Monte-Carlo estimation of time-dependent statistical characteristics of random dynamical systems

The problem of estimating the time-dependent statistical characteristics of a random dynamical system is studied under two different settings. In the first, the system dynamics is governed by a differential equation parameterized by a random parameter, while in the second, this is governed by a differential equation with an underlying parameter sequence characterized by a continuous time Markov chain. We propose, for the first time in the literature, stochastic approximation algorithms for estimating various time-dependent process characteristics of the system. In particular, we provide efficient estimators for quantities such as the mean, variance and distribution of the process at any given time as well as the joint distribution and the autocorrelation coefficient at different times. A novel aspect of our approach is that we assume that information on the parameter model (i.e., its distribution in the first case and transition probabilities of the Markov chain in the second) is not available in either case. This is unlike most other work in the literature that assumes availability of such information. Also, most of the prior work in the literature is geared towards analyzing the steady-state system behavior of the random dynamical system while our focus is on analyzing the time-dependent statistical characteristics which are in general difficult to obtain. We prove the almost sure convergence of our stochastic approximation scheme in each case to the true value of the quantity being estimated. We provide a general class of strongly consistent estimators for the aforementioned statistical quantities with regular sample average estimators being a specific instance of these. We also present an application of the proposed scheme on a widely used model in population biology. Numerical experiments in this framework show that the time-dependent process characteristics as obtained using our algorithm in each case exhibit excellent agreement with exact results. (C) 2010 Elsevier Inc. All rights reserved.

On the moulding of population viscosity by natural selection

In this paper, we explore the conjoint evolution of dispersal and social behaviour. The model investigated is of a population distributed over a number of sites each with a carrying capacity of two adults and an episode of dispersal in the juvenile stage. The fertilities are governed by whether an individual and its neighbour are selfish or co-operative. It is shown that the best dispersal strategy for the co-operative genotype always involves lower levels of dispersal; and further that ecological conditions favouring low levels of dispersal increase the selective advantage of a co-operative genotype. Given this positive feedback, we suggest that in any taxon viscosity and co-operativity will tend to be correlated and bimodally distributed. Hence we predict the existence of two kinds of animal societies; viscous and co-operative (e.g. quasi-social wasps such as Mischocyttarus), and non-viscous and selfish (e.g. communal sphecid wasps such as Cerceris), and relatively few social groups with intermediate levels of co-operativity and viscosity. We also suggest that when one of the two sexes disperses, it will be the sex with lower potential for co-operative behaviour.

Consanguinity, twinning and secondary sex ratio in the population of Karnataka, South India

Consanguineous marriages are strongly favoured in the state of Karnataka. Of 65492 marriages studied 33·07% were consanguineous, equivalent to a coefficient of inbreeding (F) of 0·0298. The twinning rate was low, 6·9 per thousand, whereas the secondary sex ratio, 0·5221, was higher than in comparable major human populations. Consanguinity exerted no significant effect on either parameter. The results also indicate that consanguinity is not associated with excess antenatal losses and suggest the possibility of enhanced selection against mutations at X chromosome loci.

A finite population model of mlecular evolution: theory and computation

This article is concerned with the evolution of haploid organisms that reproduce asexually. In a seminal piece of work, Eigen and coauthors proposed the quasispecies model in an attempt to understand such an evolutionary process. Their work has impacted antiviral treatment and vaccine design strategies. Yet, predictions of the quasispecies model are at best viewed as a guideline, primarily because it assumes an infinite population size, whereas realistic population sizes can be quite small. In this paper we consider a population genetics-based model aimed at understanding the evolution of such organisms with finite population sizes and present a rigorous study of the convergence and computational issues that arise therein. Our first result is structural and shows that, at any time during the evolution, as the population size tends to infinity, the distribution of genomes predicted by our model converges to that predicted by the quasispecies model. This justifies the continued use of the quasispecies model to derive guidelines for intervention. While the stationary state in the quasispecies model is readily obtained, due to the explosion of the state space in our model, exact computations are prohibitive. Our second set of results are computational in nature and address this issue. We derive conditions on the parameters of evolution under which our stochastic model mixes rapidly. Further, for a class of widely used fitness landscapes we give a fast deterministic algorithm which computes the stationary distribution of our model. These computational tools are expected to serve as a framework for the modeling of strategies for the deployment of mutagenic drugs.

A landscape-level assessment of Asian elephant habitat, its population and elephant-human conflict in the Anamalai hill ranges of southern Western Ghats, India

Spatial information at the landscape scale is extremely important for conservation planning, especially in the case of long-ranging vertebrates. The biodiversity-rich Anamalai hill ranges in the Western Ghats of southern India hold a viable population for the long-term conservation of the Asian elephant. Through rapid but extensive field surveys we mapped elephant habitat, corridors, vegetation and land-use patterns, estimated the elephant population density and structure, and assessed elephant-human conflict across this landscape. GIS and remote sensing analyses indicate that elephants are distributed among three blocks over a total area of about 4600 km(2). Approximately 92% remains contiguous because of four corridors; however, under 4000 km2 of this area may be effectively used by elephants. Nine landscape elements were identified, including five natural vegetation types, of which tropical moist deciduous forest is dominant. Population density assessed through the dung count method using line transects covering 275 km of walk across the effective elephant habitat of the landscape yielded a mean density of 1.1 (95% Cl = 0.99-1.2) elephant/km(2). Population structure from direct sighting of elephants showed that adult male elephants constitute just 2.9% and adult females 42.3% of the population with the rest being subadults (27.4%), juveniles (16%) and calves (11.4%). Sex ratios show an increasing skew toward females from juvenile (1:1.8) to sub-adult (1:2.4) and adult (1:14.7) indicating higher mortality of sub-adult and adult males that is most likely due to historical poaching for ivory. A rapid questionnaire survey and secondary data on elephant-human conflict from forest department records reveals that villages in and around the forest divisions on the eastern side of landscape experience higher levels of elephant-human conflict than those on the western side; this seems to relate to a greater degree of habitat fragmentation and percentage farmers cultivating annual crops in the east. We provide several recommendations that could help maintain population viability and reduce elephant-human conflict of the Anamalai elephant landscape. (C) 2013 Deutsche Gesellschaft far Saugetierkunde. Published by Elsevier GmbH. All rights reserved.

REPERTOIRE OF PROTEIN KINASES ENCODED IN THE GENOME OF ZEBRAFISH SHOWS REMARKABLY LARGE POPULATION OF PIM KINASES

In recent times, zebrafish has garnered lot of popularity as model organism to study human cancers. Despite high evolutionary divergence from humans, zebrafish develops almost all types of human tumors when induced. However, mechanistic details of tumor formation have remained largely unknown. Present study is aimed at analysis of repertoire of kinases in zebrafish proteome to provide insights into various cellular components. Annotation using highly sensitive remote homology detection methods revealed ``substantial expansion'' of Ser/Thr/Tyr kinase family in zebrafish compared to humans, constituting over 3% of proteome. Subsequent classification of kinases into subfamilies revealed presence of large number of CAMK group of kinases, with massive representation of PIM kinases, important for cell cycle regulation and growth. Extensive sequence comparison between human and zebrafish PIM kinases revealed high conservation of functionally important residues with a few organism specific variations. There are about 300 PIM kinases in zebrafish kinome, while human genome codes for only about 500 kinases altogether. PIM kinases have been implicated in various human cancers and are currently being targeted to explore their therapeutic potentials. Hence, in depth analysis of PIM kinases in zebrafish has opened up new avenues of research to verify the model organism status of zebrafish.

Does size matter? Comparative population genetics of two butterflies with different wingspans

The dispersal ability of a species is central to its biology, affecting other processes like local adaptation, population and community dynamics, and genetic structure. Among the intrinsic, species-specific factors that affect dispersal ability in butterflies, wingspan was recently shown to explain a high amount of variance in dispersal ability. In this study, a comparative approach was adopted to test whether a difference in wingspan translates into a difference in population genetic structure. Two closely related butterfly species from subfamily Satyrinae, family Nymphalidae, which are similar with respect to all traits that affect dispersal ability except for wingspan, were studied. Melanitis leda (wingspan 60-80 mm) and Ypthima baldus (wingspan 30-40 mm) were collected from the same areas along the Western Ghats of southern India. Amplified fragment length polymorphisms were used to test whether the species with a higher wingspan (M. leda) exhibited a more homogenous population genetic structure, as compared to a species with a shorter wingspan (Y. baldus). In all analyses, Y. baldus exhibited greater degree of population genetic structuring. This study is one of the few adopting a comparative approach to establish the relationship between traits that affect dispersal ability and population genetic structure.

Use of a coupled transcriptional system for consistent overexpression and purification of UDG-Ugi complex and Ugi from Escherichia coli

We have designed a novel coupled transcriptional construct wherein Escherichia coil uracil DNA glycosylase (UDC:) and Bacillus subtilis phage PBS-2 encoded uracil DNA glycosylase inhibitor protein (Ugi) genes were cloned in tandem, downstream of an inducible promoter (P-trc). Use of this bicistronic operon has allowed purification of large amounts of UDG-Ugi complex formed in vivo. The system has also been exploited for purification of large amounts of Ugi. While establishing the expression system, one of the constructs showed detectable suppression of UAG termination codon and resulted in accumulation of a minor population of a putative readthrough polypeptide cor responding to UDG. We discuss the likely occurrence of such a phenomenon in overproduction of other recombinant proteins. Finally, the usefulness of the operon construct in convenient mutational analysis to study the mechanism of UDG-Ugi interaction is also discussed.

Precipitation In Small Systems--II. Mean Field Equations More Effective Than Population Balance

Part I (Manjunath et al., 1994, Chem. Engng Sci. 49, 1451-1463) of this paper showed that the random particle numbers and size distributions in precipitation processes in very small drops obtained by stochastic simulation techniques deviate substantially from the predictions of conventional population balance. The foregoing problem is considered in this paper in terms of a mean field approximation obtained by applying a first-order closure to an unclosed set of mean field equations presented in Part I. The mean field approximation consists of two mutually coupled partial differential equations featuring (i) the probability distribution for residual supersaturation and (ii) the mean number density of particles for each size and supersaturation from which all average properties and fluctuations can be calculated. The mean field equations have been solved by finite difference methods for (i) crystallization and (ii) precipitation of a metal hydroxide both occurring in a single drop of specified initial supersaturation. The results for the average number of particles, average residual supersaturation, the average size distribution, and fluctuations about the average values have been compared with those obtained by stochastic simulation techniques and by population balance. This comparison shows that the mean field predictions are substantially superior to those of population balance as judged by the close proximity of results from the former to those from stochastic simulations. The agreement is excellent for broad initial supersaturations at short times but deteriorates progressively at larger times. For steep initial supersaturation distributions, predictions of the mean field theory are not satisfactory thus calling for higher-order approximations. The merit of the mean field approximation over stochastic simulation lies in its potential to reduce expensive computation times involved in simulation. More effective computational techniques could not only enhance this advantage of the mean field approximation but also make it possible to use higher-order approximations eliminating the constraints under which the stochastic dynamics of the process can be predicted accurately.

Refinement of defection strategies stabilizes cooperation

Cooperation among unrelated individuals is an enduring evolutionary riddle and a number of possible solutions have been suggested. Most of these suggestions attempt to refine cooperative strategies, while little attention is given to the fact that novel defection strategies can also evolve in the population. Especially in the presence of punishment to the defectors and public knowledge of strategies employed by the players, a defecting strategy that avoids getting punished by selectively cooperating only with the punishers can get a selective benefit over non-conditional defectors. Furthermore, if punishment ensures cooperation from such discriminating defectors, defectors who punish other defectors can evolve as well. We show that such discriminating and punishing defectors can evolve in the population by natural selection in a Prisoner’s Dilemma game scenario, even if discrimination is a costly act. These refined defection strategies destabilize unconditional defectors. They themselves are, however, unstable in the population. Discriminating defectors give selective benefit to the punishers in the presence of non-punishers by cooperating with them and defecting with others. However, since these players also defect with other discriminators they suffer fitness loss in the pure population. Among the punishers, punishing cooperators always benefit in contrast to the punishing defectors, as the latter not only defect with other punishing defectors but also punish them and get punished. As a consequence of both these scenarios, punishing cooperators get stabilized in the population. We thus show ironically that refined defection strategies stabilize cooperation. Furthermore, cooperation stabilized by such defectors can work under a wide range of initial conditions and is robust to mistakes.