Learning to capture the functions of genetic regulatory networks using graph motifs

V. K. Murthy; E. V. Krishnamurthy

doi:10.1504/IJAIP.2012.048146

Learning to capture the functions of genetic regulatory networks using graph motifs

V. K. Murthy, E. V. Krishnamurthy^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Network motifs have been suggested as a means to understand Systems biological functions. Based on several arguments from computer science, logic, mathematics, chemistry and non-linear dynamics, we explain the difficulties encountered in capturing the evolving dynamical functions carried out by the genetic regulatory networks (GRN), based on the assembly of motifs.

Original language	English
Pages (from-to)	185-197
Number of pages	13
Journal	International Journal of Advanced Intelligence Paradigms
Volume	4
Issue number	2
DOIs	https://doi.org/10.1504/IJAIP.2012.048146
Publication status	Published - Jul 2012

Access to Document

10.1504/IJAIP.2012.048146

Cite this

@article{18bb3a60657942039d58e518b0fdc863,

title = "Learning to capture the functions of genetic regulatory networks using graph motifs",

abstract = "Network motifs have been suggested as a means to understand Systems biological functions. Based on several arguments from computer science, logic, mathematics, chemistry and non-linear dynamics, we explain the difficulties encountered in capturing the evolving dynamical functions carried out by the genetic regulatory networks (GRN), based on the assembly of motifs.",

keywords = "Adjacency matrices, Assortative networks, CN, Complex network, Disassortative networks, Eigenvalues, Graphs, Lyapunov exponents, Motifs, Positive entropy, System biology",

author = "Murthy, {V. K.} and Krishnamurthy, {E. V.}",

year = "2012",

month = jul,

doi = "10.1504/IJAIP.2012.048146",

language = "English",

volume = "4",

pages = "185--197",

journal = "International Journal of Advanced Intelligence Paradigms",

issn = "1755-0386",

publisher = "Inderscience Enterprises Ltd",

number = "2",

}

TY - JOUR

T1 - Learning to capture the functions of genetic regulatory networks using graph motifs

AU - Murthy, V. K.

AU - Krishnamurthy, E. V.

PY - 2012/7

Y1 - 2012/7

N2 - Network motifs have been suggested as a means to understand Systems biological functions. Based on several arguments from computer science, logic, mathematics, chemistry and non-linear dynamics, we explain the difficulties encountered in capturing the evolving dynamical functions carried out by the genetic regulatory networks (GRN), based on the assembly of motifs.

AB - Network motifs have been suggested as a means to understand Systems biological functions. Based on several arguments from computer science, logic, mathematics, chemistry and non-linear dynamics, we explain the difficulties encountered in capturing the evolving dynamical functions carried out by the genetic regulatory networks (GRN), based on the assembly of motifs.

KW - Adjacency matrices

KW - Assortative networks

KW - CN

KW - Complex network

KW - Disassortative networks

KW - Eigenvalues

KW - Graphs

KW - Lyapunov exponents

KW - Motifs

KW - Positive entropy

KW - System biology

UR - http://www.scopus.com/inward/record.url?scp=84864569238&partnerID=8YFLogxK

U2 - 10.1504/IJAIP.2012.048146

DO - 10.1504/IJAIP.2012.048146

M3 - Article

SN - 1755-0386

VL - 4

SP - 185

EP - 197

JO - International Journal of Advanced Intelligence Paradigms

JF - International Journal of Advanced Intelligence Paradigms

IS - 2

ER -

Learning to capture the functions of genetic regulatory networks using graph motifs

Abstract

Access to Document

Other files and links

Fingerprint

Cite this