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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Transitions in genetic toggle switches driven by dynamic disorder in rate coefficients

In biochemical systems, intrinsic noise may drive the system switch from one stable state to another. We investigate how kinetic switching between stable states in a bistable network is influenced by dynamic disorder, i.e., fluctuations in the rate coefficients. Using the geometric minimum action method, we first investigate the optimal transition paths and the corresponding minimum actions based on a genetic toggle switch model in which reaction coefficients draw from a discrete probability distribution. For the continuous probability distribution of the rate coefficient, we then consider two models of dynamic disorder in which reaction coefficients undergo different stochastic processes with the same stationary distribution. In one, the kinetic parameters follow a discrete Markov process and in the other they follow continuous Langevin dynamics. We find that regulation of the parameters modulating the dynamic disorder, as has been demonstrated to occur through allosteric control in bistable networks in the immune system, can be crucial in shaping the statistics of optimal transition paths, transition probabilities, and the stationary probability distribution of the network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Hybrid framework for the simulation of stochastic chemical kinetics

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Quantum Markov States and Quantum Hidden Markov States

In a previous paper (Funct. Anal. Appl., 3 (2015), 205–209), we defined quantum Markov states. Here we recall this definition and present a proof of the results from that paper (which are given there without proofs). We give a definition of a quantum hidden Markov state generated by a function of a quantum Markov process and show how it is related to other definitions of such states. Our definitions work for quantum Markov fields on ℤ{sup N} and on graphs. We consider an example with the Cayley tree.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A master equation and moment approach for biochemical systems with creation-time-dependent bimolecular rate functions

Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-to-cell variability even in clonal populations. Stochastic biochemical networks have been traditionally modeled as continuous-time discrete-state Markov processes whose probability density functions evolve according to a chemical master equation (CME). In diffusion reaction systems on membranes, the Markov formalism, which assumes constant reaction propensities is not directly appropriate. This is because the instantaneous propensity for a diffusion reaction to occur depends on the creation times of the molecules involved. In this work, we develop a chemical master equation for systems of this type. While this new CME is computationally intractable, we make rational dimensional reductions to form an approximate equation, whose moments are also derived and are shown to yield efficient, accurate results. This new framework forms a more general approach than the Markov CME and expands upon the realm of possible stochastic biochemical systems that can be efficiently modeled.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

On the advantages of exploiting memory in Markov state models for biomolecular dynamics

Biomolecular dynamics play an important role in numerous biological processes. Markov State Models (MSMs) provide a powerful approach to study these dynamic processes by predicting long time scale dynamics based on many short molecular dynamics (MD) simulations. In an MSM, protein dynamics are modeled as a kinetic process consisting of a series of Markovian transitions between different conformational states at discrete time intervals (called “lag time”). To achieve this, a master equation must be constructed with a sufficiently long lag time to allow interstate transitions to become truly Markovian. This imposes a major challenge for MSM studies of proteins since the lag time is bound by the length of relatively short MD simulations available to estimate the frequency of transitions. Here, we show how one can employ the generalized master equation formalism to obtain an exact description of protein conformational dynamics both at short and long time scales without the time resolution restrictions imposed by the MSM lag time. Using a simple kinetic model, alanine dipeptide, and WW domain, we demonstrate that it is possible to construct these quasi-Markov State Models (qMSMs) using MD simulations that are 5–10 times shorter than those required by MSMs. These qMSMs only contain a handful of metastable states and, thus, can greatly facilitate the interpretation of mechanisms associated with protein dynamics. A qMSM opens the door to the study of conformational changes of complex biomolecules where a Markovian model with a few states is often difficult to construct due to the limited length of available MD simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Brownian motion on [0,∞) with linear drift, reflected at zero: exact asymptotics for ergodic means

For the Brownian motion X{sub μ}(t) on the half-axis [0,∞) with linear drift μ, reflected at zero and for fixed numbers p>0, δ>0, d>0, a≥0, we calculate the exact asymptotics as T→∞ of the mathematical expectations and probabilities as well as of their conditional versions. For p=1 we give explicit formulae for the emerging constants via the Airy function. We consider an application of the results obtained to the problem of studying the behaviour of a Brownian particle in a gravitational field in a container bounded below by an impenetrable wall when μ=−mg/(2kT{sub K}), where m is the mass of the Brownian particle, g is the gravitational acceleration, k is the Boltzmann constant, T{sub K} is the temperature in the Kelvin scale. The analysis is conducted by the Laplace method for the sojourn time of homogeneous Markov processes. Bibliography: 31 titles. (paper)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Systems of Semilinear Parabolic Variational Inequalities with Time-Dependent Convex Obstacles

We consider a system of seminlinear parabolic variational inequalities with time-dependent convex obstacles. We prove the existence and uniqueness of its solution. We also provide a stochastic representation of the solution and show that it can be approximated by the penalization method. Our proofs are based upon probabilistic methods from the theory of Markov processes and the theory of backward stochastic differential equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

A Bivariate Mixed Distribution with a Heavy-tailed Component and its Application to Single-site Daily Rainfall Simulation

This paper presents an improved brivariate mixed distribution, which is capable of modeling the dependence of daily rainfall from two distinct sources (e.g., rainfall from two stations, two consecutive days, or two instruments such as satellite and rain gauge). The distribution couples an existing framework for building a bivariate mixed distribution, the theory of copulae and a hybrid marginal distribution. Contributions of the improved distribution are twofold. One is the appropriate selection of the bivariate dependence structure from a wider admissible choice (10 candidate copula families). The other is the introduction of a marginal distribution capable of better representing low to moderate values as well as extremes of daily rainfall. Among several applications of the improved distribution, particularly presented here is its utility for single-site daily rainfall simulation. Rather than simulating rainfall occurrences and amounts separately, the developed generator unifies the two processes by generalizing daily rainfall as a Markov process with autocorrelation described by the improved bivariate mixed distribution. The generator is first tested on a sample station in Texas. Results reveal that the simulated and observed sequences are in good agreement with respect to essential characteristics. Then, extensive simulation experiments are carried out to compare the developed generator with three other alternative models: the conventional two-state Markov chain generator, the transition probability matrix model and the semi-parametric Markov chain model with kernel density estimation for rainfall amounts. Analyses establish that overall the developed generator is capable of reproducing characteristics of historical extreme rainfall events and is apt at extrapolating rare values beyond the upper range of available observed data. Moreover, it automatically captures the persistence of rainfall amounts on consecutive wet days in a relatively natural and easy way. Another interesting observation is that the recognized ‘overdispersion’ problem in daily rainfall simulation ascribes more to the loss of rainfall extremes than the under-representation of first-order persistence. The developed generator appears to be a sound option for daily rainfall simulation, especially in particular hydrologic planning situations when rare rainfall events are of great importance.

Singh, Vijay P.↗
NASA NTRS1979

A comparison of theoretical and experimental video compression designs

This paper compares theoretical and experimental picture compression designs, for images processed in 8 x 8 blocks using the Walsh-Hadamard transform (WHT). The optimum picture compression design is well known, if the mean-square error (mse) is used as the measure of distortion, and if it is assumed that the video process is a stationary first-order Markov process with a Gaussian distribution. This theoretical design gives useful results when the transform processing is done on full pictures, but gives inferior results (relative to empirical design) when transform processing is done on small 8 x 8 blocks. The use of non-Gaussian distributions for the transform components fails to improve this poor performance, which is due to the nonstationary nature of the video process. An experimentally based design procedure, which considers nonstationarity, yields significantly improved mse and subjective performance.

Jones, H. W., Jr.↗
DOE OSTI2013

PULSAR STATE SWITCHING FROM MARKOV TRANSITIONS AND STOCHASTIC RESONANCE

Markov processes are shown to be consistent with metastable states seen in pulsar phenomena, including intensity nulling, pulse-shape mode changes, subpulse drift rates, spin-down rates, and X-ray emission, based on the typically broad and monotonic distributions of state lifetimes. Markovianity implies a nonlinear magnetospheric system in which state changes occur stochastically, corresponding to transitions between local minima in an effective potential. State durations (though not transition times) are thus largely decoupled from the characteristic timescales of various magnetospheric processes. Dyadic states are common but some objects show at least four states with some transitions forbidden. Another case is the long-term intermittent pulsar B1931+24 that has binary radio-emission and torque states with wide, but non-monotonic duration distributions. It also shows a quasi-period of 38 ± 5 days in a 13 yr time sequence, suggesting stochastic resonance in a Markov system with a forcing function that could be strictly periodic or quasi-periodic. Nonlinear phenomena are associated with time-dependent activity in the acceleration region near each magnetic polar cap. The polar-cap diode is altered by feedback from the outer magnetosphere and by return currents from the equatorial region outside the light cylinder that may also cause the neutron star to episodically charge and discharge. Orbital perturbations of a disk or current sheet provide a natural periodicity for the forcing function in the stochastic-resonance interpretation of B1931+24. Disk dynamics may introduce additional timescales in observed phenomena. Future work can test the Markov interpretation, identify which pulsar types have a propensity for state changes, and clarify the role of selection effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Adaptive Sampling of Large Deviations

We introduce and test an algorithm that adaptively estimates large deviation functions characterizing the fluctuations of additive functionals of Markov processes in the long-time limit. These functions play an important role for predicting the probability and pathways of rare events in stochastic processes, as well as for understanding the physics of nonequilibrium systems driven in steady states by external forces and reservoirs. The algorithm uses methods from risk-sensitive and feedback control to estimate from a single trajectory a new process, called the driven process, known to be efficient for importance sampling. Its advantages compared to other simulation techniques, such as splitting or cloning, are discussed and illustrated with simple equilibrium and nonequilibrium diffusion models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Trace distance in stochastic dephasing with initial correlation

The time evolution of the trace distance between quantum states of a qubit which is placed under the influence of stochastic dephasing is investigated within the framework of the stochastic Liouville equation. When stochastic dephasing is subject to the homogeneous Gauss-Markov process, the trace distance is exactly calculated in the presence of the initial correlation between the qubit and the stochastic process, where the stochastic process is inevitably a nonstationary process. It is found that even the initial correlation with the classical environment can make the trace distance greater than the initial value if stochastic dephasing causes the slow modulation of the qubit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

A model capturing novel strand symmetries in bacterial DNA

Highlights: {yields} We propose a simple stochastic model to construct primitive DNA sequences. {yields} The model provide an explanation for Chargaff's second parity rule in primitive DNA sequences. {yields} The model is also used to predict a novel type of strand symmetry in primitive DNA sequences. {yields} We extend the results for bacterial DNA sequences and compare distributional properties intrinsic to the model to statistical estimates from 1049 bacterial genomes. {yields} We find out statistical evidences that the novel type of strand symmetry holds for bacterial DNA sequences. -- Abstract: Chargaff's second parity rule for short oligonucleotides states that the frequency of any short nucleotide sequence on a strand is approximately equal to the frequency of its reverse complement on the same strand. Recent studies have shown that, with the exception of organellar DNA, this parity rule generally holds for double-stranded DNA genomes and fails to hold for single-stranded genomes. While Chargaff's first parity rule is fully explained by the Watson-Crick pairing in the DNA double helix, a definitive explanation for the second parity rule has not yet been determined. In this work, we propose a model based on a hidden Markov process for approximating the distributional structure of primitive DNA sequences. Then, we use the model to provide another possible theoretical explanation for Chargaff's second parity rule, and to predict novel distributional aspects of bacterial DNA sequences.

60 APPLIED LIFE SCIENCES↗