Fluctuation and noise propagation in phenotypic transition cascades of clonal populations

Pei, Qiuming; Zhan, Xuan; Yang, Lijian; Shen, Ju; Wang, Lifang; Qui, Kang; Liu, Ting; Kirunda, John Billy; Yousif, Alssadig A. M.; Li, An-bang; Jia, Ya

doi:10.1103/physreve.92.012721

Cited by 19 publications

(11 citation statements)

References 49 publications

(110 reference statements)

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“…To measure how the balance between production and elimination of N i is affected by N k (Paulsson, 2004, 2005; Pedraza and van Oudenaarden, 2005; Hornung and Barkai, 2008; Jia et al, 2009; Pei et al, 2015), N i and N k represent the numbers of the expression productions, respectively. i, k ∈ { x, y, z }.…”

Section: Mathematical Modeling and Analytical Noisementioning

confidence: 99%

Noise Decomposition Principle in a Coherent Feed-Forward Transcriptional Regulatory Loop

et al. 2016

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Coherent feed-forward loops exist extensively in realistic biological regulatory systems, and are common signaling motifs. Here, we study the characteristics and the propagation mechanism of the output noise in a coherent feed-forward transcriptional regulatory loop that can be divided into a main road and branch. Using the linear noise approximation, we derive analytical formulae for the total noise of the full loop, the noise of the branch, and the noise of the main road, which are verified by the Gillespie algorithm. Importantly, we find that (i) compared with the branch motif or the main road motif, the full motif can effectively attenuate the output noise level; (ii) there is a transition point of system state such that the noise of the main road is dominated when the underlying system is below this point, whereas the noise of the branch is dominated when the system is beyond the point. The entire analysis reveals the mechanism of how the noise is generated and propagated in a simple yet representative signaling module.

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Section: Mathematical Modeling and Analytical Noisementioning

confidence: 99%

Noise Decomposition Principle in a Coherent Feed-Forward Transcriptional Regulatory Loop

et al. 2016

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“…To study the effects of inhibitory synapses on the abovementioned topologies is still meaningful. Also, both various noise and time delay of information transmission [47,48] should also be introduced to further investigation.…”

Section: Discussionmentioning

confidence: 99%

Synchronization mode transitions induced by chaos in modified Morris–Lecar neural systems with weak coupling

Wang

et al. 2022

Nonlinear Dyn

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Based on a modified Morris-Lecar neural model, the synchronization modes transitions between two coupled neurons or starcoupled neural network connected by weak electrical and chemical coupling are respectively investigated. For the two coupled neurons, by increasing the calcium conductivity, it is found that the period2 synchronization of action potential of neurons is transformed to desynchronization first, and then to period3 synchronization. By increasing the potassium conductivity, however, the synchronization mode transition is a reversal direction process as mentioned above. The bifurcation analysis of interspike interval shows that the synchronization modes transition is induced by the chaos. The stronger the coupling strength is, the smaller the period2 synchronization region in the parameters plane is, while the larger the period3 synchronization region will be. For the starcoupled neural network, in the presence of weak electrical coupling, it can exhibit the complete synchronization, desynchronization, and drum head mode states under different parameter values, respectively. In the presence of chemical synapse, however, the completely synchronized state can not be observed in the starcoupled neural network. Our results might provide novel insights into synchronization modes transition and related biological experiments.

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“…We consider a homogeneous situation (i.e., D 1 = D 2 = D ), which means that the noises in Eqs ( 3 and 4 ) represent the total effect of intrinsic and extrinsic noises. The probability distribution of system (3) and (4) obeys the Fokker-Planck equation 38 , 39 : …”

Section: Model and Methodsmentioning

confidence: 99%

Modeling of mesenchymal hybrid epithelial state and phenotypic transitions in EMT and MET processes of cancer cells

Qiu

Jia

2018

Sci Rep

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Based on the transcriptional regulatory mechanisms between microRNA-200 and transcription factor ZEB in an individual cancer cell, a minimal dynamic model is proposed to study the epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) processes of cancer cells. It is shown that each cancer cell can exit in any of three phenotypic states: the epithelial (E) state, the mesenchymal (M) state, and the epithelial/mesenchymal (E/M) hybrid state, and the state of cancer cell can interconvert between different states. The phase diagram shows that there are monostable, bistable, and tristable phenotypic states regions in a parameters plane. It is found that different pathway in the phase diagram can correspond to the EMT or the MET process of cancer cells, and there are two possible EMT processes. It is important that the experimental phenomenon of E/M hybrid state appearing in the EMT process but rather in the MET process can be understood through different pathways in the phase diagram. Our numerical simulations show that the effects of noise are opposite to these of time delay on the expression of transcription factor ZEB, and there is competition between noise and time delay in phenotypic transitions process of cancer cells.

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Fluctuation and noise propagation in phenotypic transition cascades of clonal populations

Cited by 19 publications

References 49 publications

Noise Decomposition Principle in a Coherent Feed-Forward Transcriptional Regulatory Loop

Noise Decomposition Principle in a Coherent Feed-Forward Transcriptional Regulatory Loop

Synchronization mode transitions induced by chaos in modified Morris–Lecar neural systems with weak coupling

Modeling of mesenchymal hybrid epithelial state and phenotypic transitions in EMT and MET processes of cancer cells

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