לוח שנה משולב

The past decade has witnessed the emergence of single-cell technologies that measure the expression level of genes at a single-cell resolution. These developments have revolutionized our understanding of the rich heterogeneity, structure, and dynamics of cellular populations, by probing the states of millions of cells, and their change under different conditions or over time. However, in standard experiments, information about the spatial context of cells, along with additional layers of information they encode about their location along dynamic processes (e.g. cell cycle or differentiation trajectories), is either lost or not explicitly accessible. This poses a fundamental problem for elucidating collective tissue function and mechanisms of cell-to-cell communication.
In this talk I will present computational approaches for addressing these challenges, by learning interpretable representations of structure, context and design principles for multicellular systems from single-cell information. I will first describe how the locations of cells in their tissue of origin and the resulting spatial gene expression can be probabilistically inferred from single-cell information by a generalized optimal-transport optimization framework that can flexibly incorporate prior biological assumptions or knowledge derived from experiments. Inference in this case is based on an organization principle for spatial gene expression, namely a structural correspondence between distances of cells in expression and physical space, which we hypothesized and supported for different tissues. We used this framework to spatially reconstruct diverse tissues and organisms, including the fly embryo, mammalian intestinal epithelium and cerebellum, and further inferred spatially informative genes. Since cells encode multiple layers of information, in addition to their spatial context, I will also discuss several approaches for the disentanglement of single-cell gene expression into distinct biological processes, based on ideas rooted in random matrix theory and manifold learning. I will finally discuss how these results can be generalized to reveal principles underlying self-organization of cells into multicellular structures, setting the foundation for the computationally-directed design of cell-to-cell interactions optimized for specific tissue structure or function.
Sunday, December 8, 2019 at 13:00
Sandwiches at 12:45
Drory Auditorium

Time irreversibility is the hallmark of nonequilibrium dissipative processes. Detecting dissipation is essential for our basic understanding of the underlying physical mechanism, however, it remains a challenge in the absence of observable directed motion, flows, or fluxes. Additional difficulty arises in complex systems where many internal degrees of freedom are inaccessible to an external observer. In living systems, for example, the dissipation is directly related to the hydrolysis of fuel molecules such as adenosine triphosphate (ATP), whose consumption rate is difficult to directly measure in many experimental setups. In this talk, I will introduce a novel approach to detect time irreversibility and estimate the entropy production from time-series measurements, even in the absence of observable currents. This method can be implemented in scenarios where only partial information is available and thus provides a new tool for studying nonequilibrium phenomena.


1. G. Bisker et al. Inferring broken detailed balance in the absence of observable currents,
Nature Communications, 10(1), 1-10 (2019)
2. G. Bisker et al. Hierarchical Bounds on Entropy Production Inferred from Partial
Information, Journal of Statistical Mechanics: Theory and Experiment (9), 093210 (2017)

Cardiovascular disease is a result of genetic and environmental risk factors that together generate arterial and cardiac pathologies. Blood vessels connect multiple organ systems throughout the entire body allowing organs to interact via circulating messengers. Multimodality imaging achieves integration of these interfacing systems’ distinct processes, quantifying interactions that contribute to cardiovascular disease. Noninvasive multimodality imaging techniques are emerging tools that can further our understanding of this complex and dynamic interplay. Multichannel multimodality imaging including optics, CT, PET and MRI, are particularly promising because they can simultaneously sample multiple biomarkers. As the opportunities provided by imaging expand, mapping interconnected systems will help us decipher the complexity of cardiovascular disease and monitor novel therapeutic strategies.