Addendum to "Nonlinear quantum evolution with maximal entropy production"

The author calls attention to previous work with related results, which has escaped scrutiny before the publication of the article "Nonlinear quantum evolution with maximal entropy production", Phys.Rev.A63, 022105 (2001).Comment: RevTex-latex2e, 2pgs., no figs.; brief report to appear in the May 2001 issue of Phys.Rev.

Epigenetic regulation of Mash1 expression

Mash1 is a proneural gene important for specifying the neural fate. The Mash1 locus undergoes specific epigenetic changes in ES cells following neural induction. These include the loss of repressive H3K27 trimethylation and acquisition of H3K9 acetylation at the promoter, switch to an early replication timing and repositioning of the locus away from the nuclear periphery. Here I examine the relationship between nuclear localization and gene expression during neural differentiation and the role of the neuronal repressor REST in silencing Mash1 expression in ES cells. Following neural induction of ES cells, I observed that relocation of the Mash1 locus occurs from day 4-6 whereas overt expression begins at day 6. Mash1 expression was unaffected by REST removal in ES cells as well as the locus localization at the nuclear periphery. In contrast bona fide REST target genes were upregulated in REST -/- cells. Interestingly, among REST targets, loci that were more derepressed upon REST removal showed an interior location (Sthatmin, Synaptophysin), while those more resistant to REST withdrawal, showed a peripheral location (BDNF, Calbidin, Complexin). To ask whether the insulator protein CTCF together with the cohesin complex might be involved in regulating Mash1 in ES cells, I performed ChIP analysis of CTCF and cohesin binding across the Mash1 locus in ES cells and used RNAi to deplete CTCF and cohesin expression. A slight increase in the transcription of Mash1 was seen in cells upon Rad21 knock down, although it was not possible to exclude this was a consequence of delayed cell cycle progression. Finally ES cell lines that carried a Mash1 transgene were created as a tool to look at whether activation of Mash1 can affect the epigenetic properties of neighbouring genes

Steepest Entropy Ascent Model for Far-Non-Equilibrium Thermodynamics. Unified Implementation of the Maximum Entropy Production Principle

By suitable reformulations, we cast the mathematical frameworks of several well-known different approaches to the description of non-equilibrium dynamics into a unified formulation, which extends to such frameworks the concept of Steepest Entropy Ascent (SEA) dynamics introduced by the present author in previous works on quantum thermodynamics. The present formulation constitutes a generalization also for the quantum thermodynamics framework. In the SEA modeling principle a key role is played by the geometrical metric with respect to which to measure the length of a trajectory in state space. In the near equilibrium limit, the metric tensor is related to the Onsager's generalized resistivity tensor. Therefore, through the identification of a suitable metric field which generalizes the Onsager generalized resistance to the arbitrarily far non-equilibrium domain, most of the existing theories of non-equilibrium thermodynamics can be cast in such a way that the state exhibits a spontaneous tendency to evolve in state space along the path of SEA compatible with the conservation constraints and the boundary conditions. The resulting unified family of SEA dynamical models is intrinsically and strongly consistent with the second law of thermodynamics. Non-negativity of the entropy production is a readily proved general feature of SEA dynamics. In several of the different approaches to non-equilibrium description we consider here, the SEA concept has not been investigated before. We believe it defines the precise meaning and the domain of general validity of the so-called Maximum Entropy Production Principle. It is hoped that the present unifying approach may prove useful in providing a fresh basis for effective, thermodynamically consistent, numerical models and theoretical treatments of irreversible conservative relaxation towards equilibrium from far non-equilibrium states.Comment: 15 pages, 4 figures, to appear in Physical Review

Quantum thermodynamic Carnot and Otto-like cycles for a two-level system

From the thermodynamic equilibrium properties of a two-level system with variable energy-level gap $\Delta$, and a careful distinction between the Gibbs relation $dE = T dS + (E/\Delta) d\Delta$ and the energy balance equation $dE = \delta Q^\leftarrow - \delta W^\to$, we infer some important aspects of the second law of thermodynamics and, contrary to a recent suggestion based on the analysis of an Otto-like thermodynamic cycle between two values of $\Delta$ of a spin-1/2 system, we show that a quantum thermodynamic Carnot cycle, with the celebrated optimal efficiency $1 - (T_{low}/T_{high})$, is possible in principle with no need of an infinite number of infinitesimal processes, provided we cycle smoothly over at least three (in general four) values of $\Delta$, and we change $\Delta$ not only along the isoentropics, but also along the isotherms, e.g., by use of the recently suggested maser-laser tandem technique. We derive general bounds to the net-work to high-temperature-heat ratio for a Carnot cycle and for the 'inscribed' Otto-like cycle, and represent these cycles on useful thermodynamic diagrams.Comment: RevTex4, 4 pages, 1 figur

Evolving concurrent Petri net models of epistasis

A genetic algorithm is used to learn a non-deterministic Petri netbased model of non-linear gene interactions, or statistical epistasis. Petri nets are computational models of concurrent processes. However, often certain global assumptions (e.g. transition priorities) are required in order to convert a non-deterministic Petri net into a simpler deterministic model for easier analysis and evaluation. We show, by converting a Petri net into a set of state trees, that it is possible to both retain Petri net non-determinism (i.e. allowing local interactions only, thereby making the model more realistic), whilst also learning useful Petri nets with practical applications. Our Petri nets produce predictions of genetic disease risk assessments derived from clinical data that match with over 92% accuracy