Random propagation in complex systems : nonlinear matrix recursions and epidemic spread

This dissertation studies long-term behavior of random Riccati recursions and mathematical epidemic model. Riccati recursions are derived from Kalman filtering. The error covariance matrix of Kalman filtering satisfies Riccati recursions. Convergence condition of time-invariant Riccati recursions are well-studied by researchers. We focus on time-varying case, and assume that regressor matrix is random and identical and independently distributed according to given distribution whose probability distribution function is continuous, supported on whole space, and decaying faster than any polynomial. We study the geometric convergence of the probability distribution. We also study the global dynamics of the epidemic spread over complex networks for various models. For instance, in the discrete-time Markov chain model, each node is either healthy or infected at any given time. In this setting, the number of the state increases exponentially as the size of the network increases. The Markov chain has a unique stationary distribution where all the nodes are healthy with probability 1. Since the probability distribution of Markov chain defined on finite state converges to the stationary distribution, this Markov chain model concludes that epidemic disease dies out after long enough time. To analyze the Markov chain model, we study nonlinear epidemic model whose state at any given time is the vector obtained from the marginal probability of infection of each node in the network at that time. Convergence to the origin in the epidemic map implies the extinction of epidemics. The nonlinear model is upper-bounded by linearizing the model at the origin. As a result, the origin is the globally stable unique fixed point of the nonlinear model if the linear upper bound is stable. The nonlinear model has a second fixed point when the linear upper bound is unstable. We work on stability analysis of the second fixed point for both discrete-time and continuous-time models. Returning back to the Markov chain model, we claim that the stability of linear upper bound for nonlinear model is strongly related with the extinction time of the Markov chain. We show that stable linear upper bound is sufficient condition of fast extinction and the probability of survival is bounded by nonlinear epidemic map.

Brf1 post-transcriptionally regulates pluripotency and differentiation responses downstream of Erk MAP Kinase

FGF/Erk MAP Kinase Signaling is a central regulator of mouse embryonic stem cell (mESC) self-renewal, pluripotency and differentiation. However, the mechanistic connection between this signaling pathway activity and the gene circuits stabilizing mESCs in vitro remain unclear. Here we show that FGF signaling post-transcriptionally regulates the mESC transcription factor network by controlling the expression of Brf1 (zfp36l1), an AU-rich element mRNA binding protein. Changes in Brf1 level disrupts the expression of core pluripotency-associated genes and attenuates mESC self-renewal without inducing differentiation. These regulatory effects are mediated by rapid and direct destabilization of Brf1 targets, such as Nanog mRNA. Interestingly, enhancing Brf1 expression does not compromise mESC pluripotency, but does preferentially regulate differentiation to mesendoderm by accelerating the expression of primitive streak markers. Together, these studies demonstrate that FGF signals utilize targeted mRNA degradation by Brf1 to enable rapid post-transcriptional control of gene expression.