Enhanced memory for detail in autism

Much research on autism focuses only on deficits in social cognition.

But another common hallmark of autism-spectrum disorders is an enhanced memory for details, such as types of objects and their location, words from different languages, types of cars etc. (‘item’ memory). Current neural models have use-dependent synaptic plasticity as the basis of memory which is not sufficient to explain this phenomenon. Neural plasticity includes intracellular signaling as a vital part of memory formation. We want to use data from autism-derived induced neuronal cells to investigate which components of these mechanisms are altered and build models to investigate the effects on neural plasticity. We may learn which properties are ultimately responsible for the observed enhanced item memory.

Toxins in Cancer

Chemical pollutants are now ubiquitous, yet their effects on cancer disease progression is rarely addressed in molecular cancer studies

We will carry out bioinformatic analyses of publicly available microarray and RNAseq data to identify toxicological gene signatures and correlate these with cancer survival data. For this purpose we will use graph analysis and machine learning, and develop new tools of high-throughput analysis, suitable to the problem.This approach is complementary to the more usual approach of seeking intrinsic gene markers as ‘driver mutations’ underlying the disease. In cancer patients, unless there is a history of occupational exposure the toxic body burden is rarely, if ever determined prior to treatment. Yet this information may prove to be vital to choose the right treatment in the era of personalized, precision medicine.This is a high-risk, high-reward project with great significance in understanding the genesis of cancer and seeking better, earlier treatment methods.

Drug Resistance

With the development of biologic drugs targeting intracellular pathways, such as lapatinib for breast cancer, new challenges arise by the development of drug resistance.

Data exist for lapatinib as for other drugs comparing the protein expression signature in drug-resistant tumor cells with parental cell lines. These data have been proven to be difficult to understand on the background of a static model of cell-internal signaling. 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 desired by practitioners, but has rarely been accomplished. Our approach has been to introduce 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.