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Sorin Tanase-Nicola

Publications and source records attributed to Sorin Tanase-Nicola.

16 recordsLinked to original sources
arXiv2012

Exchange of stability as a function of system size in a nonequilibrium system

In equilibrium systems with short-ranged interactions, the relative stability of different thermodynamic states generally does not depend on system size (as long as this size is larger than the interaction range). Here, we use a large deviations approach to show that, in contrast, different states can exchange stability as system size is varied in a driven, bistable reaction-diffusion system. This striking effect is related to a shift from a spatially uniform to a nonuniform transition state and should generically be possible in a wide range of nonequilibrium physical and biological systems.

cond-mat.stat-mech↗
arXiv2011

Nucleation in a sheared Ising model: effects of external field

Simulations using the Forward Flux Sampling method have shown a nonmonotonic de- pendence of the homogeneous nucleation rate on the shear rate for a sheared two dimensional Ising model [R. J. Allen et al, arXiv cond-mat/0805.3029]. For quasi-equilibrium systems (i.e. in the absence of shear), Classical Nucleation Theory (CNT) predicts the dependence of the critical cluster size and the nucleation rate on the external magnetic field. We investigate the behaviour of the sheared Ising model as a function of the external field. At low exter- nal field strength, the same nonmonotonic behaviour holds and the peak in the nucleation rate is remarkably insensitive to the field strength. This suggests that the same external field-dependence holds for the enhancement of nucleation by shear at low shear rates and the suppression of shear at high shear rates. At high field strength, the nucleation behaviour is qualitatively different. We also analyse the size and shape of the largest cluster in the transition state configurations, as a function of the external field. In the sheared system, the transition state cluster becomes larger and more elongated as the field strength decreases. We compare our results for the sheared system to the predictions of the CNT for the quasi- equilibrium case, and find that the CNT cannot easily be used to describe nucleation in the system under shear.

cond-mat.soft↗
arXiv2011

Fitness in time-dependent environments includes a geometric contribution

Phenotypic evolution implies sequential fixations of new genomic sequences. The speed at which these mutations fixate depends, in part, on the relative fitness (selection coefficient) of the mutant vs. the ancestor. Using a simple population dynamics model we show that the relative fitness in dynamical environments is not equal to the fitness averaged over individual environments. Instead it includes a term that explicitly depends on the sequence of the environments. This term is geometric in nature and depends only on the oriented area enclosed by the trajectory taken by the system in the environment state space. It is related to the well-studied geometric phases in classical and quantum physical systems. We discuss possible biological implications of these observations, focusing on evolution of novel metabolic or stress-resistant functions.

q-bio.PE↗
arXiv2010

Speeding up evolutionary search by small fitness fluctuations

We consider a fixed size population that undergoes an evolutionary adaptation in the weak mutuation rate limit, which we model as a biased Langevin process in the genotype space. We show analytically and numerically that, if the fitness landscape has a small highly epistatic (rough) and time-varying component, then the population genotype exhibits a high effective diffusion in the genotype space and is able to escape local fitness minima with a large probability. We argue that our principal finding that even very small time-dependent fluctuations of fitness can substantially speed up evolution is valid for a wide class of models.

q-bio.PE↗
arXiv2009

Spatio-temporal correlations can drastically change the response of a MAPK pathway

Multisite covalent modification of proteins is omnipresent in eukaryotic cells. A well-known example is the mitogen-activated protein kinase (MAPK) cascade, where in each layer of the cascade a protein is phosphorylated at two sites. It has long been known that the response of a MAPK pathway strongly depends on whether the enzymes that modify the protein act processively or distributively: distributive mechanism, in which the enzyme molecules have to release the substrate molecules in between the modification of the two sites, can generate an ultrasensitive response and lead to hysteresis and bistability. We study by Green's Function Reaction Dynamics, a stochastic scheme that makes it possible to simulate biochemical networks at the particle level and in time and space, a dual phosphorylation cycle in which the enzymes act according to a distributive mechanism. We find that the response of this network can differ dramatically from that predicted by a mean-field analysis based on the chemical rate equations. In particular, rapid rebindings of the enzyme molecules to the substrate molecules after modification of the first site can markedly speed up the response, and lead to loss of ultrasensitivity and bistability. In essence, rapid enzyme-substrate rebindings can turn a distributive mechanism into a processive mechanism. We argue that slow ADP release by the enzymes can protect the system against these rapid rebindings, thus enabling ultrasensitivity and bistability.

q-bio.MN↗
arXiv2008

The switching dynamics of the bacterial flagellar motor

Many swimming bacteria are propelled by flagellar motors that stochastically switch between the clockwise and counterclockwise rotation direction. While the switching dynamics are one of the most important characteristics of flagellar motors, the mechanisms that control switching are poorly understood. We present a statistical-mechanical model of the flagellar rotary motor, which consists of a number of stator proteins that drive the rotation of a ring of rotor proteins, which in turn drives the rotation of a flagellar filament. At the heart of our model is the assumption that the rotor protein complex can exist in two conformational states corresponding to the two respective rotation directions, and that switching between these states depends on interactions with the stator proteins. This naturally couples the switching dynamics to the rotation dynamics, making the switch sensitive to torque and speed. Another key element of our model is that after a switching event, it takes time for the load to build up, due to polymorphic transitions of the filament. Our model predicts that this slow relaxation dynamics of the filament, in combination with the load dependence of the switching frequency, leads to a characteristic switching time, in agreement with recent observations.

q-bio.SC↗
arXiv2008

Homogeneous nucleation under shear in a two-dimensional Ising model: cluster growth, coalescence and breakup

We compute rates and pathways for nucleation in a sheared two dimensional Ising model with Metropolis spin flip dynamics, using Forward Flux Sampling (FFS). We find a peak in the nucleation rate at intermediate shear rate. We analyse the origin of this peak using modified shear algorithms and committor analysis. We find that the peak arises from an interplay between three shear-mediated effects: shear-enhanced cluster growth, cluster coalescence and cluster breakup. Our results show that complex nucleation behaviour can be found even in a simple driven model system. This work also demonstrates the use of FFS for simulating rare events, including nucleation, in nonequilibrium systems.

cond-mat.soft↗
arXiv2008

Regulatory control and the costs and benefits of biochemical noise

Experiments in recent years have vividly demonstrated that gene expression can be highly stochastic. How protein concentration fluctuations affect the growth rate of a population of cells, is, however, a wide open question. We present a mathematical model that makes it possible to quantify the effect of protein concentration fluctuations on the growth rate of a population of genetically identical cells. The model predicts that the population's growth rate depends on how the growth rate of a single cell varies with protein concentration, the variance of the protein concentration fluctuations, and the correlation time of these fluctuations. The model also predicts that when the average concentration of a protein is close to the value that maximizes the growth rate, fluctuations in its concentration always reduce the growth rate. However, when the average protein concentration deviates sufficiently from the optimal level, fluctuations can enhance the growth rate of the population, even when the growth rate of a cell depends linearly on the protein concentration. The model also shows that the ensemble or population average of a quantity, such as the average protein expression level or its variance, is in general not equal to its time average as obtained from tracing a single cell and its descendants. We apply our model to perform a cost-benefit analysis of gene regulatory control. Our analysis predicts that the optimal expression level of a gene regulatory protein is determined by the trade-off between the cost of synthesizing the regulatory protein and the benefit of minimizing the fluctuations in the expression of its target gene. We discuss possible experiments that could test our predictions.

q-bio.MN↗
arXiv2006

Kramers equation and supersymmetry

Hamilton's equations with noise and friction possess a hidden supersymmetry, valid for time-independent as well as periodically time-dependent systems. It is used to derive topological properties of critical points and periodic trajectories in an elementary way. From a more practical point of view, the formalism provides new tools to study the reaction paths in systems with separated time scales. A 'reduced current' which contains the relevant part of the phase space probability current is introduced, together with strategies for its computation.

cond-mat.stat-mech↗
arXiv2006

Diffusion of transcription factors can drastically enhance the noise in gene expression

We study by simulation the effect of the diffusive motion of repressor molecules on the noise in mRNA and protein levels in the case of a repressed gene. We find that spatial fluctuations due to diffusion can drastically enhance the noise in gene expression. For a fixed repressor strength, the noise due to diffusion can be minimized by increasing the number of repressors or by decreasing the rate of the open complex formation. We also show that the effect of spatial fluctuations can be well described by a two-step kinetic scheme, where formation of an encounter complex by diffusion and the subsequent association reaction are treated separately. Our results also emphasize that power spectra are a highly useful tool for studying the propagation of noise through the different stages of gene expression.

q-bio.MN↗
arXiv2006

Signal detection, modularity and the correlation between extrinsic and intrinsic noise in biochemical networks

Understanding cell function requires an accurate description of how noise is transmitted through biochemical networks. We present an analytical result for the power spectrum of the output signal of a biochemical network that takes into account the correlations between the noise in the input signal (the extrinsic noise) and the noise in the reactions that constitute the network (the intrinsic noise). These correlations arise from the fact that the reactions by which biochemical signals are detected necessarily affect the signaling molecules and the detection components of the network simultaneously. We show that anti-correlation between the extrinsic and intrinsic noise enhances the robustness of zero-order ultrasensitive networks to biochemical noise. We discuss the consequences of the correlation between extrinsic and intrinsic noise for a modular description of noise transmission through large biochemical networks in the context of the mitogen-activated protein kinase cascade.

q-bio.MN↗
arXiv2005

Exact results for noise power spectra in linear biochemical reaction networks

We present a simple method for determining the exact noise power spectra in linear chemical reaction networks. We apply the method to networks which are representative of biochemical processes such as gene expression and signal detection. Our results clarify how noise is transmitted by signal detection motifs, and indicate how to coarse-grain networks by the elimination of fast reactions.

q-bio.SC↗
arXiv2003

Metastable states, transitions, basins and borders at finite temperatures

Langevin/Fokker-Planck processes can be immersed in a larger frame by adding fictitious fermion variables. The (super)symmetry of this larger structure has been used to derive Morse theory in an elegant way. The original physical diffusive motion is retained in the zero-fermion subspace. Here we study the subspaces with non-zero fermion number which yield deep information, as well as new computational strategies, for barriers, reaction paths, and unstable states -- even in non-zero temperature situations and when the barriers are of entropic or collective nature, as in the thermodynamic limit. The presentation is self-contained.

cond-mat.stat-mech↗
arXiv2003

Topological methods for searching barriers and reaction paths

We present a family of algorithms for the fast determination of reaction paths and barriers in phase space and the computation of the corresponding rates. The method requires the reaction times be large compared to the microscopic time, irrespective of the origin - energetic, entropic, cooperative - of the timescale separation. It lends itself to temperature cycling as in simulated annealing and to activation-relaxation routines. The dynamics is ultimately based on supersymmetry methods used years ago to derive Morse theory. Thus, the formalism automatically incorporates all relevant topological information.

cond-mat.stat-mech↗
arXiv2002

Statistical-mechanical formulation of Lyapunov exponents

We show how the Lyapunov exponents of a dynamic system can in general be expressed in terms of the free energy of a (non-Hermitian) quantum many-body problem. This puts their study as a problem of statistical mechanics, whose intuitive concepts and techniques of approximation can hence be borrowed.

cond-mat.stat-mech↗