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David B. Saakian

Publications and source records attributed to David B. Saakian.

At least 19 recordsLinked to original sources

Localization, fractality, and ergodicity in a monitored qubit

We study the statistical properties of a single two-level system (qubit) subject to repetitive ancilla-based measurements. This setup is a fundamental minimal model for exploring the intricate interplay between the unitary dynamics of the system and the nonunitary stochasticity introduced by quantum measurements, which is central to the phenomenon of measurement-induced phase transitions. We demonstrate that this "toy model" harbors remarkably rich dynamics, manifesting in the distribution function of the qubit's quantum states in the long-time limit. We uncover a compelling analogy with the phenomenon of Anderson localization, albeit governed by distinct underlying mechanisms. Specifically, the state distribution function of the monitored qubit, parameterized by a single angle on the Bloch sphere, exhibits diverse types of behavior familiar from the theory of Anderson transitions, spanning from complete localization to almost uniform delocalization, with fractality occurring between the two limits. By combining analytical solutions for various special cases with two complementary numerical approaches, we achieve a comprehensive understanding of the structure delineating the "phase diagram" of the model. We categorize and quantify the emergent regimes and identify two distinct phases of the monitored qubit: ergodic and nonergodic. Furthermore, we identify a genuinely localized phase within the nonergodic phase, where the state distribution functions consist of delta peaks, as opposed to the delocalized phase characterized by extended distributions. Identification of these phases and demonstration of transitions between them in a monitored qubit are our main findings.

quant-ph

The non-perturbative phenomenon for the Crow Kimura model with stochastic resetting

We consider the Crow Kimura model, modified via stochastic resetting. There are two principally different situations: First, when due to resetting the system jumps to the low fitness state, everything is rather simple in this case, we have a solution which is a slight modification of the standard Crow-Kimura model case. When there is resetting to the high-fitness state, there is a non-perturbative phenomenon via the resetting probability, even a minimal resetting probability drastically changes the solution. We found two subphases in this phase.

q-bio.PE

A simple statistical physics model for the epidemic with incubation period

Based on the classical SIR model, we derive a simple modification for the dynamics of epidemics with a known incubation period of infection. The model is described by a system of integro-differential equations. Parameters of our model directly related to epidemiological data. We derive some analytical results, as well as perform numerical simulations. We use the proposed model to analyze COVID-19 epidemic data in Armenia. We propose a strategy: organize a quarantine, and then conduct extensive testing of risk groups during the quarantine, evaluating the percentage of the population among risk groups and people with symptoms.

q-bio.PE

Nonlinear Stochastic Dynamics of Complex Systems, III: Noneqilibrium Thermodynamics of Self-Replication Kinetics

We briefly review the recently developed, Markov process based isothermal chemical thermodynamics for nonlinear, driven mesoscopic kinetic systems. Both the instantaneous Shannon entropy {\boldmath $S[p_α(t)]$} and relative entropy {\boldmath $F[p_α(t)]$}, defined based on probability distribution {\boldmath $\{p_α(t)\}$}, play prominent roles. The theory is general; and as a special case when a chemical reaction system is situated in an equilibrium environment, it agrees perfectly with Gibbsian chemical thermodynamics: {\boldmath $k_BS$} and {\boldmath $k_BTF$} become thermodynamic entropy and free energy, respectively. We apply this theory to a fully reversible autocatalytic reaction kinetics, represented by a Delbrück-Gillespie process, in a chemostatic nonequilibrium environment. The open, driven chemical system serves as an archetype for biochemical self-replication. The significance of {\em thermodynamically consistent} kinetic coarse-graining is emphasized. In a kinetic system where death of a biological organism is treated as the reversal of its birth, the meaning of mathematically emergent "dissipation", which is not related to the heat measured in terms of {\boldmath $k_BT$}, remains to be further investigated.

cond-mat.stat-mech

The cell cycle related rhythms, cells' states correlation and the cancer

We consider the decision making by mammalian cells, looking them as dynamic systems with rhythms. We calculate the effective dimension of the cell division model of the healthy mammalian cells consistent with the data: it is described via a four dimensional dynamic system. We assume that the cell's decision making property is strongly affected by the cells rhythms, their causal relations, and by the correlation between internal states of different cells in tissue. There is a strong correlation between the states of different healthy cells (verified partially experimentally), and we assume that there is no such a correlation between internal states of the healthy and cancer cells. The origins of the cancer are just the disruption of this correlation (self-identification of the cells) and the change of the causal relations between the Circadian and cell cycle rhythms. Assuming the Gaussian channel version of the cell-cell communications and a key role of public goods for the cancer cells, we get a strong correlation between the states of different cancer cells.

q-bio.CB

The calculation of multi-fractal properties of directed random walks on hierarchic trees with continuous branching

We consider the hierarchic tree Random Energy Model with continuous branching and calculate the moments of the corresponding partition function. We establish the multifractal properties of those moments. We derive formulas for the normal distribution of random variables, as well as for the general case. We compare our results for the moments of partition function with corresponding results of logarithmic 1-d REM and conjecture a specific power-law tail for the partition function distribution in the high-temperature phase. Our results establish a connection between reaction-diffusion equations and multi-scaling.

cond-mat.stat-mech

Exact results in the large system size limit for the dynamics of the Chemical Master Equation, a one dimensional chain of equations

We apply the Hamilton-Jacobi equation (HJE) formalism to solve the dynamics of the Chemical Master Equation (CME). We found exact analytical expressions (in large system-size limit) for the probability distribution, including explicit expression for the dynamics of variance of distribution. We also give the solution for some simple cases of the model with time-dependent rates. We derived the results of Van Kampen method from HJE approach using a special ansatz. Using the Van Kampen method, we give a system of ODE to define the variance in 2-d case. We performed numerics for the CME with stationary noise. We give analytical criteria for the disappearance of bi-stability in case of stationary noise in 1-d CME.

cond-mat.stat-mech

Finite genome length corrections for the mean fitness and gene probabilities in evolution models

Using the Hamilton-Jacobi equation approach to study genomes of length $L$, we obtain 1/L corrections for the steady state population distributions and mean fitness functions for horizontal gene transfer model, as well as for the diploid evolution model with general fitness landscapes. Our numerical solutions confirm the obtained analytic equations. Our method could be applied to the general case of nonlinear Markov models.

q-bio.PE

Biological Evolution in a Multidimensional Fitness Landscape

We considered a {multi-block} molecular model of biological evolution, in which fitness is a function of the mean types of alleles located at different parts (blocks) of the genome. We formulated an infinite population model with selection and mutation, and calculated the mean fitness. For the case of recombination, we formulated a model with a multidimensional fitness landscape (the dimension of the space is equal to the number of blocks) and derived a theorem about the dynamics of initially narrow distribution. We also considered the case of lethal mutations. We also formulated the finite population version of the model in the case of lethal mutations. Our models, derived for the virus evolution, are interesting also for the statistical mechanics and the Hamilton-Jacobi equation as well.

q-bio.PE

Finite population size effects in quasispecies models with single-peak fitness landscape

We consider finite population size effects for Crow-Kimura and Eigen quasispecies models with single peak fitness landscape. We formulate accurately the iteration procedure for the finite population models, then derive Hamilton-Jacobi equation (HJE) to describe the dynamic of the probability distribution. The steady state solution of HJE gives the variance of the mean fitness. Our results are useful for understanding population sizes of virus in which the infinite population models can give reliable results for the biological evolution problems.

q-bio.PE

Directed random walks on hierarchic trees with continuous branching: a renormalization group approach

We investigate the directed random walk on hierarchic trees. Two cases are investigated: random variables on deterministic trees with a continuous branching, and random variables on the trees constructed trough the random branching process. We derive renormalization group (partial differential) equations for the branching models with binomial, Poisson and compound Poisson distributions of random variables on the links of tree. These renormaliation group equations are new class of reaction-diffusion equations in 1-dimension.

cond-mat.stat-mech

The Markov Switching Multi-fractal models as a new class of REM-like models in 1-dimensional space

We map the Markov Switching Multi-fractal model (MSM) onto the Random Energy Model (REM). The MSM is, like the REM, an exactly solvable model in 1-d space with non-trivial correlation functions. According to our results, four different statistical physics phases are possible in random walks with multi-fractal behavior. We also introduce the continuous branching version of the model, calculate the moments and prove multiscaling behavior. Different phases have different multi-scaling properties.

cond-mat.stat-mech

Dynamics of the Chemical Master Equation, a strip of chains of equations in d-dimensional space

We investigate the multi-chain version of the Chemical Master Equation, when there are transitions between different states inside the long chains, as well as transitions between (a few) different chains. In the discrete version, such a model can describe the connected diffusion processes with jumps between different types. We apply the Hamilton-Jacobi equation to solve some aspects of the model. We derive exact {(in the limit of infinite number of particles)} results for the dynamic of the maximum of the distribution and the variance of distribution.

cond-mat.stat-mech

The three different phases in the dynamics of chemical reaction networks and their relationship to cancer

We investigate the catalytic reactions model used in cell modeling. The reaction kinetic is defined through the energies of different species of molecules following random independent distribution. The related statistical physics model has three phases and these three phases emerged in the dynamics: fast dynamics phase, slow dynamic phase and ultra-slow dynamic phase. The phenomenon we found is a rather general, does not depend on the details of the model. We assume as a hypothesis that the transition between these phases (glassiness degrees) is related to cancer. The imbalance in the rate of processes between key aspects of the cell (gene regulation, protein-protein interaction, metabolical networks) creates a change in the fine tuning between these key aspects, affects the logics of the cell and initiates cancer. It is probable that cancer is a change of phase resulting from increased and deregulated metabolic reactions.

q-bio.OT

Evolutionary dynamics on rugged fitness landscapes: exact dynamics and information theoretical aspects

The parallel mutation-selection evolutionary dynamics, in which mutation and replication are independent events, is solved exactly in the case that the Malthusian fitnesses associated to the genomes are described by the Random Energy Model (REM) and by a ferromagnetic version of the REM. The solution method uses the mapping of the evolutionary dynamics into a quantum Ising chain in a transverse field and the Suzuki-Trotter formalism to calculate the transition probabilities between configurations at different times. We find that in the case of the REM landscape the dynamics can exhibit three distinct regimes: pure diffusion or stasis for short times, depending on the fitness of the initial configuration, and a spin-glass regime for large times. The dynamic transition between these dynamical regimes is marked by discontinuities in the mean-fitness as well as in the overlap with the initial reference sequence. The relaxation to equilibrium is described by an inverse time decay. In the ferromagnetic REM, we find in addition to these three regimes, a ferromagnetic regime where the overlap and the mean-fitness are frozen. In this case, the system relaxes to equilibrium in a finite time. The relevance of our results to information processing aspects of evolution is discussed.

physics.bio-ph

The optimization and shock waves in evolution dynamics

We consider the optimal dynamics in the infinite population evolution models with general symmetric fitness landscape. The search of optimal evolution trajectories are complicated due to sharp transitions (like shock waves) in evolution dynamics with smooth fitness landscapes, which exist even in case of popular quadratic fitness. We found exact analytical solutions for discontinuous dynamics at the large genome length limit. We found the optimal mutation rates for the fixed fitness landscape. The single peak fitness landscape gives the fastest dynamics to send the vast majority of the population from the initial sequence to the neighborhood of the final sequence.

q-bio.PE

The intermediate evolution phase in case of truncated selection

Using methods of statistical physics, we present rigorous theoretical calculations of Eigen's quasispecies theory with the truncated fitness landscape which dramatically limits the available sequence space of a reproducing quasispecies. Depending on the mutation rates, we observe three phases, a selective one, an intermediate one with some residual order and a completely randomized phase. Our results are applicable for the general case of fitness landscape.

q-bio.PE

Exactly solvable dynamics of the Eigen and the Crow-Kimura models

We introduce a new way to study molecular evolution within well-established Hamilton-Jacobi formalism, showing that for a broad class of fitness landscapes it is possible to derive dynamics analytically within the $1/N$-accuracy, where $N$ is genome length. For smooth and monotonic fitness function this approach gives two dynamical phases: smooth dynamics, and discontinuous dynamics. The latter phase arises naturally with no explicit singular fitness function, counter-intuitively. The Hamilton-Jacobi method yields straightforward analytical results for the models that utilize fitness as a function of Hamming distance from a reference genome sequence. We also show the way in which this method gives dynamical phase structure for multi-peak fitness.

q-bio.PE