A Dimension Reduction Approach for Energy Landscape: Identifying Intermediate States in Metabolism‐EMT Network

Kang, Xiaoyang; Li, Chunhe

doi:10.1002/advs.202003133

Cited by 33 publications

(36 citation statements)

References 57 publications

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“…For EMT, cells in the E state need to enter the A state first , and then enter the M state, while for the MET, before the cells in the M state reaching the E state, cells are likely to enter the H state first. 143 . Therefore, tumor cells in the mesenchymal state can still transform into the pEMT phenotype when the microenvironment changes.…”

Section: Discussionmentioning

confidence: 99%

Partial EMT in Squamous Cell Carcinoma: A Snapshot

Liao¹,

Wang²,

An³

et al. 2021

Int. J. Biol. Sci.

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In the process of cancer EMT, some subgroups of cancer cells simultaneously exhibit both mesenchymal and epithelial characteristics, a phenomenon termed partial EMT (pEMT). pEMT is a plastic state in which cells coexpress epithelial and mesenchymal markers. In squamous cell carcinoma (SCC), pEMT is regulated, and the phenotype is maintained via the HIPPO pathway, NOTCH pathway and TGF-β pathways and by microRNAs, lncRNAs and the cancer microenvironment (CME); thus, SCC exhibits aggressive tumorigenic properties and high stemness, which leads collective migration and therapy resistance. Few studies have reported therapeutic interventions to address cells that have undergone pEMT, and this approach may be an effective way to inhibit the plasticity, drug resistance and metastatic potential of SCC.

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Section: Discussionmentioning

confidence: 99%

Partial EMT in Squamous Cell Carcinoma: A Snapshot

Liao¹,

Wang²,

An³

et al. 2021

Int. J. Biol. Sci.

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“…Traditional GRN-based landscape can be hindered by the computational issue raised by high-dimensional GRNs. Recently, a model-based dimension reduction approach of the landscape (DRL) was proposed to construct a low-dimensional energy landscape of high-dimensional GRNs [ 36 ], which overcomes the limitations of traditional methods. Although great success has been achieved, the GRN-based landscape depends on prior biological knowledge of the underlying GRN.…”

Section: Discussionmentioning

confidence: 99%

“…Modelling of cell development has long been a key goal of systems biology.The Waddington landscape is a classic metaphor for describing cell development. Mathematical framework of cell developmental energy landscape has been developed to study the dynamics of cell statetransitions from gene regulatory network (GRN) based perspective (e.g., [33][34][35][36]) and state manifold based perspective (e.g., [5,37,38]) (see [18] for a recent review of the two approaches). Traditional GRN-based landscape can be hindered by the computational issue raised by high-dimensional GRNs.…”

Section: Discussionmentioning

confidence: 99%

Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data

2022

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Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, dynamic inference of an evolving cell population from time series scRNA-seq data is challenging owing to the stochasticity and nonlinearity of the underlying biological processes. This calls for the development of mathematical models and methods capable of reconstructing cellular dynamic transition processes and uncovering the nonlinear cell-cell interactions. In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data. The free energy of our model explicitly takes into account of the cell-cell interactions in a nonlinear quadratic term. We then recast the model inference problem in the form of a dynamic optimal transport framework and solve it efficiently with the adjoint method of optimal control. We evaluated GraphFP on the time series scRNA-seq data set of embryonic murine cerebral cortex development. We illustrated that it 1) reconstructs cell state potential energy, which is a measure of cellular differentiation potency, 2) faithfully charts the probability flows between paired cell states over the dynamic processes of cell differentiation, and 3) accurately quantifies the stochastic dynamics of cell type frequencies on probability simplex in continuous time. We also illustrated that GraphFP is robust in terms of cluster labelling with different resolutions, as well as parameter choices. Meanwhile, GraphFP provides a model-based approach to delineate the cell-cell interactions that drive cell differentiation. GraphFP software is available at https://github.com/QiJiang-QJ/GraphFP.

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“…This may introduce inaccuracy for analyzing original high-dimensional system, quantitatively. So, it's also important to develop other dimensional reduction approach (Kang and Li, 2021). Furthermore, the local circuit model we studied here only involves individual brain area, while a biologically realistic decision-making may be distributed, engaging multiple brain regions (Siegel et al, 2015;Steinmetz et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Quantifying the Landscape of Decision Making From Spiking Neural Networks

2021

Front. Comput. Neurosci.

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The decision making function is governed by the complex coupled neural circuit in the brain. The underlying energy landscape provides a global picture for the dynamics of the neural decision making system and has been described extensively in the literature, but often as illustrations. In this work, we explicitly quantified the landscape for perceptual decision making based on biophysically-realistic cortical network with spiking neurons to mimic a two-alternative visual motion discrimination task. Under certain parameter regions, the underlying landscape displays bistable or tristable attractor states, which quantify the transition dynamics between different decision states. We identified two intermediate states: the spontaneous state which increases the plasticity and robustness of changes of minds and the “double-up” state which facilitates the state transitions. The irreversibility of the bistable and tristable switches due to the probabilistic curl flux demonstrates the inherent non-equilibrium characteristics of the neural decision system. The results of global stability of decision-making quantified by barrier height inferred from landscape topography and mean first passage time are in line with experimental observations. These results advance our understanding of the stochastic and dynamical transition mechanism of decision-making function, and the landscape and kinetic path approach can be applied to other cognitive function related problems (such as working memory) in brain networks.

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A Dimension Reduction Approach for Energy Landscape: Identifying Intermediate States in Metabolism‐EMT Network

Cited by 33 publications

References 57 publications

Partial EMT in Squamous Cell Carcinoma: A Snapshot

Partial EMT in Squamous Cell Carcinoma: A Snapshot

Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data

Quantifying the Landscape of Decision Making From Spiking Neural Networks

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