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Title: Optimal Transport Modeling of Population Dynamics: Applications in Single-Cell Biology

Abstract: To understand the plasticity of cells and their responses to molecular perturbations, such as drugs or developmental signals, it is vital to recover the underlying population dynamics and fate decisions of single cells. However, measuring features of single cells requires destroying them. As a result, a cell population can only be monitored with unpaired sequential snapshots. In order to reconstruct individual cell fate trajectories, as well as the overall dynamics, one needs to re-align these unpaired snapshots, in order to guess for each cell what it might have become at the next step.

Optimal transport theory can provide such maps, and provides the mathematical link that unifies several contributions to model cellular dynamics that we present here: Inference from data of an energy potential best able to describe the evolution of differentiation processes, building on the Jordan-Kinderlehrer-Otto (JKO) flow; recovery of differential equations modeling the stochastic transitions between cell fates in developmental processes; as well as zero-sum game theory models parameterizing distribution shifts upon interventions, which we employ to model heterogeneous responses of tumor cells to cancer drugs. These models extend the set of existing tools to handle cell dynamics with robust and flexible methods, and make for an exciting avenue of future work on inferring personalized cancer therapies from single-cell patient samples


Speaker: Charlotte Bunne - https://twitter.com/_bunnech


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Chapters:
00:00 Introduction speaker
01:20 Start talk and overview
05:15 JKONet - Problem setup
07:48 JKONet - Introduction to JKO Flows
13:30 JKONet - Solve JKO Flows with backpropagation
19:59 JKONet - Evaluation
23:07 JKONet - Summary and conclusion
24:04 CellOT - Overview and methodology
28:24 CellOT - Evaluation
32:08 Future work
35:04 HoloProt - Overview and methodology
41:57 HoloProt - Evaluations
44:30 Conclusion