TY - JOUR
T1 - Relative stability of network states in Boolean network models of gene regulation in development
AU - Zhou, Joseph Xu
AU - Samal, Areejit
AU - d'Hérouël, Aymeric Fouquier
AU - Price, Nathan D.
AU - Huang, Sui
N1 - Funding Information:
Research reported in this publication was supported by the Center for Systems Biology/2P50GM076547 of the National Institutes of Health under award R01GM987654. This research was also supported by the National Science Foundation Grant PHY11-25915. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Funding Information:
Research reported in this publication was supported by the Center for Systems Biology/2P50GM076547 of the National Institutes of Health under award R01GM987654. This research was also supported by the National Science Foundation Grant PHY11-25915. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Publisher Copyright:
© 2016 Elsevier Ireland Ltd.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.
AB - Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.
KW - Attractor states
KW - Boolean network (BN)
KW - Cell differentiation
KW - Epigenetic landscape
KW - Gene regulatory network (GRN)
KW - Multistable dynamical system
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U2 - 10.1016/j.biosystems.2016.03.002
DO - 10.1016/j.biosystems.2016.03.002
M3 - Article
C2 - 26965665
AN - SCOPUS:84960448086
SN - 0303-2647
VL - 142-143
SP - 15
EP - 24
JO - BioSystems
JF - BioSystems
ER -