Bio
Daniel is a first generation mathematician and researcher currently pursuing a Ph.D. at the CRUK Cambridge Institute (CRUK CI). Born in Mexico City, he developed a passion for pure mathematics during his bachelor´s studies, in particular for algebraic topology. After graduating at the Escuela Superior de Física y Matemáticas of the National Polytechnic Institute (ESFM, IPN) in Mexico, he further expanded his knowledge by completing a MSc in Applied Mathematics, focusing on the implementation of stochastic processes in Game Theory. With a strong foundation in mathematical theory and its practical applications, he took the next step to follow his passion for research by embarking on his doctoral journey under the guidance of Professor Carlos Caldas and Dr. Oscar M. Rueda.
In his Ph.D. studies, Daniel´s primary focus lies in the development and application of statistical methods and mathematical models for the analysis and integration of high-throughput data and tumour growth profiles in breast cancer. Specifically, he is interested in using data that incorporates drug response in in vivo, as well as high-throughput screening in ex vivo pre-clinical models such as PDTXs and PDTCs. Daniel strongly believe that applying maths to biomedical research will improve our understanding of breast cancer. Daniel strives to make meaningful contributions towards the development of enhanced treatment strategies for breast cancer patients.
Research interests
Cancer is a serious heath concern and to effectively deal with it, we need personalised treatments based on a deep understanding of its nature. The Caldas group has made significant strides in predicting cancer outcomes by pinpointing key genetic alterations in breast cancer.
But to discover and evaluate individual treatments, we need trustworthy systems that mirror the diverse range of cancers seen in patients. This is where the group´s work with mouse-derived cancer models and cells comes into play. These models are designed to simulate actual patient tumours, which could aid in finding the most suitable personalised treatments.
A significant challenge lies in making sense of the comprehensive data from these animal and cellular models, which spans from tumour growth rates to genomics and transcriptomics data. It can be tricky to determine if a treatment that shows promise in the lab will be effective in a real patient. For example, treatments on mouse models do not always entirely eradicate the cancer, making it difficult to link from what we see in the lab with actual patient outcomes.
To understand why certain breast cancer types resist drugs, we need to employ more sophisticated mathematical and statistical approaches. In that sense, this project aims to integrate machine learning and mathematical models that uses drug response and multi-omics data to shed light on the crucial elements influencing drug performance predictions. The Turing Institute´s expertise in machine learning is vital for achieving this goal.