scholarly journals ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information

2016 ◽  
Vol 32 (14) ◽  
pp. 2233-2235 ◽  
Author(s):  
Alexander Lachmann ◽  
Federico M. Giorgi ◽  
Gonzalo Lopez ◽  
Andrea Califano
Keyword(s):  
Mutual Information ◽  
Reverse Engineering ◽  
Gene Network ◽  
Adaptive Partitioning

scholarly journals SJARACNe: a scalable software tool for gene network reverse engineering from big data

2018 ◽  
Vol 35 (12) ◽  
pp. 2165-2166 ◽  
Author(s):  
Alireza Khatamian ◽  
Evan O Paull ◽  
Andrea Califano ◽  
Jiyang Yu
Keyword(s):  
Big Data ◽  
Reverse Engineering ◽  
Gene Network ◽  
Software Tool ◽  
Scalable Software

scholarly journals Reverse Engineering Gene Network Identifies New Dysferlin-interacting Proteins

2010 ◽  
Vol 286 (7) ◽  
pp. 5404-5413 ◽  
Author(s):  
Mafalda Cacciottolo ◽  
Vincenzo Belcastro ◽  
Steve Laval ◽  
Kate Bushby ◽  
Diego di Bernardo ◽  
...  
Keyword(s):  
Reverse Engineering ◽  
Gene Network ◽  
Interacting Proteins

A Gene Network Simulator to Assess Reverse Engineering Algorithms

2009 ◽  
Vol 1158 (1) ◽  
pp. 125-142 ◽  
Author(s):  
Barbara Di Camillo ◽  
Gianna Toffolo ◽  
Claudio Cobelli
Keyword(s):  
Reverse Engineering ◽  
Gene Network ◽  
Network Simulator

scholarly journals Bursty Expression Enhances Reliability of Messaging in Gene Networks

2020 ◽  
Author(s):  
Leonardo R. Gama ◽  
Guilherme Giovanini ◽  
Gábor Balázsi ◽  
Alexandre F. Ramos
Keyword(s):  
Transcription Factors ◽  
Mutual Information ◽  
Gene Networks ◽  
Gene Network ◽  
The State ◽  
Expression Levels ◽  
Temporal Precision ◽  
Spatio Temporal ◽  
Steady State Solutions ◽  
Slow Switching

AbstractThe promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by the Shannon’s entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter having long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a bursty regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon’s theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.

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