Treffer: Differential transcriptional regulation by alternatively designed mechanisms: A mathematical modeling approach.

Title:
Differential transcriptional regulation by alternatively designed mechanisms: A mathematical modeling approach.
Authors:
Yildirim N; Division of Natural Sciences, New College of Florida, Bayshore Road, Sarasota, FL, USA., Aktas ME; Department of Mathematics, Florida State University, W College Ave, Tallahassee, FL, USA., Ozcan SN; Department of Mathematics, North Carolina State University, Raleigh, NC, USA., Akbas E; Department of Computer Science, Florida State University, W College Ave, Tallahassee, FL, USA., Ay A; Departments of Biology and Mathematics, Colgate University, Oak Drive, Hamilton, NY, USA.
Source:
In silico biology [In Silico Biol] 2017; Vol. 12 (3-4), pp. 95-127.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: SAGE Publications Country of Publication: Netherlands NLM ID: 9815902 Publication Model: Print Cited Medium: Internet ISSN: 1434-3207 (Electronic) Linking ISSN: 13866338 NLM ISO Abbreviation: In Silico Biol Subsets: MEDLINE
Imprint Name(s):
Publication: 2025- : [Thousand Oaks, CA] : SAGE Publications
Original Publication: Amsterdam ; Washington, DC : [Tokyo] : IOS Press ; Ohmsha, c1998-
Contributed Indexing:
Keywords: Mathematical modeling; delayed differential equation; gene regulation; gene regulatory network; numerical simulation; signal transduction
Entry Date(s):
Date Created: 20160807 Date Completed: 20190318 Latest Revision: 20190318
Update Code:
20250114
DOI:
10.3233/ISB-160467
PMID:
27497472
Database:
MEDLINE

Weitere Informationen

Cells maintain cellular homeostasis employing different regulatory mechanisms to respond external stimuli. We study two groups of signal-dependent transcriptional regulatory mechanisms. In the first group, we assume that repressor and activator proteins compete for binding to the same regulatory site on DNA (competitive mechanisms). In the second group, they can bind to different regulatory regions in a noncompetitive fashion (noncompetitive mechanisms). For both competitive and noncompetitive mechanisms, we studied the gene expression dynamics by increasing the repressor or decreasing the activator abundance (inhibition mechanisms), or by decreasing the repressor or increasing the activator abundance (activation mechanisms). We employed delay differential equation models. Our simulation results show that the competitive and noncompetitive inhibition mechanisms exhibit comparable repression effectiveness. However, response time is fastest in the noncompetitive inhibition mechanism due to increased repressor abundance, and slowest in the competitive inhibition mechanism by increased repressor level. The competitive and noncompetitive inhibition mechanisms through decreased activator abundance show comparable and moderate response times, while the competitive and noncompetitive activation mechanisms by increased activator protein level display more effective and faster response. Our study exemplifies the importance of mathematical modeling and computer simulation in the analysis of gene expression dynamics.