Abstract
In the previous paper, we reconstructed the entire transcriptional network for all 2418 clock-associated genes in the model filamentous fungus, Neurospora crassa ( 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 five transcription factors as well as to six 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.
Highlights
One of the major challenges of systems biology is the integration of a variety of omics and physiological data to understand complex traits, such as metabolism, development, behavior, and disease [1]
COMPARISON OF THE REGULATORY MODEL TO PROFILING EXPERIMENTS The first step in the analysis was to examine how the new regulatory model ensemble explains microarray data obtained every 4 hours over a 48 hour window in which N. crassa cultures were shifted to dark conditions (D/D) (Materials and Methods)
Predictions were derived as a model average across an ensemble of models (Fig 1) using a Variable Topology Ensemble Method (VTENS) (See Materials and Methods)
Summary
One of the major challenges of systems biology is the integration of a variety of omics and physiological data to understand complex traits, such as metabolism, development, behavior, and disease [1]. One of the major challenges in carrying out a program to test the regulatory mechanisms prevalent in a genome is having network reconstruction methods that: (1) recognize the sparsity of omics data scattered over the whole genome coupled with the large number of parameters (i.e., rate constants and initial conditions of molecular species) [3], [4] for specifying a genetic network; (2) scale to the whole genome [5]; and (3) allow the reconstruction of networks of unknown topology (i.e., who regulates whom) [6]–[8] Microbial systems, such as Saccharomyces cerevisiae and N. crassa, have laid the foundation for understanding eukaryotic gene regulation [1], [9]–[14].
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