A critical examination of stoichiometric and path-finding approaches to metabolic pathways

Advances in the field of genomics have enabled computational analysis of metabolic pathways at the genome scale. Singular attention has been devoted in the literature to stoichiometric approaches, and path-finding approaches, to metabolic pathways. Stoichiometric approaches make use of reaction stoichiometry when trying to determine metabolic pathways. Stoichiometric approaches involve elementary flux modes and extreme pathways. In contrast, path-finding approaches propose an alternative view based on graph theory in which reaction stoichiometry is not considered. Path-finding approaches use shortest path and k-shortest path concepts. In this article we give a critical overview of the theory, applications and key research challenges of stoichiometric and path-finding approaches to metabolic pathways.     

Resolving the roles of body size and species identity in driving functional diversity

Efforts to characterize food webs have generated two influential approaches that reduce the complexity of natural communities. The traditional approach groups individuals based on their species identity, while recently developed approaches group individuals based on their body size. While each approach has provided important insights, they have largely been used in parallel in different systems. Consequently, it remains unclear how body size and species identity interact, hampering our ability to develop a more holistic framework that integrates both approaches. We address this conceptual gap by developing a framework which describes how both approaches are related to each other, revealing that both approaches share common but untested assumptions about how variation across size classes or species influences differences in ecological interactions among consumers. Using freshwater mesocosms with dragonfly larvae as predators, we then experimentally demonstrate that while body size strongly determined how predators affected communities, these size effects were species specific and frequently nonlinear, violating a key assumption underlying both size- and species-based approaches. Consequently, neither purely species- nor size-based approaches were adequate to predict functional differences among predators. Instead, functional differences emerged from the synergistic effects of body size and species identity. This clearly demonstrates the need to integrate size- and species-based approaches to predict functional diversity within communities.     

<Emphasis Type="Italic">In vivo</Emphasis> imaging approaches in animal models of rheumatoid arthritis

The interaction of activated leukocytes with the rheumatoid synovial environment is a key process in arthritis. Understanding this process will play an important role in designing effective treatments. In vivo imaging approaches combined with molecular genetics in animal models provide important tools to address these issues. The present review will focus on approaches to in vivo imaging, with particular attention to approaches that are proving useful for, or have promise for, research on animal models of rheumatoid arthritis. These approaches will probably shed light on the specific local mechanisms involved in chronic inflammation and provide real time monitoring approaches to follow cellular and molecular events related to disease development.     

Evaluation of Ecotoxicity Effect Indicators for Use in LCIA (10+4 pp)

Goal, Scope and Background The paper describes different ecotoxicity effect indicator methods/approaches. The approaches cover three main groups, viz. PNEC approaches, PAF approaches and damage approaches. Ecotoxicity effect indicators used in life cycle impact assessment (LCIA) are typically modelled to the level of impact, indicating the potential impact on 'ecosystem health'. The few existing indicators, which are modelled all the way to damage, are poorly developed, and even though relevant alternatives from risk assessment exist (e.g. recovery time and mean extinction time), these are unfortunately at a very early stage of development, and only few attempts have been made to include them in LCIA. Methods The approaches are described and evaluated against a set of assessment criteria comprising compatibility with the methodological requirements of LCIA, environmental relevance, reproducibility, data demand, data availability, quantification of uncertainty, transparency and spatial differentiation. Results and Discussion The results of the evaluation of the two impact approaches (i.e. PNEC and PAF) show both pros and cons for each of them. The assessment factor-based PNEC approaches have a low data demand and use only the lowest data (e.g. lowest NOEC value). Because it is developed in tiered risk assessment, and hence makes use of conservative assessment factors, it is not optimal, in its present form, to use in the comparative framework of LCIA, where best estimates are sought. The PAF approaches have a higher data demand but use all data and can be based on effect data (PNEC is no-effect-based), thus making these approaches non-conservative and more suitable for LCIA. However, indiscriminate use of ecotoxicity data tends to make the PAF-approaches no more environmentally relevant than the assessment factor-based PNEC approaches. The PAF approaches, however, can at least in theory be linked to damage modelling. All the approaches for damage modelling which are included here have a high environmental relevance but very low data availability, apart from the 'media recovery-approach', which depends directly on the fate model. They are all at a very early stage of development. Conclusion Recommendations and Outlook. An analysis of the different PAF approaches shows that the crucial point is according to which principles and based on which data the hazardous concentration to 50% of the included species (i.e. HC50) is estimated. The ability to calculate many characterisation factors for ecotoxicity is important for this impact category to be included in LCIA in a proper way. However, the access to effect data for the relevant chemicals is typically limited. So, besides the coupling to damage modelling, the main challenge within the further development and improvement of ecotoxicity effect indicators is to find an optimal method to estimate HC50 based on little data.     

Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data

Approaches based on linear mixed models (LMMs) have recently gained popularity for modelling population substructure and relatedness in genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. Given their strong concordance, in most applications, the choice of precise LMM implementation cannot be based on power/type I error considerations but must instead be based on considerations such as speed and ease-of-use.     

Biological Indicator Systems in Floodplains – a Review

Based on a literature review, the different approaches to biological indicator systems in floodplains are summarised. Four general categories of bioindication are defined and proposed here: 1. Classification indicators, 2.1 Environmental indicators, 2.2 Biodiversity indicators, 3. Valuation indicators. Furthermore, existing approaches in floodplains are classified according to the four categories. Relevant and widely used approaches in floodplains are explained in more detail. The results of the RIVA project are put into the context of these indication approaches. It is concluded that especially functional assessment approaches using biological traits of the species can be seen as very promising and deserve more attention by conservation biologists and floodplain ecologists. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Towards systematic functional characterization of cancer genomes

Whole-genome approaches to identify genetic and epigenetic alterations in cancer genomes have begun to provide new insights into the range of molecular events that occurs in human tumours. Although in some cases this knowledge immediately illuminates a path towards diagnostic or therapeutic implementation, the bewildering lists of mutations in each tumour make it clear that systematic functional approaches are also necessary to obtain a comprehensive molecular understanding of cancer. Here we review the current range of methods, assays and approaches for genome-scale interrogation of gene function in cancer. We also discuss the integration of functional-genomics approaches with the outputs from cancer genome sequencing efforts.     

Combinatorial genetic perturbation to refine metabolic circuits for producing biofuels and biochemicals

Recent advances in metabolic engineering have enabled microbial factories to compete with conventional processes for producing fuels and chemicals. Both rational and combinatorial approaches coupled with synthetic and systematic tools play central roles in metabolic engineering to create and improve a selected microbial phenotype. Compared to knowledge-based rational approaches, combinatorial approaches exploiting biological diversity and high-throughput screening have been demonstrated as more effective tools for improving various phenotypes of interest. In particular, identification of unprecedented targets to rewire metabolic circuits for maximizing yield and productivity of a target chemical has been made possible. This review highlights general principles and the features of the combinatorial approaches using various libraries to implement desired phenotypes for strain improvement. In addition, recent applications that harnessed the combinatorial approaches to produce biofuels and biochemicals will be discussed.     

Lifting capacity of workers

In order to establish a valid criteria on which to establish lifting guidelines, lifting capacity determination must be made. This has been attempted using several approaches mainly psychophysical, physiological, and biomechanical methodologies. Although the data obtained from these approaches yield information about lifting capacity, some conflict in these data can be found. The purpose of this paper is to discuss the three different approaches, the data obtained from these approaches, and some data on the lifting capacity for males and females in the industrial workforce. In addition, the conflicts in these data are also presented.     

Talking with the Donkey: Cooperative Approaches to Environmental Protection

In recent years, governments throughout the world have expressed growing interest in cooperative approaches to environmental protection, including negotiated rulemaking, flexible approaches t o enforcement, and voluntary codes and agreements. It is often argued that cooperative approaches are more cost effective, more conducive t o innovation, and better able t o promote fundamental attitudinal change than traditional "command and control" regulation. However, the overly broad term "cooperative approaches" fails t o acknowledge fundamental differences among these novel po I icies, including distinct ions between mandatory and voluntary programs and between those that involve bipartite negotiations between government and business and those that invite participation by a broader range of interests. This article analyzes these cooperative approaches first by offering a framework to distinguish among various cooperative policy instruments. Second, the article critically examines theoretical arguments and empirical evidence concerning one class o f cooperative approaches, voluntary challenges and agreements. The most striking finding is how little we know about the effectiveness of voluntary approaches. This is a function not only of the quite recent experience with these approaches, but also of more fundamental inattention t o program evaluation and obstacles to evaluation inherent in voluntary programs. The article concludes with a call for a more rigorous program of research to examine the effectiveness of the new policy instruments and t o compare them with traditional regulation and market-based incentives.     

Ecosystem Approaches to Human Health and Well-being: Reflections from Use in a Mining Context

This article briefly discusses the evolution of ecosystem approaches, and illustrates the use of ecosystem approaches to assess human health and well-being in a mining context. It discusses the various elements that help distinguish such approaches from other approaches. Well-being here is understood broadly in terms of its “constituents” and “determinants,” of which health is an important constituent. Ecological, health, and social assessments highlighted a number of impacts from mining activity in Goa, India. These generated a list of issues of concern that were validated by stakeholders—community, industry, and government—which served as a basis for the development of tools to track mining-induced changes in health and well-being. The article concludes by reflecting on some of the challenges posed by the use of ecosystem approaches to assessing human health and well-being.     

The importance of patient-specific radiation dose calculations for the administration of radionuclides in therapy.

This paper advocates patient-specific approaches to radiation dose calculations for radionuclides used in therapy and outlines strategies for implementing such approaches. The use of a simple approaches to radionuclide therapy, e.g. a single amount of activity for all patients or the same amount of activity administered per unit body weight do not permit the optimization of individual patient therapy. While limitations in current models and logistic problems prevent dose calculations of the quality currently enjoyed with external radiation therapy approaches, improvements can be made, and models are constantly evolving. Specific suggestions regarding the extension of current models, and of the use of new models which use image data from individual patients, are discussed in the context of allowing radiotherapy with internal emitters to employ the kind of patient-specific approaches that are used in other therapeutic modalities, which are clearly in the patients' best interests.     

Parametric and nonparametric FDR estimation revisited

Nonparametric and parametric approaches have been proposed to estimate false discovery rate under the independent hypothesis testing assumption. The parametric approach has been shown to have better performance than the nonparametric approaches. In this article, we study the nonparametric approaches and quantify the underlying relations between parametric and nonparametric approaches. Our study reveals the conservative nature of the nonparametric approaches, and establishes the connections between the empirical Bayes method and p-value-based nonparametric methods. Based on our results, we advocate using the parametric approach, or directly modeling the test statistics using the empirical Bayes method.     

microRNA计算发现方法的研究进展

microRNA (miRNA)是近几年发现的一类长度为～21 nt的内源非编码小RNA, 在植物和动物中发挥着重要而广泛的调控功能。它的发现主要有cDNA克隆测序和计算发现两条途径。由于cDNA克隆测序方法受miRNA表达的时间和组织特异性以及表达水平的影响, 而计算发现可以弥补其不足, 因此miRNA的计算发现方法研究受到了广泛的重视。文章对近几年计算发现miRNA的研究进展进行了综述, 根据计算发现方法的本质, 将计算发现方法归纳为5类, 分别是同源片段搜索方法、基于比较基因组学的预测方法、基于序列和结构特征打分的预测方法、结合作用靶标的预测方法和基于机器学习的预测方法, 并对各类方法的原理、核心思想、优点和局限性进行了分析, 最后探讨了进一步的发展方向。     

Machine learning methods without tears: a primer for ecologists

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.     

Genomic approaches to fungal pathogenicity

Within a few years, the genome sequences of a large number of medically and agriculturally important fungi will be known. With this resource come the promises of genomic approaches to study pathogenicity and host-fungus interactions. Genomics is particularly attractive for these questions, as conventional genetic and biochemical approaches are limited in many pathogenic fungi. Recent work has applied signature-tagged mutagenesis and DNA microarray analysis to virulence studies in several fungal species, and novel approaches, such as protein arrays and genomic deletion libraries, are being developed in Saccharomyces cerevisiae and have significant potential in other fungi. High-throughput gene-discovery approaches should greatly increase our understanding of fungal pathogenesis.     

Integrating forward and reverse proteomics to unravel protein function

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.     

Adaptations to new environments in humans: the role of subtle allele frequency shifts

Humans show tremendous phenotypic diversity across geographically distributed populations, and much of this diversity undoubtedly results from genetic adaptations to different environmental pressures. The availability of genome-wide genetic variation data from densely sampled populations offers unprecedented opportunities for identifying the loci responsible for these adaptations and for elucidating the genetic architecture of human adaptive traits. Several approaches have been used to detect signals of selection in human populations, and these approaches differ in the assumptions they make about the underlying mode of selection. We contrast the results of approaches based on haplotype structure and differentiation of allele frequencies to those from a method for identifying single nucleotide polymorphisms strongly correlated with environmental variables. Although the first group of approaches tends to detect new beneficial alleles that were driven to high frequencies by selection, the environmental correlation approach has power to identify alleles that experienced small shifts in frequency owing to selection. We suggest that the first group of approaches tends to identify only variants with relatively strong phenotypic effects, whereas the environmental correlation methods can detect variants that make smaller contributions to an adaptive trait.     

Current views on <Emphasis>HIV-1</Emphasis> latency,persistence, and cure

HIV-1 infection cannot be cured as it persists in latently infected cells that are targeted neither by the immune system nor by available therapeutic approaches. Consequently, a lifelong therapy suppressing only the actively replicating virus is necessary. The latent reservoir has been defined and characterized in various experimental models and in human patients, allowing research and development of approaches targeting individual steps critical for HIV-1 latency establishment, maintenance, and reactivation. However, additional mechanisms and processes driving the remaining low-level HIV-1 replication in the presence of the suppressive therapy still remain to be identified and targeted. Current approaches toward HIV-1 cure involve namely attempts to reactivate and purge HIV latently infected cells (so-called “shock and kill” strategy), as well as approaches involving gene therapy and/or gene editing and stem cell transplantation aiming at generation of cells resistant to HIV-1. This review summarizes current views and concepts underlying different approaches aiming at functional or sterilizing cure of HIV-1 infection.     

The reliability of observational approaches for detecting interspecific parasite interactions: comparison with experimental results

Interactions among coinfecting parasites have the potential to alter host susceptibility to infection, the progression of disease and the efficacy of disease control measures. It is therefore essential to be able to accurately infer the occurrence and direction of such interactions from parasitological data. Due to logistical constraints, perturbation experiments are rarely undertaken to directly detect interactions, therefore a variety of approaches are commonly used to infer them from patterns of parasite association in observational data. However, the reliability of these various approaches is not known. We assess the ability of a range of standard analytical approaches to detect known interactions between infections of nematodes and intestinal coccidia (Eimeria) in natural small-mammal populations, as revealed by experimental perturbations. We show that correlation-based approaches are highly unreliable, often predicting strong and highly significant associations between nematodes and Eimeria in the opposite direction to the underlying interaction. The most reliable methods involved longitudinal analyses, in which the nematode infection status of individuals at one month is related to the infection status by Eimeria the next month. Even then, however, we suggest these approaches are only viable for certain types of infections and datasets. Overall we suggest that, in the absence of experimental approaches, careful consideration be given to the choice of statistical approach when attempting to infer interspecific interactions from observational data.