Circadian rhythms allow organisms to anticipate and adapt to reliable environmental events. The suprachiasmatic nucleus (SCN) generates these precise daily oscillations and yet adapts to environmental changes like seasonal day length, depending heavily on reorganization of gene and cellular network structures. The long-term goals of this research are to: 1) create a dynamic network-mapping algorithm that reveals features including interaction functions, 2) apply this algorithm to infer functional connectivity of diverse systems under different conditions, and 3) resolve longstanding questions about optimal spatial hierarchies in network control. This proposal aims to: overcome a significant mathematical challenge (to create a predictive, data-rich network representation of complex, nonlinear dynamical processes), solve an important biological problem (to decrypt the underlying interaction network of the circadian clock), and apply the solutions to novel system control (to explore the impact of the network structure on animal behavior through enhanced feedback).
In Aim 1, we will solve the large-scale, topology estimation problem of complex networks by the utilization of orthonormal bases for expressing connection functions.
In Aim 2, we will reduce the network to a dynamically equivalent small network. This will be applied to control entrainment of oscillatory networks using a reverse engineered phase assignment (PA).
In Aim 3, we will apply these techniques to map the topology and identify intercellular phase coupling functions among thousands of SCN cells. We will then test whether hubs within the network represent specific cell types (e.g., vasoactive intestinal polypeptide, VIP) and play key roles in the development and maintenance of synchrony. The predictive power of the reconstructed networks will be tested following chronodisruption (e.g., by targeted deletion of cells, Aim 3.2), enhanced behavioral feedback (EBF), Aim 4) and PA (Aim 5). The innovative combination of novel mathematical and biological tools (e.g., color switching bioluminescence in VIP and non-VIP cells) will reveal the roles of the multiple SCN coupling pathways and greatly improve our understanding of the spatiotemporal dynamics of information processing in the SCN. Control-theoretic protocols, EBF and PA will create a new research paradigm in network science and circadian biology. The research findings will give significant insights into the structure of the circadian network and how repeated daily disruptions can reorganize this structure and impact behavior. By formulating the biological problem from a mathematical viewpoint, the team will reveal network dynamics with novel computational strategies that help mitigate the effects of circadian disruptions.
Decrypting the intricacies of daily rhythms, generated by the circadian clock network in the body, requires a novel, integrative data-driven and model-based mathematical machinery. The research on the organization and disruption of the circadian system (with targeted deletion of cells and innovative dynamic light protocols) will provide critical insights into how circadian cells can be coordinated to encode local time. These findings will have important implications for prevention and treatment of timing-related disorders, including seasonal affective disorder, shift-work disorder, and jet-lag.
