Cemal Erdem and Elin Chorell seek collaborators at a Lunch Pitch

Make computational models great again

Pitch by Cemal Erdem, Assistant professor, Department of Medical Biosciences
 
Development of a single cancer drug, on average, costs more than half a billion US dollars and takes years to go into the market. However, as Cemal explained in his pitch, large-scale computational models can help prioritize lead candidates and stratify potentially responsive patients, and these are currently underutilized. Computational models are also critical to re-purpose available drugs, reveal new mechanisms to target, and design better clinical regimens. For these models to become useful and predictive, they need to be trained on experimental and clinical data. One thing that the famous ChatGPT has shown is that machine learning models can become quite successful if they are trained with enough data. in Cemal’s pitch, he showed snippets of computational models that can be built on in his lab.
 
Cemal ended his pitch with a request for any and all datasets the audience has or knows about. He hopes that, working with others, machine learning and mechanistic models with patient data can be combined to create clinically predictive computational models for cancer and other diseases.

The sphinx of our metabolism 

Pitch by Elin Chorell, Assistant professor, Department of Public Health and Clinical Medicine

The global rise in obesity and type 2 diabetes poses a significant health risk, accompanied by comorbidities like cardiovascular diseases and certain cancers. Recent findings challenge the notion of type 2 diabetes as a lifelong condition, revealing that a reduction of organ fat can induce disease remission meaning that lipid mechanisms are of key importance to both disease progression and remission. While diabetes can be managed with lifestyle changes and medication, the underlying mechanism remains unclear, hindering efficient risk prediction and treatment.

Elin Chorell’s expertise lies in mass spectrometry-based lipidomics screening, focusing on insulin signaling tissues, disease progression, and remission. Her research group explores obesity-related states, studying the impact of exercise and diet through human studies, mouse models, and cell experiments. Their focus is on sphingolipids, a complex class of lipids involved in a range of cellular processes. Her research indicates that sphingolipid metabolism, specifically in pancreatic islets and skeletal muscle, are detrimental for the insulin signaling machinery. Sphingolipid metabolism, says Elin, like its namesake ‘the Sphinx’, remains an enigma due to their diverse chemical composition and therefore a challenge to measure. Elin is focused on unraveling the chemical composition of the bioactive lipids of the sphingolipid metabolism in her quest to understand obesity-associated disease. She is currently seeking to connect altered tissue metabolism with circulating markers. Identifying important markers could help uncover therapeutic targets for risk assessment and monitoring.

In order to achieve this goal she is seeking collaborators experienced in sorting cell populations or tissue or single cell bioimaging to further unravel the spatial orientation of the bioactive sphingolipid derivatives her group has identified in their model systems.