Events Archive

Deep learning for single cell biology

Gene expression in all organisms is controlled by short DNA and RNA sequences called cis-regulatory elements (CREs). Proteins in the cellular milieu bind to nucleic acid sequences present within CREs, interact with one another, and thus form macromolecular complexes that modulate the expression of nearby genes.

To understand computation in the brain, one needs to understand the input-output relationships for neural circuits and the anatomical and functional relationships of individual neurons therein.

Our lab develops new imaging approaches for visualizing bacterial physiology in relevant contexts: We use live-cell single-molecule localization microscopy and lattice-light sheet microscopy to access 3D spatial and temporal information with high resolution.

While efficient coding has been a successful organizational principle in visual neuroscience, to make a more general theory behavioral, mechanistic, and even evolutionary constraints need to be added to this framework.

Bacteria exhibit a high degree of intracellular macromolecular crowding. To control the level of crowding cells must increase their volumes in response to the accumulation of biomass during growth. This coordination is fundamentally not understood in any cell type.

Macromolecular protein complexes perform essential biological functions across life forms. The assembly of such complexes is known to be regulated at the level of gene transcription, but little is known about the factors that control their assembly once the mature protein subunits enter their target space (cytoplasm, membrane, or cell wall).

Visual systems detect many features of natural scenes, including motion. Motion detection guides critical behaviors like hunting, evading predators, and finding mates. It can be framed as an inference problem, in which light intensity measurements are combined to estimate a latent variable of image velocity.