Dynamical Behaviors of Rb-E2F Pathway Including Negative Feedback Loops Involving miR449

<div><p>MiRNAs, which are a family of small non-coding RNAs, regulate a broad array of physiological and developmental processes. However, their regulatory roles have remained largely mysterious. E2F is a positive regulator of cell cycle progression and also a potent inducer of apoptosis. Positive feedback loops in the regulation of Rb-E2F pathway are predicted and shown experimentally. Recently, it has been discovered that E2F induce a cluster of miRNAs called miR449. In turn, E2F is inhibited by miR449 through regulating different transcripts, thus forming negative feedback loops in the interaction network. Here, based on the integration of experimental evidence and quantitative data, we studied Rb-E2F pathway coupling the positive feedback loops and negative feedback loops mediated by miR449. Therefore, a mathematical model is constructed based in part on the model proposed in Yao-Lee et al. (2008) and nonlinear dynamical behaviors including the stability and bifurcations of the model are discussed. A comparison is given to reveal the implication of the fundamental differences of Rb-E2F pathway between regulation and deregulation of miR449. Coherent with the experiments it predicts that miR449 plays a critical role in regulating the cell cycle progression and provides a twofold safety mechanism to avoid excessive E2F-induced proliferation by cell cycle arrest and apoptosis. Moreover, numerical simulation and bifurcation analysis shows that the mechanisms of the negative regulation of miR449 to three different transcripts are quite distinctive which needs to be verified experimentally. This study may help us to analyze the whole cell cycle process mediated by other miRNAs more easily. A better knowledge of the dynamical behaviors of miRNAs mediated networks is also of interest for bio-engineering and artificial control.</p></div

Bifurcation diagram of [E2F] with as a control parameter at .

<p>Set the AUTO axes to run from 0 to 1.2 along the x-axis and from −0.05 to 1.25 along the y-axis. The initial values for the simulation are , [E2F] = 0, [MiR449] = 0.004, [Myc] = 0.0280, [Cdk6] = 0.0090, [CycE] = 0, [Rb] = 2.9918, [PRb] = 0.0004, [RE] = 0.0157.</p

Time courses of [E2F] and [CycE-Cdk2] at .

<p>Assume initial conditions are [E2F] = 0, [MiR449] = 0, [Myc] = 0, [Cdk6] = 0, [CycE] = 0, [Rb] = 0.55, [PRb] = 0, [RE] = 0. Set the Viewaxes run from 0 to 200 along the x-axis and from 0 to 1.25 along the y-axis.(a) ; (b) .</p

Rb-E2F pathway mediated by miR449.

<p>Rb-E2F circuit coupling the positive feedback loops and negative feedback loops mediated by miR449. E2F represents all E2F activators (E2F1, E2F2 and E2F3a). Rb represents all pocket proteins (Rb, p130 and p107). MiR449 represents miR449 family (miR449a, miR449b and miR449c).</p

Bifurcation diagram of [E2F] with S as a control parameter at .

<p>Set the AUTO axes to run from 0 to 5 along the x-axis and from 0 to 0.75 along the y-axis. Initial values as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043908#pone-0043908-g002" target="_blank">Fig. 2</a>.</p

Parameters values for the mathematical model.

<p>Parameters values for the mathematical model.</p

Bifurcation diagram of [CycE-Cdk2] with as a control parameter at .

<p>Set the AUTO axes to run from 0 to 5 along the x-axis and from −0.001 to 0.13 along the y-axis. Initial values as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043908#pone-0043908-g002" target="_blank">Fig. 2</a>.</p

Image2_Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study.PNG

Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared.Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment.Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.</p

Time courses of [E2F], [CycE-Cdk2] and [MiR449] at .

<p>Assume initial conditions are [E2F] = 1.2, [MiR449] = 0, [Myc] = 0, [Cdk6] = 0, [CycE] = 0, [Rb] = 0.55, [PRb] = 0, [RE] = 0. (a) . Set the Viewaxes run from 0 to 200 along the x-axis and from 0 to 2 along the y-axis; (b) . Set the Viewaxes run from 0 to 200 along the x-axis and from 0 to 1.7 along the y-axis.</p

Time courses of [E2F], [CycE-Cdk2] and [MiR449] at .

<p>Assume initial conditions are [E2F] = 1.2, [MiR449] = 0, [Myc] = 0, [Cdk6] = 0, [CycE] = 0, [Rb] = 0.55, [PRb] = 0, [RE] = 0. (a) . Set the Viewaxes run from 0 to 150 along the x-axis and from 0 to 1.8 along the y-axis; (b) . Set the Viewaxes run from 0 to 25 along the x-axis and from 0 to 3.3 along the y-axis.</p