The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which extends the cellular hierarchy proposed by the classical cancer stem cell (CSC) theory. Since it is still questionable if the phenotypic plasticity is a crucial improvement to the hierarchical model or just a minor extension to it, it is worthwhile to explore the dynamic behavior characterizing the reversible phenotypic plasticity. In this study we compare the hierarchical model and the reversible model in predicting the cell-state dynamics observed in biological experiments. Our results show that the hierarchical model shows significant disadvantages over the reversible model in describing both long-term stability (phenotypic equilibrium) and short-term transient dynamics (overshoot) in cancer cell populations. In a very specific case in which the total growth of population due to each cell type is identical, the hierarchical model predicts neither phenotypic equilibrium nor overshoot, whereas the reversible model succeeds in predicting both of them. Even though the performance of the hierarchical model can be improved by relaxing the specific assumption, its prediction to the phenotypic equilibrium strongly depends on a precondition that may be unrealistic in biological experiments. Moreover, it still does not show as rich dynamics as the reversible model in capturing the overshoots of both CSCs and non-CSCs. By comparison, it is more likely for the reversible model to correctly predict the stability of the phenotypic mixture and various types of overshoot behavior.
Robustness is a fundamental characteristic of biological systems since all living systems need to adapt to internal or external perturbations, unpredictable environments, stochastic events and unreliable components, and so on. A long-term challenge in systems biology is to reveal the origin of robustness underlying molecular regulator network. In this study, a simple Boolean model is used to investigate the global dynamic properties and robustness of cardiac progenitor cell (CPC) induced pluripotent stem cell network that governs reprogramming and directed differentiation process. It is demonstrated that two major attractors correspond to source and target cell phenotypes, respectively, and two dominating attracting trajectories characterise the biological pathways between two major cell phenotypes. In particular, the experimentally observed transition between different cell phenotypes can be reproduced and explained theoretically. Furthermore, the robustness of major attractors and trajectories is largely maintained with respect to small perturbations to the network. Taken together, the CPC-induced pluripotent stem cell network is extremely robustly designed for their functions.
We numerically investigate the transmission of time-modulated random point trains in a conductance-based neuron model by including shot noise described as additive noise trains. The results show that additive noise trains can induce neuron responses exhibiting correlation with the temporally modulated random point trains. In addition, the additive noise power density can be increased up to an optimal value where the output signal-noise ratio (SNR) reaches a maximum value. This property of noise-enhanced transmission of random point trains can be related to the stochastic resonance (SR) phenomenon. More interestingly, we find that the SNR gain can exceed unity and can also be optimized by tuning the average rate of the input random point trains. The present study illustrates the potential to utilize the additive noise and temporally modulated random point trains for optimizing the response of the neuron to inputs, as well as a guidance in the design of information processing devices to random neuron spiking.
The collective response of an ensemble of leaky integrate-and-fire neurons induced by local correlated noise is investigated theoretically. Based on the linear response theory, we derive the analytic expression of signal-to-noise ratio (SNR). Numerical results show that the amplitude of internal noise can be increased up to an optimal value where the output SNR reaches a maximum value. Interestingly, we find that the correlated noise between the nearest neurons could lead to the obvious SNR gain. We also show that the SNR can reach unity under condition that the correlated noise between the nearest neurons is negative. This nonlinear amplification of SNR gain in an ensemble of noisy neurons can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and amplitude of the weak periodic signal.
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