In this project we will extend a new method of modeling that makes the simulation of coupled chemical reactions, such as those found in metabolism, much easier to implement and apply. The new approach relies upon mass action dynamics and makes modeling biological phenomena across large scales much easier because (1) time can be rescaled to an appropriate degree and (2) the dynamical equations are `telescopic' ? that is, sets of coupled reactions can be collapsed and just summary reactions modeled with the approach. We will apply this new method to understand the dynamical behavior and circadian rhythms of cells. Circadian rhythms are found in organisms ranging in complexity from cyanobacteria to humans and represent a major aspect of cellular regulation in the majority of eukaryotes. These rhythms control many aspects of cellular behavior, ranging from the release of cellulases in fungi to degrade cellulose in the environment to the dynamics of human cells. Circadian dysfunction in humans underlies metabolic, behavioral, and cognitive disorders. The new approach is based on recent developments in physics, and in statistical thermodynamic fluctuation theories. In these approaches, modeling the relative time- dependence of reactions can be shown to be much easier than modeling the absolute time. We will apply these methods not only to the enzymatic reactions of metabolism, but also to the regulatory network that comprises the circadian clock which controls the metabolism of cells over a period of 24 hours.
This project will develop new simulation methods that have much higher predictive power for understanding cell behavior. This new technology will be used to understand circadian rhythms, which are involved in a large array of biomedical phenomena and diseases ? from the behavior of cyanobacteria to the breakdown of cellulose by fungi to human metabolic disorders.
| Dunlap, Jay C; Loros, Jennifer J (2018) Just-So Stories and Origin Myths: Phosphorylation and Structural Disorder in Circadian Clock Proteins. Mol Cell 69:165-168 |
| Hughes, Michael E; Abruzzi, Katherine C; Allada, Ravi et al. (2017) Guidelines for Genome-Scale Analysis of Biological Rhythms. J Biol Rhythms 32:380-393 |
