The purpose of this project is to reprogram the immune system. New therapies are being developed that modulate immune cells and can be used to treat diseases that manipulate the immune system, e.g. cancer and tuberculosis. On the cellular level, immune cells integrate signals from receptors on their surface to activate different transcriptional programs through a network of thousands of signaling proteins. Because of the network’s complexity, it is challenging to identify targets that reprogram cells with precision. To overcome this, computer models have been developed to predict the response, yet, current models only cover a fraction of the network. Recent developments in deep learning has enabled large-scale models to be constructed that make accurate predictions in many different domains. Here, a deep neural network model of intracellular signaling will be developed to predict cellular responses to interventions. During the first year the model will be constructed, using database knowledge of the signaling network (Aim 1) and will be parameterized with data from a large co-stimulation experiment conducted by collaborators at Massachusetts Institute of Technology. During the second year the model will be used to characterize signaling signatures for infected macrophages (Aim 2). Appropriate interventions will be identified, and validated (by collaborators) during the third year (Aim 3). If successful, the project will enable computer guided interventions against disease.
Full Professor at Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Post doc at Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Funding Chalmers participation during 2020–2023