Cellular and Biomolecular States Are Highly Multimodal and Complex
Cellular state is a multifaceted and complex phenomenon, involving multiple overlapping omics layers that vary in time and space. Capturing and representing this multimodal complexity is essential for predictive modeling of cell behavior and for advancing our understanding of cellular function.
Foundational Capabilities (3)
Train predictive models that can probabilistically infer any missing
omics data from any available measurements by learning a broadly useful
latent space. Such a universal representation of cellular state could become
the foundation for many predictive and diagnostic applications.
Develop automated approaches for mechanistic interpretability of virtual
neural network models trained on biological state data. This would enable the
extraction of mechanistic insights from predictive models, potentially
informing both basic science and therapeutic design.
Models that represent cells as self-organizing and adaptive are critical for enabling the simulation of multi-cellular systems.
These should incorporate subcellular data from experimental techniques tracking individual molecules within cells in real time, as well as capture the fundamental interaction between external and internal molecular environment.