Loading...
Density evolution for noise propagation analysis in biological networks
Kotiang, Stephen Eslami, Ali
Kotiang, Stephen
Eslami, Ali
Citations
Altmetric:
Files
Loading...
Open access PDF
Adobe PDF, 4.07 MB
Authors
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2022-01-13
Type
Article
Genre
Keywords
Boolean networks,Density evolution,Error propagation,Factor graph,Gene regulatory networks
Subjects (LCSH)
Citation
S. Kotiang and A. Eslami, "Density Evolution for Noise Propagation Analysis in Biological Networks," in IEEE Access, vol. 10, pp. 4261-4270, 2022, doi: 10.1109/ACCESS.2022.3140720.
Abstract
Accurate prediction of noise propagation in biological networks is key to understanding faithful
signal propagation in gene networks as well as for designing noise-tolerant synthetic gene circuits. Knowledge on how biological fluctuations propagate up the development ladder of biological systems is currently
lacking. Similarly, little research effort has been devoted to the analysis of error propagation in biological
networks. To capture and characterize error evolution, this paper considers a Boolean network (BN) model
representation of a biological network such that nodes on the graph represent diverse biological entities, e.g.,
proteins, genes, messenger-RNAs, etc. In addition, the network edges capture the interactions between nodes.
By conducting a density evolution analysis on the graphical model based on node functionalities, a recursive
closed-form expression for error propagation is derived. Subsequently, the recursive equation allows us to
obtain a necessary condition to guarantee noise-error elimination in dynamic discrete gene networks. Our
analytical formulations provide a step toward achieving optimal network parameters for resilience against
variability or noise in biology
Table of Contents
Description
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 4.
Publisher
IEEE
Journal
Book Title
Series
IEEE Access;2021
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
DOI
ISSN
2169-3536