Modular genetic regulatory networks increase organization during pattern formation.
Studies have shown that genetic regulatory networks (GRNs) consist of modules that are densely connected subnetworks that function quasi-autonomously. Modules may be recognized motifs that comprise of two or three genes with particular regulatory functions and connectivity or be purely structural and identified through connection density. It is unclear what evolutionary and developmental advantages modular structure and in particular motifs provide that have led to this enrichment. This study seeks to understand how modules within developmental GRNs influence the complexity of multicellular patterns that emerge from the dynamics of the regulatory networks. We apply an algorithmic complexity to measure the organization of the patterns. A computational study was performed by creating Boolean intracellular networks within a simulated epithelial field of embryonic cells, where each cell contains the same network and communicates with adjacent cells using contact-mediated signaling. Intracellular networks with random connectivity were compared to those with modular connectivity and with motifs. Results show that modularity effects network dynamics and pattern organization significantly. In particular: (1) modular connectivity alone increases complexity in network dynamics and patterns; (2) bistable switch motifs simplify both the pattern and network dynamics; (3) all other motifs with feedback loops increase multicellular pattern complexity while simplifying the network dynamics; (4) negative feedback loops affect the dynamics complexity more significantly than positive feedback loops.
Department of Computer Science, Utah State University, United States. Electronic address: hmd.mohamadlou@gmail.com.
Hamid
H
Mohamadlou
Department of Biology, Utah State University, United States; Center for Integrated BioSystems, Utah State University, United States. Electronic address: gregory.podgorski@usu.edu.
Gregory J
GJ
Podgorski
Department of Computer Science, Utah State University, United States; Institute for Systems Biology, Seattle, United States. Electronic address: nick.flann@usu.edu.
Nicholas S
NS
Flann
D000465:Algorithms; D000818:Animals; D019521:Body Patterning / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D004331:Drosophila melanogaster / Q000196:embryology / Q000235:genetics; D004625:Embryo, Nonmammalian / Q000166:cytology / Q000196:embryology / Q000378:metabolism; D025461:Feedback, Physiological; D018507:Gene Expression Regulation, Developmental; D053263:Gene Regulatory Networks / Q000235:genetics*; D008957:Models, Genetic
Genetic regulatory networks; Kolmogorov complexity; Network motifs; Pattern formation