Quantitative Modelling of the Waddington Epigenetic Landscape

Fard, Atefeh Taherian; Ragan, Mark A.

doi:10.1007/978-1-4939-9224-9_7

Cited by 11 publications

(5 citation statements)

References 43 publications

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“…Data-driven methods typically reconstruct landscapes from gene expression data, including Hopfield network-based methods and entropy-based methods. Hopfield network, a classic type of artificial neural network, is an associative memory model, which can store input patterns as attractors in the network and the patterns can be recalled from partial input [26]. So far, most of the data-driven methods use the Hopfield network to model the epigenetic landscape.…”

Section: Data-driven Methodsmentioning

confidence: 99%

TMELand: An end-to-end Pipeline for Quantification and Visualization of Waddington's Epigenetic Landscape based on Gene Regulatory Network

Zhu

Kang

et al. 2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Waddington's epigenetic landscape is a framework depicting the processes of cell differentiation and reprogramming under the control of a gene regulatory network (GRN). Traditional model-driven methods for landscape quantification focus on the Boolean network or differential equation-based models of GRN, which need sophisticated prior knowledge and hence hamper their practical applications. To resolve this problem, we combine data-driven methods for inferring GRNs from gene expression data with model-driven approach to the landscape mapping. Specifically, we build an end-to-end pipeline to link data-driven and model-driven methods and develop a software tool named TMELand for GRN inference, visualizing Waddington's epigenetic landscape, and calculating state transition paths between attractors to uncover the intrinsic mechanism of cellular transition dynamics. By integrating GRN inference from real transcriptomic data with landscape modeling, TMELand can facilitate studies of computational systems biology, such as predicting cellular states and visualizing the dynamical trends of cell fate determination and transition dynamics from single-cell transcriptomic data. The source code of TMELand, a user manual, and model files of case studies can be downloaded freely from https://github.com/JieZheng-ShanghaiTech/TMELand.

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Section: Data-driven Methodsmentioning

confidence: 99%

TMELand: An end-to-end Pipeline for Quantification and Visualization of Waddington's Epigenetic Landscape based on Gene Regulatory Network

Zhu

Kang

et al. 2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…The output of each node in the network is deduced from the interaction between this node and the input of other nodes. The interaction between i th and j th nodes is called connection w ij and { w ij } constitutes a weight matrix [ 31 , 55 ]. One of the fascinating aspects of the HNN is, like the other neural network, its function as an associate memory with a surprising fault tolerance with respect to both input data errors and internal failures.…”

Section: Methodsmentioning

confidence: 99%

Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator

Li¹,

Long²,

Yan³

et al. 2023

Research

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The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.

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“…However, due to the complexity and noise (such as the molecular noise discussed in Sec. 3.2.1), the landscape for the cell developmental processes of multicellular organisms maybe quite bumpy, and during the cell differentiation process, stochastic state transitions may happen [413,414,415]. For example, Wang at.al [400] considered the effect of stochastic fluctuations in a canonical gene circuit.…”

Section: Potential Landscapes Of Cellular Processes In Complex Multic...mentioning

confidence: 99%

Network resilience

Liu,

Li,

et al. 2020

Preprint

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Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.

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Quantitative Modelling of the Waddington Epigenetic Landscape

Cited by 11 publications

References 43 publications

TMELand: An end-to-end Pipeline for Quantification and Visualization of Waddington's Epigenetic Landscape based on Gene Regulatory Network

TMELand: An end-to-end Pipeline for Quantification and Visualization of Waddington's Epigenetic Landscape based on Gene Regulatory Network

Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator

Network resilience

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