Lecturer: Orr Levy, Yale
 Abstract:
Complex dynamical systems comprise numerous components that interact in a non-trivial topology and non-linear form, giving rise to diverse array of states and rich system properties, spanning from robustness to evolvability. Unveiling such properties is of great importance, enabling better prediction and control of complex systems. However, this endeavor remains challenging due to the incomplete knowledge in many systems. Here, we developed a novel computational top-down approach designed to estimate global features of the complex system from a collection of data samples, without inferring its entire map of interactions. We demonstrate the effectiveness of this approach in two distinct biological cases, by applying it to single-cell RNA sequencing data, inferring different characteristics of the complex machinery of the cell, its gene regulatory network (GRN). First, we found a decrease in the gene-to-gene transcriptional coordination among aging cells, suggesting a general, potentially universal, stochastic attribute of transcriptional dysregulation in aging, implying that the gene regulatory network is progressively losing its consistency across cells as organisms age. In the latter, we leveraged this approach to infer the interaction intensity of GRNs across various cell types. Our findings reveal a pronounced complexity-stability tradeoff, suggesting that a GRN could be stable up to a critical level of complexity, a product of the diversity of gene expression and the intensity of the GRN interactions. This insight suggests that GRNs were shaped by stability constraints, which in turn impose limits on the extent of gene expression diversity within cells.