistry of bio-inorganic polymers and transition metals complexes from the Adam Mickiewicz University in Poland. During PhD at the University of South Florida in Tampa, she studied computational and biophysical chemistry. While at USF, she applied and developed computational methods for the conformational studies of biomolecules on various levels of resolution including ab initio, atomistic and coarse- grained models of small molecules, proteins and DNA. Her first postdoctoral training in the Mathematical Oncology Department at Moffitt Cancer Center in Tampa focused on applications of theoretical and computational methods in clinically relevant settings. At Moffitt, she worked on 2D models of imaging agents penetration through the tumor tissue, and 3D models of tumor organoids growth. Her desire to more completely contribute to the field of precision medicine led her to the Institute for Research in Biomedicine in Barcelona, Spain where her postdoctoral training was jointly funded by MSCA actions and BIST. At IRB, she applied multi-omics and machine learning approaches to uncover the structural signatures of cancer mutations in individual or subgroups of patients.
In her research she combines approaches from computational and biophysical chemistry, structural biology, mathematical oncology and biostatistics to elucidate the role of heterogeneity in cancer progression and response to treatment. She seeks to understand variations in disease evolution, variability in the response to treatment and development of resistance, and how this knowledge, explored on the multi-scaled resolution levels using highly interdisciplinary approach, can be utilized to improve the outcomes of individual patients.
Staff
Vikram Adhikarla
Vikram Adhikarla is a Staff Scientist in the Division of Mathematical Oncology at City of Hope. He has a Ph.D. in Physics from the University of Wisconsin – Madison with specialized focus in Medical Physics. His interests lie in the domain of computational modeling and analysis of both pre-clinical and clinical imaging data. He has experience in modeling tumor-vasculature system based on hypoxia and proliferation positron emission tomography (PET) imaging data and has worked on modeling the response of tumor-vasculature system to anti-angiogenic therapy.
He has further trained in the kinetic analysis of PET imaging data in the Department of Radiology at Emory University in Atlanta. His focus in particular has been on the evaluation of novel radiotracers used for imaging neurological disorders.
As a scientist at City of Hope, he uses his skills to analyze the migration of stem cells in immunohistological and three dimensional cleared images of the mice brain. This data analysis feeds into his work on the prediction of stem cell migration paths for the translational purpose of optimizing the clinical delivery of stem cell therapeutics. Concurrently, he is also involved in analysis of clinical molecular imaging data for evaluation of novel radiotracers poised to deliver clinical impact.
He has further trained in the kinetic analysis of PET imaging data in the Department of Radiology at Emory University in Atlanta. His focus in particular has been on the evaluation of novel radiotracers used for imaging neurological disorders.
As a scientist at City of Hope, he uses his skills to analyze the migration of stem cells in immunohistological and three dimensional cleared images of the mice brain. This data analysis feeds into his work on the prediction of stem cell migration paths for the translational purpose of optimizing the clinical delivery of stem cell therapeutics. Concurrently, he is also involved in analysis of clinical molecular imaging data for evaluation of novel radiotracers poised to deliver clinical impact.
Adina Matache is a part-time Senior Research Associate in the Division of Mathematical Oncology at City of Hope. She received her Ph.D. in Electrical Engineering from University of California, Los Angeles (UCLA) and her B.S. and M.S. degrees, both in Electrical Engineering from University of Washington.
Adina has extensive research experience with theoretical analysis and modeling and simulation of digital communication systems, including terrestrial and satellite communication systems. She gained her expertise in digital communications and information theory while working for commercial companies and Federally Funded R&D Centers. At City of Hope, she applies her knowledge in communication and information theory to research immune cell signaling in breast cancer. She hopes this research can lead to new insights into how immune cells are signaling and processing molecular information in healthy donors and how the intracellular signaling pathways may be impaired in breast cancer patients.
Postdoctoral researchers
Alex Brummer is a PhD physicist with research experience in mathematical biology and ecology and evolutionary biology. His research aims to develop and test mathematical models of cancer growth and treatment response. He works jointly with Dr. Russell Rockne (Mathematical Oncology, Beckman Research Institute) examining cancer treatment response in glioblastoma, breast cancer, and leukemia; and with Dr. Van Savage (Computational Medicine, UCLA) studying imaging biomarkers of lung cancer derived from pulmonary vasculature.
Alex is interested in understanding and predicting large scale phenomena from small scale interactions and optimizations. His research leverages elements from non-linear dynamics, population ecology, fluid mechanics, fractal geometry, spatial networks, shape analysis, computer vision, and allometric scaling theory. His work with Dr. Rockne is currently focused on (i) studying how different nonlinear model interactions between glioblastoma and CAR T-cells give way to various growth trajectories, and (ii) using data-driven, sparse identification techniques to discover underlying interactions.
Ryan Woodall is a Post-doctoral Fellow in the department of Mathematical Oncology, working with Dr. Rockne on modeling the transport of interstitial fluid within brain tumors using computational fluid dynamics. He utilizes his experience in both computational fluid dynamics and medical image processing to create patient-specific models of fluid transport within the brain and tumor tissue. He completed his PhD at the University of Texas at Austin, where he worked closely with Dr. Tom Yankeelov on models of fluid flow within tumors and earned an NIH coursework portfolio in Imaging Science and Informatics. Specifically, he has developed histology-derived fluid flow models of contrast agent transport within mouse xenograft tumors, for the purpose of assessing the accuracy of common parameterization methods for dynamic contrast-enhanced MRI. He has also developed models of radio-liposome transport within glioblastoma, in collaboration with the University of Texas Health Science Center at San Antonio, for the purpose of aiding clinicians to predict the optimal catheter placement for each individual patient undergoing nanoliposome infusion to the brain. He hopes to use his expertise to help engineer algorithms which are capable of optimizing the delivery of drugs and radiation therapy to tumors for individual patients, and to further understand intra-tumoral fluid transport within the brain.
Lisa Uechi received her Ph.D. and M.Sc. in Informatics (Computer Science) from the Department of Intelligence Science and Technology, Kyoto University, Japan. The research subjects of her graduate program included computational biology and applied mathematics for the prediction of economic activities. In her research projects, she developed mathematical models for analyzing the dynamical pattern changes of population density and evaluating the damage expansion rate via a metabolic networks. She also proposed a novel method that could predict significant financial sectors along with the changes in economic trends by collaborating economists at Boston University.
After her graduate programs, she focused on information theory-based methods for data mining and applications for genetic data. She proposed mathematical models and algorithms based on information theory to analyze the dependencies among the observed phenotypes and genetic variants. She created a model for evaluating the reliability of multi-variable interaction analysis based on the concept of communication channels for data with noise and missing values. She is currently working on the development of mathematical models based on thermodynamics and information theory for Acute Myeloid Leukemia.