Abstract
SummaryNetwork structures describing regulation between biomolecules have been determined in many biological systems. Dynamics of molecular activities based on such networks are considered to be the origin of many biological functions. Recently, it has been proved mathematically that key nodes for controlling dynamics in networks are identified from network structure alone. Here, we applied this theory to a gene regulatory network for the cell fate specification of seven tissues in the ascidian embryo and found that this network, which consisted of 92 factors, had five key molecules. By controlling the activities of these key molecules, the specific gene expression of six of seven tissues observed in the embryo was successfully reproduced. Since this method is applicable to all nonlinear dynamic systems, we propose this method as a tool for controlling gene regulatory networks and reprogramming cell fates.
Highlights
Network systems produce dynamics of molecular activity in organisms, and such dynamics are thought to be the origin of biological functions (Alon, 2007; Oda et al, 2005; Peter and Davidson, 2016)
The network structure for the specification of cell fate has been determined by a genome-wide gene knockdown assay for regulatory genes that are expressed during embryogenesis (Imai et al, 2006) and was recently updated using data that had been accumulated after the initial construction (Satou and Imai, 2015)
If the fate decision is based on the steady states of this network, cell-type-specific gene expression patterns should be reproduced by manipulating the activities of feedback vertex set (FVS) in the network
Summary
Network systems produce dynamics of molecular activity in organisms, and such dynamics are thought to be the origin of biological functions (Alon, 2007; Oda et al, 2005; Peter and Davidson, 2016). We recently developed a new theoretical framework (linkage logic theory) (Fiedler et al, 2013; Mochizuki, 2008; Mochizuki et al, 2013), with which key nodes for controlling nonlinear dynamics are identified only from network structure without assuming quantitative details, such as functional forms, parameters, or initial states. According to this theory, the dynamics of a system is controllable to converge on any solution by controlling a subset of nodes called a feedback vertex set (FVS). We show that the minimum FVSs of this network contain only five factors and that the dynamics of the GRN is controllable by these five FVS factors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
