scholarly journals Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3

2018 ◽  
pp. 217-233 ◽  
Author(s):  
Vân Anh Huynh-Thu ◽  
Guido Sanguinetti
Keyword(s):  
Regulatory Network ◽  
Network Topologies

scholarly journals Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Sven Thiele ◽  
Luca Cerone ◽  
Julio Saez-Rodriguez ◽  
Anne Siegel ◽  
Carito Guziołowski ◽  
...  
Keyword(s):  
Experimental Data ◽  
Regulatory Network ◽  
Network Topologies ◽  
Sign Consistency

scholarly journals Elucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patterns

2021 ◽  
Author(s):  
Juan Camilo Arboleda Rivera ◽  
Gloria Machado Rodriguez ◽  
Boris Anghelo Rodriguez Rey ◽  
Jayson Gutierrez Betancur
Keyword(s):  
Synthetic Biology ◽  
Gene Regulatory Network ◽  
Input Signal ◽  
Regulatory Network ◽  
Expression Patterns ◽  
Network Motif ◽  
Search Space ◽  
Emergent Properties ◽  
Network Topologies ◽  
Gene Regulatory

Background: A central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cell's gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. Methods: In this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. Results: Out of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.

scholarly journals The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods

2018 ◽  
Vol 373 (1750) ◽  
pp. 20170222 ◽  
Author(s):  
Johannes Meisig ◽  
Nils Blüthgen
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Body ◽  
Stem Cell Differentiation ◽  
Network Pruning ◽  
Reconstruction Performance ◽  
Network Topologies ◽  
Gene Regulatory

A large body of data have accumulated that characterize the gene regulatory network of stem cells. Yet, a comprehensive and integrative understanding of this complex network is lacking. Network reverse engineering methods that use transcriptome data to derive these networks may help to uncover the topology in an unbiased way. Many methods exist that use co-expression to reconstruct networks. However, it remains unclear how these methods perform in the context of stem cell differentiation, as most systematic assessments have been made for regulatory networks of unicellular organisms. Here, we report a systematic benchmark of different reverse engineering methods against functional data. We show that network pruning is critical for reconstruction performance. We also find that performance is similar for algorithms that use different co-expression measures, i.e. mutual information or correlation. In addition, different methods yield very different network topologies, highlighting the challenge of interpreting these resulting networks as a whole. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.

scholarly journals Discovering Regulatory Network Topologies Using Ensemble Methods on GPGPUs With Special Reference to the Biological Clock of Neurospora crassa

IEEE Access ◽  
2015 ◽  
Vol 3 ◽  
pp. 27-42 ◽  
Author(s):  
Ahmad Al-Omari ◽  
James Griffith ◽  
Michael Judge ◽  
Thiab Taha ◽  
Jonathan Arnold ◽  
...  
Keyword(s):  
Special Reference ◽  
Neurospora Crassa ◽  
Regulatory Network ◽  
Biological Clock ◽  
Ensemble Methods ◽  
Network Topologies

Construction of transcription factor mutual regulatory network based on a new database

2014 ◽  
Author(s):  
Zhenping Yang ◽  
Wei Wang ◽  
Yang Yang ◽  
Hongfei Chen ◽  
Jinke Wang
Keyword(s):  
Transcription Factor ◽  
Regulatory Network
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