Creating, Modeling, and Visualizing Metabolic Networks

Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and create due to the diverse types of information that need to be represented. This chapter describes a suite of interlinked tools for developing, displaying, and modeling metabolic networks. The metabolic network interactions database, MetNetDB, contains information on regulatory and metabolic interactions derived from a combination of web databases and input from biologists in their area of expertise. PathBinderA mines the biological “literaturome” by searching for new interactions or supporting evidence for existing interactions in metabolic networks. Sentences from abstracts are ranked in terms of the likelihood that an interaction is described and combined with evidence provided by other sentences. FCModeler, a publicly available software package, enables the biologist to visualize and model metabolic and regulatory network maps. FCModeler aids in the development and evaluation of hypotheses, and provides a modeling framework for assessing the large amounts of data captured by high-throughput gene expression experiments

Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments

Microarray technology has become widespread as a means to investigate gene function and metabolic pathways in an organism. A common experiment involves probing, at each of several time points, the gene expression of experimental units subjected to different treatments. Due to the high cost of microarrays, such experiments may be performed without replication and therefore provide a gene expression measurement of only one experimental unit for each combination of treatment and time point. Though an experiment with replication would provide more powerful conclusions, it is still possible to identify differentially expressed genes and to estimate the number of false positives for a specified rejection region when the data is unreplicated. We present a method for identifying differentially expressed genes in this situation that utilizes polynomial regression models to approximate underlying expression patterns. In the first stage of a two-stage permutation approach, we choose a ‘best’ model at each gene after considering all possible regression models involving treatment effects, terms polynomial in time, and interactions between treatments and polynomial terms. In the second stage, we identify genes whose ‘best’ model differs significantly from the overall mean model as differentially expressed. The number of expected false positives in the chosen rejection region and the overall proportion of differentially expressed genes are both estimated using a method presented by Storey and Tibshirani (2003). For illustration, the proposed method is applied to an Arabidopsis thaliana microarray data set

A novel approach to assessing the ecosystem-wide impacts of reintroductions

Reintroducing a species to an ecosystem can have significant impacts on the recipient ecological community. Although reintroductions can have striking and positive outcomes, they also carry risks; many well intentioned conservation actions have had surprising and unsatisfactory outcomes. A range of network-based mathematical methods have been developed to make quantitative predictions of how communities will respond to management interventions. These methods are based on the limited knowledge of which species interact with each other and in what way. However, expert knowledge isn’t perfect and can only take models so far. Fortunately, other types of data, such as abundance time-series, is often available, but, to date, no quantitative method exists to integrate these various data types into these models, allowing more precise ecosystem-wide predictions. In this paper, we develop mathematical methods that combine time-series data of multiple species with knowledge of species interactions and we apply it to proposed reintroductions at Booderee National Park in Australia. There have been large fluctuations in species abundances at Booderee National Park in recent history, following intense feral fox (Vulpes vulpes) control – including the local extinction of the greater glider (Petauroides volans). These fluctuations can provide information about the system isn’t readily obtained from a stable system, and we use them to inform models that we then use to predict potential outcomes of eastern quoll (Dasyurus viverrinus) and long-nosed potoroo (Potorous tridactylus) reintroductions. One of the key species of conservation concern in the park is the eastern bristlebird (Dasyornis brachypterus), and we find that long-nosed potoroo introduction would have very little impact on the eastern bristlebird population, while the eastern quoll introduction increased the likelihood of eastern bristlebird decline, although that depends on the strength and form of any possible interaction.We thank the ARC Centre of Excellence for Environmental Decisions, The National Environmental Research Project Decisions Hub and an ARC Linkage Project (LP160100496) for funding. CB is the recipient of a John Stocker Postdoctoral Fellowship from the Science and Industry Endowment Fund. MB is supported by an ARC Future Fellowship (FT170100274). EMM is a current ARC Future Fellowship (FT170100140) and was supported by an ARC DECRA Fellowship for the majority of this work

Effects of fluid and drinking on pneumonia mortality in older adults: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Advice to drink plenty of fluid is common in respiratory infections. We assessed whether low fluid intake (dehydration) altered outcomes in adults with pneumonia. METHODS: We systematically reviewed trials increasing fluid intake and well-adjusted, well-powered observational studies assessing associations between markers of low-intake dehydration (fluid intake, serum osmolality, urea or blood urea nitrogen, urinary output, signs of dehydration) and mortality in adult pneumonia patients (with any type of pneumonia, including community acquired, health-care acquired, aspiration, COVID-19 and mixed types). Medline, Embase, CENTRAL, references of reviews and included studies were searched to 30/10/2020. Studies were assessed for inclusion, risk of bias and data extracted independently in duplicate. We employed random-effects meta-analysis, sensitivity analyses, subgrouping and GRADE assessment. Prospero registration: CRD42020182599. RESULTS: We identified one trial, 20 well-adjusted cohort studies and one case-control study. None suggested that more fluid (hydration) was associated with harm. Ten of 13 well-powered observational studies found statistically significant positive associations in adjusted analyses between dehydration and medium-term mortality. The other three studies found no significant effect. Meta-analysis suggested doubled odds of medium-term mortality in dehydrated (compared to hydrated) pneumonia patients (GRADE moderate-quality evidence, OR 2.3, 95% CI 1.8 to 2.8, 8619 deaths in 128,319 participants). Heterogeneity was explained by a dose effect (greater dehydration increased risk of mortality further), and the effect was consistent across types of pneumonia (including community-acquired, hospital-acquired, aspiration, nursing and health-care associated, and mixed pneumonia), age and setting (community or hospital). The single trial found that educating pneumonia patients to drink ≥1.5 L fluid/d alongside lifestyle advice increased fluid intake and reduced subsequent healthcare use. No studies in COVID-19 pneumonia met the inclusion criteria, but 70% of those hospitalised with COVID-19 have pneumonia. Smaller COVID-19 studies suggested that hydration is as important in COVID-19 pneumonia mortality as in other pneumonias. CONCLUSIONS: We found consistent moderate-quality evidence mainly from observational studies that improving hydration reduces the risk of medium-term mortality in all types of pneumonia. It is remarkable that while many studies included dehydration as a potential confounder, and major pneumonia risk scores include measures of hydration, optimal fluid volume and the effect of supporting hydration have not been assessed in randomised controlled trials of people with pneumonia. Such trials, are needed as potential benefits may be large, rapid and implemented at low cost. Supporting hydration and reversing dehydration has the potential to have rapid positive impacts on pneumonia outcomes, and perhaps also COVID-19 pneumonia outcomes, in older adults

MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis

MetNet (http://www.botany.iastate.edu/∼mash/metnetex/metabolicnetex.html) is publicly available software in development for analysis of genome-wide RNA, protein and metabolite profiling data. The software is designed to enable the biologist to visualize, statistically analyse and model a metabolic and regulatory network map of Arabidopsis, combined with gene expression profiling data. It contains a JAVA interface to an interactions database (MetNetDB) containing information on regulatory and metabolic interactions derived from a combination of web databases (TAIR, KEGG, BRENDA) and input from biologists in their area of expertise. FCModeler captures input from MetNetDB in a graphical form. Sub-networks can be identified and interpreted using simple fuzzy cognitive maps. FCModeler is intended to develop and evaluate hypotheses, and provide a modelling framework for assessing the large amounts of data captured by high-throughput gene expression experiments. FCModeler and MetNetDB are currently being extended to three-dimensional virtual reality display. The MetNet map, together with gene expression data, can be viewed using multivariate graphics tools in GGobi linked with the data analytic tools in R. Users can highlight different parts of the metabolic network and see the relevant expression data highlighted in other data plots. Multi-dimensional expression data can be rotated through different dimensions. Statistical analysis can be computed alongside the visual. MetNet is designed to provide a framework for the formulation of testable hypotheses regarding the function of specific genes, and in the long term provide the basis for identification of metabolic and regulatory networks that control plant composition and development