Since I started my PhD in 2017, I've become part of several research projects related to Mathematical Oncology. In particular, I'm interested in decoding colon cancer in the context of Lynch syndrome, the most common inherited colon cancer syndrome. You are invited to explore the different projects by clicking below.
Mathematics in Oncology
Mathematics in Oncology is a collaborative initiative in Heidelberg, Germany which started with the start of my PhD in 2017. Since November 2021, it is an official three-year research project funded by the Klaus Tschira Foundation. We implement mathematical modeling on the example of Lynch syndrome, in particular colorectal cancer development in the hereditary context, focusing on cancer development, tumor immunology, and the translation to clinical decision making.
The Prospective Lynch Syndrome Database (PLSD) has been developed as an international, multicenter, prospective, observational study that aims to provide age and organ-specific cancer risks in Lynch syndrome according to gene and gender, estimates of survival after cancer and information on the effects of interventions. Supporting PLSD with statistical analyses and mathematical advice allows cancer risk information and clinical recommendations for diagnosis, prevention and treatment of Lynch syndrome carriers on a population level.
Indicate is a collaborative initiative that aims at identifying factors influencing cancer risk in Lynch syndrome. The goal is to find out whether the risk of developing a tumor could depend on one of the essential host factors, namely the HLA type of the individual, and to delineate the HLA type-related cancer risk of Lynch syndrome individuals. Being part of INDICATE and coordinating mathematical modeling within INDICATE allows me to derive mathematical models for understanding tumor-immune interactions and for exploring phenomena such as immunoediting, using real-world immunological data.
Mathematical modeling and artificial intelligence for newborn screening
In newborn screening diagnostics blood samples taken from newborns within a few days after birth are analyzed to identify rare metabolic diseases and hormonal disorders. The accurate and efficient diagnosis of these diseases is important but challenging due to their low prevalence. In this collaborative initiative, we closely work together with clinicians from the University Hospital Heidelberg to develop and analyze innovative data-driven and biology-driven mathematical models to exploit the full information of newborn screening test results to improve specificity and positive prediction. Mathematical uncertainty quantification (UQ) is a valuable tool to describe and quantify noise in the data, in order to obtain reliable classification results for newborn screening. Further, we apply techniques of Explainable AI (XAI) in the context of rare disease diagnosis with the overall goal of reliable and interpretable models with high diagnostic prediction to support the diagnosis of rare diseases within newborns.
Informatics for Life
Despite remarkable progress in the diagnosis and treatment of acute and chronic cardiovascular diseases, they still represent the leading cause of death and hospitalization. This worrying situation is due to the ageing population and to the fact that the heart is affected by several comorbidities and side effects of their treatment. In the past, cardiovascular research was dominated by hypothesis driven strategies, while only recently novel computational methods allow the dissection of large datasets obtained from single individuals up to the population-level, simulation of complex disease processes and prediction of molecular and clinical phenotypes and their outcome. Despite this progress, translation of these computational technologies into healthcare requires a rigorous translational agenda and close cooperation of different disciplines. Informatics for life embark on the high potential of a joint approach incorporating experts from computational methods and clinical research in cardiology and beyond. An important element for true translation into clinical application will be the patient-centric environment of Informatics for life.