Understanding cellular decisions in latent space

Theoretical biology has historically introduced many physics-inspired metaphors (such as the Waddington landscape), but in the age of molecular biology and network complexity, it is not clear if and how such ideas can be used in a predictive way. In this talk, I will show how multicellular signalling dynamics can sometimes be best represented and modelled in a low dimensional « latent space », offering an alternative modeling approach to standard gene networks. For gap genes during fly development, an auto-encoder model is used to derive a 2D model, allowing for intuitive interpretation of both dynamics and positional information. In the context of immune recognition by T cells, we developed a robotic platform combined to machine learning to exhibit a universal dynamics for cytokines in response to antigens. This model fully explains patterns of immune activations, adversarial interactions in immunotherapy, and can be re-connected a posteriori to molecular interactions. The successful application of a similar concept to two very different contexts suggests this approach can be broadly applied to many biological dynamics.