Cross-taxon surrogacy of biodiversity in the Indian Garhwal Himalaya

Biodiversity surveys were conducted in 13, 10x50 m(2) plots located between 1400 to 3100 in abode mean sea level in a range of habitats in temperate mixed Oak and Coniferous forests through sub-alpine to the alpine grasslands in Chamoli district of Uttaranchal state in the Indian Garhwal Himalaya. Cross-taxon congruence in biodiversity (alpha-diversity and beta-diversity) across macrolichens, mosses, liverworts, woody plants (shrubs and trees) and ants was investigated, so as to examine the extent to which these group, of organisms can function as Surrogates for each other. Although woody plants provided a major substrate for macrolichens and mosses, there was no species-specific association between them. Woody plant species richness was highly positively correlated with mosses (r(2) = 0.63, P < 0.001) but the relationship, as not particularly very strong with lichens and liverworts. While there was a significant correlation in the species turnover (β-diversity) of macrolichens with mosses (r(2) = 0.21 P < 0.005). the relationship was relatively poor with the woody plants. On the other hand. negative correlations emerged in the species richness of ants with those of macrolichens, mosses and woody plants (r(2) = -0.44 P < 0.05). but most of the complementarity (turnover) relationships among them were positive, Since diversity between taxonomic hierarchies within the group was consistently significantly positively correlated in all these taxa, the higher taxonomic categories Such as genus and family may be employed as surrogates for rapid assessment and monitoring of species diversity, Although no single group other than macrolichens has emerged as a good indicator of changes in species richness in all other groups, some concordant relationships between them conform to the hypothesis that species assemblages of certain taxonomic groups could still be used as surrogates for efficient monitoring of species diversity in other groups whose distribution may further predict the importance of conserving overall biodiversity in landscapes such as the Garhwal Himalaya. (C) 2002 Elsevier Science Ltd. All rights reserved.

Inferring biochemical routes from biochemical networks

Metabolism is a defining feature of life, and its study is important to understand how a cell works, alterations that lead to disease and for applications in drug discovery. From a systems perspective, metabolism can be represented as a network that captures all the metabolites as nodes and the inter-conversions among pairs of them as edges. Such an abstraction enables the networks to be studied by applying graph theory, particularly, to infer the flow of chemical information in the networks by identifying relevant metabolic pathways. In this study, different weighting schemes are used to illustrate that appropriately weighted networks can capture the quantitative cellular dynamics quite accurately. Thus, the networks now combine the elegance and simplicity of representation of the system and ease of analysing metabolic graphs. Metabolic routes or paths determined by this therefore are likely to be more biologically meaningful. The usefulness of the approach is demonstrated with two examples, first for understanding bacterial stress response and second for studying metabolic alterations that occurs in cancer cells.

Computational framework to integrate the effect of antigen recognition on disease epidemiology outcome: multi-scale approach

Human Leukocyte Antigen (HLA) plays an important role, in presenting foreign pathogens to our immune system, there by eliciting early immune responses. HLA genes are highly polymorphic, giving rise to diverse antigen presentation capability. An important factor contributing to enormous variations in individual responses to diseases is differences in their HLA profiles. The heterogeneity in allele specific disease responses decides the overall disease epidemiological outcome. Here we propose an agent based computational framework, capable of incorporating allele specific information, to analyze disease epidemiology. This framework assumes a SIR model to estimate average disease transmission and recovery rate. Using epitope prediction tool, it performs sequence based epitope detection for a given the pathogenic genome and derives an allele specific disease susceptibility index depending on the epitope detection efficiency. The allele specific disease transmission rate, that follows, is then fed to the agent based epidemiology model, to analyze the disease outcome. The methodology presented here has a potential use in understanding how a disease spreads and effective measures to control the disease.