142 related articles for article (PubMed ID: 35786725)
1. Unraveling the stochastic transition mechanism between oscillation states by the landscape and the minimum action path theory.
Lang J; Li C
Phys Chem Chem Phys; 2022 Aug; 24(34):20050-20063. PubMed ID: 35786725
[TBL] [Abstract][Full Text] [Related]
2. Landscape and flux govern cellular mode-hopping between oscillations.
Li C; Ye L
J Chem Phys; 2019 Nov; 151(17):175101. PubMed ID: 31703512
[TBL] [Abstract][Full Text] [Related]
3. Landscape, flux, correlation, resonance, coherence, stability, and key network wirings of stochastic circadian oscillation.
Li C; Wang E; Wang J
Biophys J; 2011 Sep; 101(6):1335-44. PubMed ID: 21943414
[TBL] [Abstract][Full Text] [Related]
4. Landscape and flux quantify the stochastic transition dynamics for p53 cell fate decision.
Ye L; Song Z; Li C
J Chem Phys; 2021 Jan; 154(2):025101. PubMed ID: 33445890
[TBL] [Abstract][Full Text] [Related]
5. Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition.
Lang J; Nie Q; Li C
Biophys J; 2021 Oct; 120(20):4484-4500. PubMed ID: 34480928
[TBL] [Abstract][Full Text] [Related]
6. Potential and flux field landscape theory. I. Global stability and dynamics of spatially dependent non-equilibrium systems.
Wu W; Wang J
J Chem Phys; 2013 Sep; 139(12):121920. PubMed ID: 24089732
[TBL] [Abstract][Full Text] [Related]
7. Funneled potential and flux landscapes dictate the stabilities of both the states and the flow: Fission yeast cell cycle.
Luo X; Xu L; Han B; Wang J
PLoS Comput Biol; 2017 Sep; 13(9):e1005710. PubMed ID: 28892489
[TBL] [Abstract][Full Text] [Related]
8. Quantifying the Landscape of Decision Making From Spiking Neural Networks.
Ye L; Li C
Front Comput Neurosci; 2021; 15():740601. PubMed ID: 34776914
[TBL] [Abstract][Full Text] [Related]
9. Minimum Action Path Theory Reveals the Details of Stochastic Transitions Out of Oscillatory States.
de la Cruz R; Perez-Carrasco R; Guerrero P; Alarcon T; Page KM
Phys Rev Lett; 2018 Mar; 120(12):128102. PubMed ID: 29694079
[TBL] [Abstract][Full Text] [Related]
10. Macrophage phenotype transitions in a stochastic gene-regulatory network model.
Frank AJ; Larripa K; Ryu H; Röblitz S
J Theor Biol; 2023 Nov; 575():111634. PubMed ID: 37839584
[TBL] [Abstract][Full Text] [Related]
11. Unifying deterministic and stochastic ecological dynamics via a landscape-flux approach.
Xu L; Patterson D; Staver AC; Levin SA; Wang J
Proc Natl Acad Sci U S A; 2021 Jun; 118(24):. PubMed ID: 34117123
[TBL] [Abstract][Full Text] [Related]
12. Robustness and coherence of a three-protein circadian oscillator: landscape and flux perspectives.
Wang J; Xu L; Wang E
Biophys J; 2009 Dec; 97(11):3038-46. PubMed ID: 19948134
[TBL] [Abstract][Full Text] [Related]
13. The energy pump and the origin of the non-equilibrium flux of the dynamical systems and the networks.
Xu L; Shi H; Feng H; Wang J
J Chem Phys; 2012 Apr; 136(16):165102. PubMed ID: 22559506
[TBL] [Abstract][Full Text] [Related]
14. Quantifying the landscape and kinetic paths for epithelial-mesenchymal transition from a core circuit.
Li C; Hong T; Nie Q
Phys Chem Chem Phys; 2016 Jul; 18(27):17949-56. PubMed ID: 27328302
[TBL] [Abstract][Full Text] [Related]
15. Exploring the Underlying Mechanisms of the Xenopus laevis Embryonic Cell Cycle.
Zhang K; Wang J
J Phys Chem B; 2018 May; 122(21):5487-5499. PubMed ID: 29310435
[TBL] [Abstract][Full Text] [Related]
16. Potential landscape and probabilistic flux of a predator prey network.
Li C; Wang E; Wang J
PLoS One; 2011 Mar; 6(3):e17888. PubMed ID: 21423576
[TBL] [Abstract][Full Text] [Related]
17. Energy Landscape Analysis of the Epithelial-Mesenchymal Transition Network.
Ye L; Li C
Methods Mol Biol; 2022; 2488():145-157. PubMed ID: 35347688
[TBL] [Abstract][Full Text] [Related]
18. Comparison of Deterministic and Stochastic Regime in a Model for Cdc42 Oscillations in Fission Yeast.
Xu B; Kang HW; Jilkine A
Bull Math Biol; 2019 May; 81(5):1268-1302. PubMed ID: 30756233
[TBL] [Abstract][Full Text] [Related]
19. Transition path time distributions.
Laleman M; Carlon E; Orland H
J Chem Phys; 2017 Dec; 147(21):214103. PubMed ID: 29221402
[TBL] [Abstract][Full Text] [Related]
20. Quantifying the Landscape and Transition Paths for Proliferation-Quiescence Fate Decisions.
Chen Z; Li C
J Clin Med; 2020 Aug; 9(8):. PubMed ID: 32784979
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
