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- ageing (1)
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- gene regulatory networks (1)
- irreversible volume-change (1)
- irreversible work share (1)
- memory and delay (1)
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Institute
Temperature-memory polymers remember the temperature, where they were deformed recently, enabled by broad thermal transitions. In this study, we explored a series of crosslinked poly[ethylene-co-(vinyl acetate)] networks (cPEVAs) comprising crystallizable polyethylene (PE) controlling units exhibiting a pronounced temperature-memory effect (TME) between 16 and 99 °C related to a broad melting transition (∼100 °C). The nanostructural changes in such cPEVAs during programming and activation of the TME were analyzed via in situ X-ray scattering and specific annealing experiments. Different contributions to the mechanism of memorizing high or low deformation temperatures (Tdeform) were observed in cPEVA, which can be associated to the average PE crystal sizes. At high deformation temperatures (>50 °C), newly formed PE crystals, which are established during cooling when fixing the temporary shape, dominated the TME mechanism. In contrast, at low Tdeform (<50 °C), corresponding to a cold drawing scenario, the deformation led preferably to a disruption of existing large crystals into smaller ones, which then fix the temporary shape upon cooling. The observed mechanism of memorizing a deformation temperature might enable the prediction of the TME behavior and the knowledge based design of other TMPs with crystallizable controlling units.
Recent experiments show that transcription factors (TFs) indeed use the facilitated diffusion mechanism to locate their target sequences on DNA in living bacteria cells: TFs alternate between sliding motion along DNA and relocation events through the cytoplasm. From simulations and theoretical analysis we study the TF-sliding motion for a large section of the DNA-sequence of a common E. coli strain, based on the two-state TF-model with a fast-sliding search state and a recognition state enabling target detection. For the probability to detect the target before dissociating from DNA the TF-search times self-consistently depend heavily on whether or not an auxiliary operator (an accessible sequence similar to the main operator) is present in the genome section. Importantly, within our model the extent to which the interconversion rates between search and recognition states depend on the underlying nucleotide sequence is varied. A moderate dependence maximises the capability to distinguish between the main operator and similar sequences. Moreover, these auxiliary operators serve as starting points for DNA looping with the main operator, yielding a spectrum of target detection times spanning several orders of magnitude. Auxiliary operators are shown to act as funnels facilitating target detection by TFs.
We define and study in detail utraslow scaled Brownian motion (USBM) characterized by a time dependent diffusion coefficient of the form . For unconfined motion the mean squared displacement (MSD) of USBM exhibits an ultraslow, logarithmic growth as function of time, in contrast to the conventional scaled Brownian motion. In a harmonic potential the MSD of USBM does not saturate but asymptotically decays inverse-proportionally to time, reflecting the highly non-stationary character of the process. We show that the process is weakly non-ergodic in the sense that the time averaged MSD does not converge to the regular MSD even at long times, and for unconfined motion combines a linear lag time dependence with a logarithmic term. The weakly non-ergodic behaviour is quantified in terms of the ergodicity breaking parameter. The USBM process is also shown to be ageing: observables of the system depend on the time gap between initiation of the test particle and start of the measurement of its motion. Our analytical results are shown to agree excellently with extensive computer simulations.
Stochastic Wilson
(2015)
We consider a simple Markovian class of the stochastic Wilson–Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around −1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence.
For the calculation of the work in an irreversible pressure-volume change, we propose approxima-tions, which in contrast to the usual representation in the literature reflect the work performed during expansion and compression symmetrically. The calculations are based on the Reversible-Share-Theorem: Is used the force to overcome for calculating the work, so it captures only the configurational reversible work share.