Ensemble Methods for Identifying RNA Operons and Regulons in the Clock Network of Neurospora Crassa

Abstract

With both light entrainment data (L/D) and free running data in the dark (D/D), Variable Topology Ensemble Methods (VTENS) were used to reconstruct the entire genome-scale clock network involving 3380 genes and their products under the hypothesis that clock-controlled genes are both transcriptionally regulated and post-transcriptionally regulated through RNA operons. The clock network has 12 hubs with target genes surrounding twelve regulators, and 694 putative clock-controlled genes provide linkage between hubs in their joint regulation. There were 71 significant feedforward loops within this clock network, the majority of which involved genes lhp-1 and NCU06108 targeting genes connected with the ribosome and ribosome biogenesis. All 12 regulators target amino acid biosynthesis. The largest hub (768 genes) surrounded the post-transcriptional regulator lhp-1defining an RNA operon. There appears to be no partitioning of light-regulated genes and circadian genes between the 12 hubs. The regulators lhp-1, has-1, and rrp-3 appear to target putative clock-controlled genes in ribosome biogenesis. The combined hypothesis of 6 RNA operons and 5 transcriptional regulons was successful in describing the dynamics of the clock network with 3380 genes and their products with a χ 2 = 2.133 per data point for 60,759 mRNA time points. With little known about allosteric regulation of metabolism, the clock network provided a major link between gene regulation and metabolism.

Uncovering in vivo biochemical patterns from time-series metabolic dynamics

“…In the new interface, with a few clicks, biologists will be able to select one cluster in the FN or the CN and highlight nodes in the other network, visualize network clusters in the FPCA scores plot or vice versa, and search for interactions between a group of network nodes in metabolic pathways [ 42 , 43 , 46 ]. Based on biochemical pathways, information from other omics experiments, such as transcriptomics [ 7 ] and proteomics [ 8 ] can also be co-analyzed with metabolomics [ 15 ].…”

“…Such direct approaches face challenges in computation and data accessibility. Including a realistic topology and making appropriate parameter estimations are computationally expensive [ 10 , 11 , 13 – 15 ]. Compromises in topological structure (e.g., regulation) can result from a lack of pathway knowledge or enough data and significantly reduce a model’s utility.…”

Big Data Bot with a Special Reference to Bioinformatics

“…Effectively managing and extracting these big structured and unstructured data is a time-consuming job. These data can be analyzed using information extraction systems, for which several tools and techniques have been presented that can be used to extract useful information from structured and unstructured data using parallel algorithms with general-purpose graphics processing unit (GPGPUs) [56,57]. However, numerous studies on information extraction are limited to text, image, audio, or video data types [58][59][60][61][62].…”

Memory-friendly fixed-point iteration method for nonlinear surface mode oscillations of acoustically driven bubbles: from the perspective of high-performance GPU programming

“…In addition, the shape modes themselves are also mutually coupled; thus, this approach can capture the energy transition between the surface modes and the mean radial pulsation. The model is composed of globally coupled second-order ordinary differential equations (albeit the model complexity is significantly increased compared to the linearised approach), for which the use of GPUs is already widespread [76] , [77] , [78] , [79] , [80] , [81] , [82] . However, due to the implicit nature of the coupling, a linear system of algebraic equations has to be solved at every function evaluation for the second time-derivatives of the radial dynamics and the surface mode amplitudes.…”