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Stochastic networks for the clock were identified by ensemble methods using genetic algorithms that captured the amplitude and period variation in single cell oscillators ofNeurosporacrassa. The genetic algorithms were at least an order of magnitude faster than ensemble methods using parallel tempering and appeared to provide a globally optimum solution from a random start in the initial guess of model parameters (i.e., rate constants and initial counts of molecules in a cell). The resulting goodness of fit$${x}^{2}$$$x^2$was roughly halved versus solutions produced by ensemble methods using parallel tempering, and the resulting$${x}^{2}$$$x^2$per data point was only$${\chi }^{2}/n$$${\chi }^2/n$= 2,708.05/953 = 2.84. The fitted model ensemble was robust to variation in proxies for “cell size”. The fitted neutral models without cellular communication between single cells isolated by microfluidics provided evidence for onlyoneStochastic Resonance at one common level of stochastic intracellular noise across days from 6 to 36 h of light/dark (L/D) or in a D/D experiment. When the light-driven phase synchronization was strong as measured by the Kuramoto (K), there was degradation in the single cell oscillations away from the stochastic resonance. The rate constants for the stochastic clock network are consistent with those determined on a macroscopic scale of 10⁷cells.

 

In previous work we reconstructed the entire transcriptional network for all 2,418 clock-associated genes in the model filamentous fungus, N. crassa. Several authors have suggested that there is extensive post-transcriptional control in the genome-wide clock network (IEEE 3: 27, 2015). Here we have successfully reconstructed the entire clock network in N. crassa with a Variable Topology Ensemble Method (VTENS), assigning each clock-associated gene to the regulation of one or more of 5 transcription factors as well as to 6 RNA operons. The resulting network provides a unifying framework to explore the clock’s linkage to metabolism through post-transcriptional regulation, in which ~850 genes are predicted to fall under the regulatory control of an RNA operon. A unique feature of all of the RNA operons inferred is their functional connection to genes connected to the ribosome. We have been successful in distinguishing several hypotheses about regulatory topologies of the clock network through protein profiling of the regulators.