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The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling waves dynamics. Here, we infer data-driven models from high-resolution in-vivo recordings of mouse brain obtained from wide-field calcium imaging. We then assimilate experimental and simulated data through the characterization of the spatio-temporal features of cortical waves in experimental recordings. Inference is built in two steps: an inner loop that optimizes a mean-field model by likelihood maximization, and an outer loop that optimizes a periodic neuro-modulation via direct comparison of observables that characterize cortical slow waves. The model reproduces most of the features of the non-stationary and non-linear dynamics present in the high-resolution in-vivo recordings of the mouse brain. The proposed approach offers new methods of characterizing and understanding cortical waves for experimental and computational neuroscientists.

16 pages, 6 figures., Since the last century, deterministic compartmental models have emerged as powerful tools to predict and control epidemic outbreaks, in many cases helping to mitigate their impacts. A key quantity for these models is the so-called basic reproduction number, R_{0}, that measures the number of secondary infections produced by an initial infected individual in a fully susceptible population. Some methods have been developed to allow the direct computation of this quantity provided that some conditions are fulfilled, such that the model has a prepandemic disease-free equilibrium state. This condition is fulfilled only when the populations are stationary. In the case of vector-borne diseases, this implies that the vector birth and death rates need to be balanced. This is not fulfilled in many realistic cases in which the vector population grows or decreases. Here we develop a vector-borne epidemic model with growing and decaying vector populations that in the long term converge to an asymptotic stationary state, and study the conditions under which the standard methods to compute R_{0} work and discuss an alternative when they fail. We also show that growing vector populations produce a delay in the epidemic dynamics when compared to the case of the stationary vector population. Finally, we discuss the conditions under which the model can be reduced to the Susceptible, Infectious, and/or Recovered (SIR) model with fewer compartments and parameters, which helps in solving the problem of parameter unidentifiability of many vector-borne epidemic models., A.G.R. and M.A.M. acknowledge financial support from Grants No. RTI2018-095441-B-C22 (SuMaEco) and No. PID2021-123723OB-C22 (CYCLE) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” and Grant No. MDM-2017-0711 (María de Maeztu Program for Units of Excellence in R&D) funded by MCIN/AEI/10.13039/501100011033.

11 pages, 6 figures, 1 table., Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenized and parasite mobility is not explicitly accounted for. However, in realistic scenarios epidemic transmission is conditioned by the spatial distribution of hosts and the parasites' mobility patterns, calling for an explicit description of space. In this work, we develop a spatially explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission through extensive numerical simulations and theoretical analysis. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal progression of the epidemic are assessed. We show that larger values of pathogen mobility imply more severe epidemics, as the number of infections increases, and shorter timescales to extinction. An analytical expression for the basic reproduction number of the spatial model, R~0 , is derived as a function of the non-spatial counterpart, R 0, which characterizes a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system., Grant no. RTI2018-095441-B-C22 (SuMaEco) funded by MCIN/AEI/10.13039/501100011033 and by ‘ERDF A way of making Europe’ (A.G.-R. and M.A.M.). Grant no. MDM-2017-0711 (María de Maeztu Excellence Unit) funded by MCIN/AEI/10.13039/501100011033 (A.G.-R., C.L. and M.A.M.). F.V. through the Professors Convidats Programme funded by UIB.

Context. Homochirality is a generic and unique property of life on Earth and is considered a universal and agnostic biosignature. Homochirality induces fractional circular polarization in the incident light that it reflects. Because this circularly polarized light can be sensed remotely, it can be one of the most compelling candidate biosignatures in life detection missions. While there are also other sources of circular polarization, these result in spectrally flat signals with lower magnitude. Additionally, circular polarization can be a valuable tool in Earth remote sensing because the circular polarization signal directly relates to vegetation physiology. Aims. While high-quality circular polarization measurements can be obtained in the laboratory and under semi-static conditions in the field, there has been a significant gap to more realistic remote sensing conditions. Methods. In this study, we present sensitive circular spectropolarimetric measurements of various landscape elements taken from a fast-moving helicopter. Results. We demonstrate that during flight, within mere seconds of measurements, we can differentiate (S/N>5) between grass fields, forests, and abiotic urban areas. Importantly, we show that with only nonzero circular polarization as a discriminant, photosynthetic organisms can even be measured in lakes. Conclusions. Circular spectropolarimetry can be a powerful technique to detect life beyond Earth, and we emphasize the potential of utilizing circular spectropolarimetry as a remote sensing tool to characterize and monitor in detail the vegetation physiology and terrain features of Earth itself., 7 pages, 6 figures

By means of an experimental dataset, we use deep learning to implement an RGB extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males typically associate a given emotion with darker colors while females with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors., Comment: To appear in Physical Review R. (8 pages, 13 figures)

While many epidemiological models were proposed to understand and handle COVID-19 pandemic, too little has been invested to understand human viral replication and the potential use of novel antivirals to tackle the infection. In this work, using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans are characterized. A complete analysis of the main dynamic characteristic is developed based on the reproduction number. The equilibrium regions of the system are fully characterized, and the stability of such regions is formally established. Mathematical analysis highlights critical conditions to decrease monotonically SARS-CoV-2 in the host, as such conditions are relevant to tailor future antiviral treatments. Simulation results show the aforementioned system characterization. Fil: Abuin, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Anderson, Alejandro Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Ferramosca, Antonio. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Hernandez Vargas, Esteban Abelardo. Universidad Nacional Autónoma de México; México Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina

We study the critical behavior of a model with nondissipative couplings aimed at describing the collective behavior of natural swarms, using the dynamical renormalization group under a fixed-network approximation. At one loop, we find a crossover between an unstable fixed point, characterized by a dynamical critical exponent z ¼ d=2, and a stable fixed point with z ¼ 2, a result we confirm through numerical simulations. The crossover is regulated by a length scale given by the ratio between the transport coefficient and the effective friction, so that in finite-size biological systems with low dissipation, dynamics is ruled by the unstable fixed point. In three dimensions this mechanism gives z ¼ 3=2, a value significantly closer to the experimental window, 1.0 ≤ z ≤ 1.3, than the value z ≈ 2 numerically found in fully dissipative models, either at or off equilibrium. This result indicates that nondissipative dynamical couplings are necessary to develop a theory of natural swarms fully consistent with experiments., Instituto de Física de Líquidos y Sistemas Biológicos

Many pressing medical challenges -- such as diagnosing disease, enhancing directed stem cell differentiation, and classifying cancers -- have long been hindered by limitations in our ability to quantify proteins in single cells. Mass-spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.

We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed., Comment: Additional information on the webpage http://www.quantware.ups-tlse.fr/QWLIB/wormgooglematrix/index.html

The interplay between parasites and their hosts is found in all kinds of species and plays an important role in understanding the principles of evolution and coevolution. Usually, the different genotypes of hosts and parasites oscillate in their abundances. The well-established theory of oscillatory Red Queen dynamics proposes an ongoing change in frequencies of the different types within each species. So far, it is unclear under what conditions Red Queen dynamics persists, especially when the number of types per species increases. Some models show that with many types of hosts and parasites or more species chaotic dynamics occur. In our analysis, an arbitrary number of types within two species are examined in a deterministic framework with constant or changing population size and very simple interactions. This general framework allows for analytical solutions for internal fixed points and their stability. The numerical analysis shows that for two species, once more than two types are considered per species, irregular dynamics in their frequencies can be observed in the long run. The nature of the dynamics depends strongly on the initial configuration of the system; the usual regular Red Queen oscillations are only observed when all types initially have similar abundance.

A gauge model of neural network is introduced, which resembles the Z(2) Higgs lattice gauge theory of high-energy physics. It contains a neuron variable $S_x = \pm 1$ on each site $x$ of a 3D lattice and a synaptic-connection variable $J_{x\mu} = \pm 1$ on each link $(x,x+\hat{\mu}) (\mu=1,2,3)$. The model is regarded as a generalization of the Hopfield model of associative memory to a model of learning by converting the synaptic weight between $x$ and $x+\hat{\mu}$ to a dynamical Z(2) gauge variable $J_{x\mu}$. The local Z(2) gauge symmetry is inherited from the Hopfield model and assures us the locality of time evolutions of $S_x$ and $J_{x\mu}$ and a generalized Hebbian learning rule. At finite "temperatures", numerical simulations show that the model exhibits the Higgs, confinement, and Coulomb phases. We simulate dynamical processes of learning a pattern of $S_x$ and recalling it, and classify the parameter space according to the performance. At some parameter regions, stable column-layer structures in signal propagations are spontaneously generated. Mutual interactions between $S_x$ and $J_{x\mu}$ induce partial memory loss as expected., Comment: 17 pages, 18 figures. Final Version

We study the three-dimensional static configurations of nonhomogeneous Kirchhoff filaments with periodically varying Young's modulus. This type of variation may occur in long tandemly repeated sequences of DNA. We analyse the effects of the Young's modulus frequence and amplitude of oscillation in the stroboscopic maps, and in the regular (non chaotic) spatial configurations of the filaments. Our analysis shows that the tridimensional conformations of long filaments may depend critically on the Young's modulus frequence in case of resonance with other natural frequencies of the filament. As expected, far from resonance the shape of the solutions remain very close to that of the homogeneous case. In the case of biomolecules, it is well known that various other elements, besides sequence-dependent effects, combine to determine their conformation, like self-contact, salt concentration, thermal fluctuations, anisotropy and interaction with proteins. Our results show that sequence-dependent effects alone may have a significant influence on the shape of these molecules, including DNA. This could, therefore, be a possible mechanical function of the ``junk'' sequences., 18 pages (twocolumn), 5 figures Revised manuscript

We consider a model for strand separation in negatively supercoiled dna, and study the effect of supercoiling on the localization of denaturation, using notions from statistical mechanics. The model is based on on mean field Ising system, coupled with a random field one dimensional ISing model. The effect of the word in AT and Gc associated with the DNA on localization of denaturation is studied under the hypothesis of small perimeter., 20 pages

Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of pre- and post-synaptic spikes and this is called spike-timing-dependent plasticity (STDP). Many authors have claimed that a precise balance between long-term potentiation (LTP) and long-term depression (LTD) of STDP is crucial in the storage of spatio-temporal patterns. Some authors have numerically investigated the impact of an imbalance between LTP and LTD on the network properties. However, the mathematical mechanism remains unknown. We analytically show that an associative memory network has the robust retrieval properties of spatio-temporal patterns, and these properties make the network less vulnerable to any deviation from a precise balance between LTP and LTD when the network contains a finite number of neurons., 24 pages, 5 figures

Several controversial issues concerning the packing of linear DNA in bacteriophages and globules are discussed. Exact relations for the osmotic pressure, capsid pressure and loading force are derived in terms of the hole size inside phages under the assumption that the DNA globule has a uniform density. A new electrostatic model is introduced for computing the osmotic pressure of rodlike polyelectrolytes at very high concentrations. At intermediate packing, a reptation model is considered for DNA diffusing within a toroidal globule. Under tight packing conditions, a model of Coulomb sliding friction is proposed. A general discussion is given of our current understanding of the statics and dynamics of confined DNA in the context of to the following experiments: characterization of the liquid crystalline phases, X-ray scattering by phages, osmotic stress measurements, cyclization within globules and single-molecule determination ofthe loading forces., Comment: 32 pages, 5 figures, submitted to Phil.Trans.Roy.Soc.A ; special issue entitled "Mechanics of DNA", editor J.M.T.Thompson

We investigate a map-based model of paced cardiac muscle in the presence of closed-loop feedback control. The model relates the duration of an action potential to the preceding diastolic interval as well as the preceding action potential duration and thus has some amount of `memory.' We find that the domain of control depends on this memory, independently of the specific functional form of the map. The memory-dependent domain of control can encompass large feedback gains, thus providing the first possible explanation of the recent experimental results of Hall et al. [Phys. Rev. Lett, 88, 198102 (2002)] on controlling alternans in small pieces of rapidly-paced cardiac muscle., Comment: 10 pages, 2 figures, submitted to Phys. Rev. Lett

A model of the columnar functional organization of neocortical association areas is studied. The neuronal network is composed of many Hebbian autoassociators, or modules, each of which interacts with a relatively small number of the others. Every module encodes and stores a number of elementary percepts, or features. Memory items, or patterns, are peculiar combinations of features sparsely distributed over the multi-modular network. Any feature stored in any module can be involved in several of the stored patterns; feature-sharing is in fact source of local ambiguities and, consequently, a potential cause of erroneous memory retrieval activity spreading through the model network. The memory retrieval dynamics of the large multi-modular autoassociator is investigated by means of quantitative analysis and numerical simulations. An oscillatory retrieval process is found to be very efficient in overcoming feature-sharing drawbacks; it requires a mechanism that modulates the robustness of local attractors to noise, and neuronal activity sparseness such that quiescent and active modules are about equally noisy. Correlated activation of interconnected modules and extramodular neuronal contacts more effective than the intramodular ones seem to be general requirements in order to efficiently achieve satisfactory quality of memory retrieval. It is also shown that, even in ideal conditions, some spots of the network cannot be reached by retrieval activity spread. The locations of these activity isles depend on the pattern to retrieve and on the cue, while their extension only depends on architecture of the graph and statistics of the stored patterns. The existence of these isles determines an upper-bound to retrieval quality that does not depend on the specific retrieval dynamics adopted, nor on whether feature-sharing is permitted. The oscillatory retrieval process nearly saturates this bound., Comment: 23 pages, 8 figures (PDF), pdflatex. Pre-peer-review version (for copyright reasons). The more readable post-peer-review author's version is available on the author's personal webpage, which is accessible from orcid.org and Google Scholar

A feed-forward neural net with adaptable synaptic weights and fixed, zero or non-zero threshold potentials is studied, in the presence of a global feedback signal that can only have two values, depending on whether the output of the network in reaction to its input is right or wrong. It is found, on the basis of four biologically motivated assumptions, that only two forms of learning are possible, Hebbian and Anti-Hebbian learning. Hebbian learning should take place when the output is right, while there should be Anti-Hebbian learning when the output is wrong. For the Anti-Hebbian part of the learning rule a particular choice is made, which guarantees an adequate average neuronal activity without the need of introducing, by hand, control mechanisms like extremal dynamics. A network with realistic, i.e., non-zero threshold potentials is shown to perform its task of realizing the desired input–output relations best if it is sufficiently diluted, i.e., if only a relatively low fraction of all possible synaptic connections is realized.

The time evolution of an exactly solvable layered feedforward neural network with three-state neurons and optimizing the mutual information is studied for arbitrary synaptic noise (temperature). Detailed stationary temperature-capacity and capacity-activity phase diagrams are obtained. The model exhibits pattern retrieval, pattern-fluctuation retrieval and spin-glass phases. It is found that there is an improved performance in the form of both a larger critical capacity and information content compared with three-state Ising-type layered network models. Flow diagrams reveal that saddle-point solutions associated with fluctuation overlaps slow down considerably the flow of the network states towards the stable fixed-points., Comment: 17 pages Latex including 6 eps-figures

We develop a propagating front analysis, in terms of a local probability of zipping, for the helicase activity of opening up a double stranded DNA (dsDNA). In a fixed-distance ensemble (conjugate to the fixed-force ensemble) the front separates the zipped and unzipped phases of a dsDNA and a drive acts locally around the front. Bounds from variational analysis and numerical estimates for the speed of a helicase are obtained. Different types of helicase behaviours can be distinguished by the nature of the drive., 5 pages, 5 eps figures; replaced by the published version

A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of `path interference', which makes that the neural net quickly forgets previously learned input-output relations is tackled by adding a Hebbian term (proportional to the learning rate $\eta$) to the reinforcement term (proportional to $\rho$) in the learning rule. It is shown that the number of learning steps is reduced considerably if $1/4 < \eta/\rho < 1/2$, i.e., if the Hebbian term is neither too small nor too large compared to the reinforcement term.

We use scale-free networks to study properties of the infected mass $M$ of the network during a spreading process as a function of the infection probability $q$ and the structural scaling exponent $\gamma$. We use the standard SIR model and investigate in detail the distribution of $M$, We find that for dense networks this function is bimodal, while for sparse networks it is a smoothly decreasing function, with the distinction between the two being a function of $q$. We thus recover the full crossover transition from one case to the other. This has a result that on the same network a disease may die out immediately or persist for a considerable time, depending on the initial point where it was originated. Thus, we show that the disease evolution is significantly influenced by the structure of the underlying population., Comment: 7 pages, 3 figures, submitted to Physica A; Improved the discussion and shifted the emphasis on the distributions of figure 2. Because of this we had to change the title of the paper

We propose an assembly algorithm {\sc Barnacle} for sequences generated by the clone-based approach. We illustrate our approach by assembling the human genome. Our novel method abandons the original physical-mapping-first framework. As we show, {\sc Barnacle} more effectively resolves conflicts due to repeated sequences. The latter is the main difficulty of the sequence assembly problem. Inaddition, we are able to detect inconsistencies in the underlying data. We present and compare our results on the December 2001 freeze of the public working draft of the human genome with NCBI's assembly (Build 28). The assembly of December 2001 freeze of the public working draft generated by {\sc Barnacle} and the source code of {\sc Barnacle} are available at (http://www.cs.rutgers.edu/~vchoi)., Comment: 13 pages, 10 figures

Cu,Zn superoxide dismutase (SOD1) has been implicated in the familial form of the neurodegenerative disease Amyotrophic Lateral Sclerosis (ALS). It has been suggested that mutant mediated SOD1 misfolding/aggregation is an integral part of the pathology of ALS. We study the folding thermodynamics and kinetics of SOD1 using a hybrid molecular dynamics approach. We reproduce the experimentally observed SOD1 folding thermodynamics and find that the residues which contribute the most to SOD1 thermal stability are also crucial for apparent two-state folding kinetics. Surprisingly, we find that these residues are located on the surface of the protein and not in the hydrophobic core. Mutations in some of the identified residues are found in patients with the disease. We argue that the identified residues may play an important role in aggregation. To further characterize the folding of SOD1, we study the role of cysteine residues in folding and find that non-native disulfide bond formation may significantly alter SOD1 folding dynamics and aggregation propensity., 16 pages, 5 figures

Physical mechanisms underlying the empirical correlation between relative contact order (CO) and folding rate among naturally-occurring small single-domain proteins are investigated by evaluating postulated interaction schemes for a set of three-dimensional 27mer lattice protein models with 97 different CO values. Many-body interactions are constructed such that contact energies become more favorable when short chain segments sequentially adjacent to the contacting residues adopt native-like conformations. At a given interaction strength, this scheme leads to folding rates that are logarithmically well correlated with CO (correlation coefficient $r=0.914$) and span more than 2.5 orders of magnitude, whereas folding rates of the corresponding G\=o models with additive contact energies have much less logarithmic correlation with CO and span only approximately one order of magnitude. The present protein chain models also exhibit calorimetric cooperativity and linear chevron plots similar to that observed experimentally for proteins with apparent simple two-state folding/unfolding kinetics. Thus, our findings suggest that CO-dependent folding rates of real proteins may arise partly from a significant positive coupling between nonlocal contact favorabilities and local conformational preferences., Comment: 23 pages, 4 postscript figures (will appear on Proteins)

The entropy loss due to the formation of one or multiple loops in circular and linear DNA chains is calculated from a scaling approach in the limit of long chain segments. The analytical results allow to obtain a fast estimate for the entropy loss for a given configuration. Numerical values obtained for some examples suggest that the entropy loss encountered in loop closure in typical genetic switches may become a relevant factor which has to be overcome by the released bond energy between the looping contact sites., 7 pages, 3 figures

The hair cells of the vertebrate inner ear convert mechanical stimuli to electrical signals. Two adaptation mechanisms are known to modify the ionic current flowing through the transduction channels of the hair bundles: a rapid process involves calcium ions binding to the channels; and a slower adaptation is associated with the movement of myosin motors. We present a mathematical model of the hair cell which demonstrates that the combination of these two mechanisms can produce `self-tuned critical oscillations', i.e. maintain the hair bundle at the threshold of an oscillatory instability. The characteristic frequency depends on the geometry of the bundle and on the calcium dynamics, but is independent of channel kinetics. Poised on the verge of vibrating, the hair bundle acts as an active amplifier. However, if the hair cell is sufficiently perturbed, other dynamical regimes can occur. These include slow relaxation oscillations which resemble the hair bundle motion observed in some experimental preparations., 13 pages, 6 figures,REVTeX 4, To appear in Biophysical Journal

The curvature stress of DNA packed inside a phage is balanced against its electrostatic self-interaction. The DNA density is supposed nonuniform and as a result the Donnan effect is also inhomogeneous. The coarse-grained DNA density is a nonlinear function of the DNA radius of curvature at a given position inside the bacteriophage. It turns out that a region (or regions) exists totally free from DNA. The size of such holes is computed., 11 pages, 2 figures, submitted to J. Phys. Chem. B (special Polyelectrolytes issue)

We use mean field theory to study the response properties of a simple randomly-connected model cortical network of leaky integrate-and-fire neurons with balanced excitation and inhibition. The formulation permits arbitrary temporal variation of the input to the network and takes exact account of temporal firing correlations. We find that neuronal firing statistics depend sensitively on the firing threshold. In particular, spike count variances can be either significantly greater than or significantly less than those for a Poisson process. These findings may help in understanding the variability observed experimentally in cortical neuronal responses., 7 pages, submitted to CNS2002

Motivated by recent experiments on DNA torsion-force-extension characteristics we consider the writhing geometry of open stiff molecules. We exhibit a cyclic motion which allows arbitrarily large twisting of the end of a molecule via an activated process. This process is suppressed for forces larger than femto-Newtons which allows us to show that experiments are sensitive to a generalization of the Calugareanu-White formula for the writhe. Using numerical methods we compare this formulation of the writhe with recent analytic calculations., Comment: 12 pages 10 figures. Revtex4

Recent studies in ecology and epidemiology indicate that it is important to include spatial heterogeneity, synchronization and seasonality in the theoretical models. In this work, spatial heterogeneity is introduced via coupled map lattices (CML) and partial differential equations. Stability and persistence of some realistic CML are discussed. Chaos control, synchronization and persistence are studied for some CML. Some applications in population biology and ecology are given. A simple method for finding the sufficient conditions for the existence of periodic solutions for differential equations with periodic coefficients is given. This will simplify the study of seasonality., Comment: 10 pages

We present a simple game which mimics the complex dynamics found in most natural and social systems. Intelligent players modify their strategies periodically, depending on their performances. We propose that the agents use hybridized one-point genetic crossover mechanism,inspired by genetic evolution in biology, to modify the strategies and replace the bad strategies. We study the performances of the agents under different conditions and investigate how they adapt themselves in order to survive or be the best, by finding new strategies using the highly effective mechanism we proposed., 4 pages, 4 eps figures. Uses REVTeX

We propose a phase coexistence based mechanism for activity of helicases, ubiquitous enzymes that unwind double stranded DNA. The helicase-DNA complex constitutes a fixed-stretch ensemble that entails a coexistence of domains of zipped and unzipped phases of DNA, separated by a domain wall. The motor action of the helicase leads to a change in the position of the fixed constraint thereby shifting the domain wall on dsDNA. We associate this off-equilibrium domain wall motion with the unzipping activity of helicase. We show that this proposal gives a clear and consistent explanation of the main observed features of helicases., Comment: Revtex4. 5 pages. 4 figures. Published version

We propose a model for cell migration where epithelial cells are able to detect trajectories of other cells and try to follow them. As cells move along in 2D cell culture, they mark their paths by loosing tiny parts of cytoplasm. Any cell moving on a surface where other cells have moved before faces a network of cell trajectories, which it tries to restrict its motion onto. With the Tsallis modification of classical thermodynamics one can solve the relevant Fokker-Planck like equation and obtain experimentally testable distribution functions. We compare the model to experimental data of normal mammary epithelial cells and cells which have been genetically manipulated to change their degree of cell-cell interaction., 15 pages, 3 eps figures

We propose and solve a Hamiltonian model for multidimensional epistastatic interactions between beneficial mutations. The model is able to give rise either to a phase transition between two equilibrium states, without any coexistence, or exhibits a state where hybrid species can coexist, with gradual passage from one wild type to another. The transition takes place as a function of the "tolerance" of the environment, which we define as the amount of noise in the system., Comment: 3 pages, 2 figures (in seperate files) spelling corrected and a reference added

The soft condensed matter of biological organisms exhibits atomic motions whose properties depend strongly on temperature and hydration conditions. Due to the superposition of rapidly fluctuating alternative motions at both very low temperatures (quantum effects) and very high temperatures (classical Brownian motion regime), the dimension of an atomic ``path'' is in reality different from unity. In the intermediate temperature regime and under environmental conditions which sustain active biological functions, the fractal dimension of the sets upon which atoms reside is an open question. Measured values of the fractal dimension of the sets on which the Hydrogen atoms reside within the Azurin protein macromolecule are reported. The distribution of proton positions was measured employing thermal neutron elastic scattering from Azurin protein targets. As the temperature was raised from low to intermediate values, a previously known and biologically relevant dynamical transition was verified for the Azurin protein only under hydrated conditions. The measured fractal dimension of the geometrical sets on which protons reside in the biologically relevant temperature regime is given by $D=0.65 \pm 0.1$. The relationship between fractal dimensionality and biological function is qualitatively discussed., Comment: ReVTeX4 format with 5 *.eps figures

We introduce into the Penna Model for biological ageing one of the possible male mechanisms used to maximize the ability of their sperm to compete with sperm from other males. Such a selfish mechanism increases the male reproduction success but may decrease the survival probability of the whole female population, depending on how it acts. We also find a dynamic phase transition induced by the existence of an absorbing state where no selfish males survive., 7 pages, latex including 2 eps figures

We show that the Tangled Nature model can be interpreted as a general formulation of the quasi-species model by Eigen et al. in a frequency dependent fitness landscape. We present a detailed theoretical derivation of the mutation threshold, consistent with the simulation results, that provides a valuable insight into how the microscopic dynamics of the model determine the observed macroscopic phenomena published previously. The dynamics of the Tangled Nature model is defined on the microevolutionary time scale via reproduction, with heredity, variation, and natural selection. Each organism reproduces with a rate that is linked to the individuals' genetic sequence and depends on the composition of the population in genotype space. Thus the microevolutionary dynamics of the fitness landscape is regulated by, and regulates, the evolution of the species by means of the mutual interactions. At low mutation rate, the macro evolutionary pattern mimics the fossil data: periods of stasis, where the population is concentrated in a network of coexisting species, is interrupted by bursts of activity. As the mutation rate increases, the duration and the frequency of bursts increases. Eventually, when the mutation rate reaches a certain threshold, the population is spread evenly throughout the genotype space showing that natural selection only leads to multiple distinct species if adaptation is allowed time to cause fixation., Comment: Paper submitted to Journal of Physics A. 13 pages, 4 figures

We investigate a statistical model for multidimensional epistasis. The genotype is devided into subsequences, and within each subsequence mutations which occur in a prescribed order are beneficial. The bit-string model used to represent the genotype, may be cast in the form of a ferromagnetic Ising model with a staggered field. We obtain the actual correlations between mutations at different sites, within an equilibrium population at a given "tolerance," which we define to be the temperature of the statistical ensemble., 3 pages, figures available seperately as .eps files

We introduce a spherical Hopfield-type neural network involving neurons and patterns that are continuous variables. We study both the thermodynamics and dynamics of this model. In order to have a retrieval phase a quartic term is added to the Hamiltonian. The thermodynamics of the model is exactly solvable and the results are replica symmetric. A Langevin dynamics leads to a closed set of equations for the order parameters and effective correlation and response function typical for neural networks. The stationary limit corresponds to the thermodynamic results. Numerical calculations illustrate our findings., 9 pages Latex including 3 eps figures, Addition of an author in the HTML-abstract unintentionally forgotten, no changes to the manuscript

Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans allows increasing the detection power, but haplotype-based methods require individual genotypes and are not applicable on pool-sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium, accumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal, avoiding windowing methods. This method provided results similar to haplotype-based methods on two benchmark data sets with individual genotypes. Results obtained for a divergent selection experiment on behavior in quail, where two lines were sequenced in pools, are precise and biologically coherent, while competing methods failed: our approach led to the detection of signals involving genes known to act on social responsiveness or autistic traits. This local score approach is general and can be applied to other genome-wide analyzes such as GWAS or genome scans for selection., Comment: 32 pages, 4 figures

We present a stochastic model of gait rhythm dynamics, based on transitions between different ``neural centers'', that reproduces distinctive statistical properties of normal human walking. By tuning one model parameter, the hopping range, the model can describe alterations in gait dynamics from childhood to adulthood --- including a decrease in the correlation and volatility exponents with maturation. The model also generates time series with multifractal spectra whose broadness depends only on this parameter., Comment: 4 revtex pages, 6 figures

Barn owls hunt in the dark by using cues from both sight and sound to locate their prey. This task is facilitated by topographic maps of the external space formed by neurons (e.g., in the optic tectum) that respond to visual or aural signals from a specific direction. Plasticity of these maps has been studied in owls forced to wear prismatic spectacles that shift their visual field. Adaptive behavior in young owls is accompanied by a compensating shift in the response of (mapped) neurons to auditory signals. We model the receptive fields of such neurons by linear filters that sample correlated audio-visual signals, and search for filters that maximize the gathered information, while subject to the costs of rewiring neurons. Assuming a higher fidelity of visual information, we find that the corresponding receptive fields are robust and unchanged by artificial shifts. The shape of the aural receptive field, however, is controlled by correlations between sight and sound. In response to prismatic glasses, the aural receptive fields shift in the compensating direction, although their shape is modified due to the costs of rewiring., 7 pages, 1 figure

It has been conjectured that evolution exerted pressure to preserve amino acids bearing thermodynamic, kinetic, and functional roles. In this letter we show that the physical requirement to maintain protein stability gives rise to a sequence conservatism pattern that is in remarkable agreement with that found in natural proteins. Based on the physical properties of amino acids, we propose a model of evolution that explains conserved amino acids across protein families sharing the same fold., 5 pages, 3 figures

This paper reviews the molecular theory of hydrophobic effects relevant to biomolecular structure and assembly in aqueous solution. Recent progress has resulted in simple, validated molecular statistical thermodynamic theories and clarification of confusing theories of decades ago. Current work is resolving effects of wider variations of thermodynamic state, e.g. pressure denaturation of soluble proteins, and more exotic questions such as effects of surface chemistry in treating stability of macromolecular structures in aqueous solution, Comment: submitted to Ann. Rev. Phys. Chem., 31 pages, 245 references, 2 figures

We compared the properties of the error threshold transition in quasispecies evolution for three different topologies of the genome space. They are a) hypercube b) rugged landscape modelled by an ultrametric space, and c) holey landscape modelled by Bethe lattice. In all studied topologies the phase transition exists. We calculated the critical exponents in all the cases. For the critical exponent corresponding to appropriately defined susceptibility we found super-universal value., 23 pages, 4 figures, accepted for publication in Physica A, LaTeX - elsart

Spatially or chemically isolated functional modules composed of several cellular components and carrying discrete functions are considered fundamental building blocks of cellular organization, but their presence in highly integrated biochemical networks lacks quantitative support. Here, we show that the metabolic networks of 43 distinct organisms are organized into many small, highly connected topologic modules that combine in a hierarchical manner into larger, less cohesive units, with their number and degree of clustering following a power law. Within Escherichia coli , the uncovered hierarchical modularity closely overlaps with known metabolic functions. The identified network architecture may be generic to system-level cellular organization.

It has become widely accepted that the most dangerous cardiac arrhythmias are due to re- entrant waves, i.e., electrical wave(s) that re-circulate repeatedly throughout the tissue at a higher frequency than the waves produced by the heart's natural pacemaker (sinoatrial node). However, the complicated structure of cardiac tissue, as well as the complex ionic currents in the cell, has made it extremely difficult to pinpoint the detailed mechanisms of these life-threatening reentrant arrhythmias. A simplified ionic model of the cardiac action potential (AP), which can be fitted to a wide variety of experimentally and numerically obtained mesoscopic characteristics of cardiac tissue such as AP shape and restitution of AP duration and conduction velocity, is used to explain many different mechanisms of spiral wave breakup which in principle can occur in cardiac tissue. Some, but not all, of these mechanisms have been observed before using other models; therefore, the purpose of this paper is to demonstrate them using just one framework model and to explain the different parameter regimes or physiological properties necessary for each mechanism (such as high or low excitability, corresponding to normal or ischemic tissue, spiral tip trajectory types, and tissue structures such as rotational anisotropy and periodic boundary conditions). Each mechanism is compared with data from other ionic models or experiments to illustrate that they are not model-specific phenomena. The fact that many different breakup mechanisms exist has important implications for antiarrhythmic drug design and for comparisons of fibrillation experiments using different species, electromechanical uncoupling drugs, and initiation protocols., 128 pages, 42 figures (29 color, 13 b&w)

Airborne contaminants, e.g., bacterial spores, are usually analyzed by time consuming microscopic, chemical and biological assays. Current research into real time laser spectroscopic detectors of such contaminants is based on e.g. resonant Raman spectroscopy. The present approach derives from recent experiments in which atoms and molecules are prepared by one (or more) coherent laser(s) and probed by another set of lasers. The connection with previous studies based on "Coherent Anti-Stokes Raman Spectroscopy" (CARS) is to be noted. However generating and utilizing maximally coherent oscillation in macromolecules having an enormous number of degrees of freedom is much more challenging. This extension of the CARS technique is called FAST CARS (Femtosecond Adaptive Spectroscopic Techniques for Coherent Anti-Stokes Raman Spectroscopy), and the present paper proposes and analyses ways in which it could be used to rapidly identify pre-selected molecules in real time., Comment: 43 pages, 21 figures; replacement with references added. Submitted to the Proceedings of National Academy of Sciences

The folding kinetics of three-dimensional lattice Go models with side chains is studied using two different Monte Carlo move sets. A flexible move set based on single, double and triple backbone moves is found to be far superior compared to the standard Monte Carlo dynamics. It was found that the folding time grows as a power law with the chain length and the corresponding exponent $\lambda \approx 3.6$ for Go models. The study shows that the incorporation of side chains dramatically slows down folding rates., Comment: 4 pages, CCP2001 proceeding paper