Identifying mechanisms of regulation to model carbon flux during heat stress and generate testable hypotheses
| Autor: | Allen Hubbard, Abhyudai Singh, Sara Jastrebski, Carl J. Schmidt, Susan J. Lamont, Xiaoke Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
B Vitamins Male Computer science lcsh:Medicine Gene Expression Pathology and Laboratory Medicine Biochemistry Antioxidants Transcriptome Medicine and Health Sciences Genomic library Amino Acids lcsh:Science Carbon flux chemistry.chemical_classification Multidisciplinary Organic Compounds Physics Systems Biology Classical Mechanics Vitamins Lipids Glutathione Amino acid Complement (complexity) Heat stress Chemistry Physical Sciences Metabolome Mechanical Stress Research Article Chemical Elements Systems biology Genomics Computational biology Cholines Data type Polymorphism Single Nucleotide Carbon Cycle 03 medical and health sciences Signs and Symptoms Diagnostic Medicine Genetics Sulfur Containing Amino Acids Animals Hyperthermia Cysteine Heat shock Gene Library 030102 biochemistry & molecular biology lcsh:R Organic Chemistry Chemical Compounds Biology and Life Sciences Proteins Computational Biology Lipid Metabolism Carbon 030104 developmental biology Thermal Stresses Metabolism chemistry Linear Models RNA lcsh:Q Peptides Chickens Sulfur Heat-Shock Response |
| Zdroj: | PLoS ONE PLoS ONE, Vol 13, Iss 10, p e0205824 (2018) |
| ISSN: | 1932-6203 |
| Popis: | Understanding biological response to stimuli requires identifying mechanisms that coordinate changes across pathways. One of the promises of multi-omics studies is achieving this level of insight by simultaneously identifying different levels of regulation. However, computational approaches to integrate multiple types of data are lacking. An effective systems biology approach would be one that uses statistical methods to detect signatures of relevant network motifs and then builds metabolic circuits from these components to model shifting regulatory dynamics. For example, transcriptome and metabolome data complement one another in terms of their ability to describe shifts in physiology. Here, we extend a previously described method used to identify single nucleotide polymorphism (SNP’s) associated with metabolic changes (Gieger et al., 2008). We apply this strategy to link changes in sulfur, amino acid and lipid production under heat stress by relating ratios of compounds to potential precursors and regulators. This approach provides integration of multi-omics data to link previously described, discrete units of regulation into functional pathways and hypothesizes novel biology relevant to the heat stress response. |
| Databáze: | OpenAIRE |
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