Theoretical and Mathematical Work: Bioinformatics approach

Our theoretical work on cancer biology is centered on the OMICS approach, i.e. the exploitation of large databases for the purpose of extracting specific information. For instance, bioinformatic analyses of publicly available microarray and RNAseq data can be used to identify immune gene signatures and correlate these with cancer survival data. It is highly likely that the whole immune system, as indicated by expression levels for relevant genes, has been altered, and high-level patterns need to be identified. This requires existing tool use from graph analysis and machine learning. In this respect, we obtained help from Louise and Ashwin financed from our general fund. Our goal is the development of new computational approaches (1, 2), especially in the area of systems models (3, 4), and to provide support for dedicated mathematical scholars.

Medical Application: Precision Medicine

With the development of biologic drugs targeting intracellular pathways, new challenges arise by optimization of drug therapy and the development of drug resistance (1, 2, 3). Modeling the cell as a self-regulating system, which adapts to significant protein expression changes by up and down regulating other components of the signaling system, is often required in order to optimize drug treatments. One approach is to use optimal control and model drug combination therapy (1, 2). Drug resistance can be checked by introducing an optimality function and expect the cell to constantly adapt its protein expression to maximize this function. Interpreting and informing this basic model by data will lead to a mathematically sound and resilient drug resistance model. This can be used to predict and to mitigate the development of drug resistance. Mathematical modeling for precision medicine (4) will have significant applications in any drug-based therapies, and is poised to overcome the limitations of single-target drug development and treatment.

Last changed: 5/19/2024