New computational approaches for analysis of cis-regulatory networks

The investigation and modeling of gene regulatory networks requires computational tools specific to the task. We present several locally developed software tools that have been used in support of our ongoing research into the embryogenesis of the sea urchin. These tools are especially well suited to iterative refinement of models through experimental and computational investigation. They include: BioArray, a macroarray spot processing program; SUGAR, a system to display and correlate large-BAC sequence analyses; SeqComp and FamilyRelations, programs for comparative sequence analysis; and NetBuilder, an environment for creating and analyzing models of gene networks. We also present an overview of the process used to build our model of the Strongylocentrotus purpuratus endomesoderm gene network. Several of the tools discussed in this paper are still in active development and some are available as open source.     

Some approaches for measuring and modelling riparian shade

The radiation environment of streams is of major ecological importance because it controls stream thermal regime and light availability for photosynthesis. Therefore, methods are needed for measuring stream shade in practical riparian management. The quantity ‘diffuse non-interceptance’ (difn), defined as the proportion of incident lighting received under a sky of uniform brightness and best estimated from fish-eye images, is useful for general specification of light exposure. For routine measurement of difn along stream reaches we recommend using a matched pair of simple light sensors (e.g. photosynthetically available radiation sensors) under conditions of complete overcast (which has almost uniform brightness). Methods are also needed for predicting future light exposure as riparian plantings grow and increasingly shade the stream. A simple model is outlined for predicting difn at the channel centre as a function of channel dimensions (stream width, w) and riparian plant character (foliage density, canopy height, h). The model reproduces the broad empirical trend of increasing shade with increasing h/w ratio. Future model refinement will aim to quantify the increase in shade moving from channel centre to edge under an overhanging canopy.     

Dynamic energy budget approaches for modelling organismal ageing

Ageing is a complex multifactorial process involving a progressive physiological decline that, ultimately, leads to the death of an organism. It involves multiple changes in many components that play fundamental roles under healthy and pathological conditions. Simultaneously, every organism undergoes accumulative 'wear and tear' during its lifespan, which confounds the effects of the ageing process. The scenario is complicated even further by the presence of both age-dependent and age-independent competing causes of death. Various manipulations have been shown to interfere with the ageing process. Calorie restriction, for example, has been reported to increase the lifespan of a wide range of organisms, which suggests a strong relation between energy metabolism and ageing. Such a link is also supported within the main theories for ageing: the free radical hypothesis, for instance, links oxidative damage production directly to energy metabolism. The Dynamic Energy Budgets (DEB) theory, which characterizes the uptake and use of energy by living organisms, therefore constitutes a useful tool for gaining insight into the ageing process. Here we compare the existing DEB-based modelling approaches and, then, discuss how new biological evidence could be incorporated within a DEB framework.     

Simultaneous modelling of distributional patterns in a guild of eastern-Australian cicadas

Summary The distributions, with respect to habitat structure, of nine species of eastern-Australian cicadas have been shown to be non-random. The most striking consequence of this non-randomness is a marked inverse relationship between habitat breadth and habitat position (terms defined in text). Eight basic models and 12 derived models were used in conjunction with a canonical space to try to account for the ways in which the species of cicadas were distributed with respect to habitat. Several models produced results that were in reasonable agreement with the observed data. The most parsimonious of these corresponds to analytical results of other workers, such as Diamond's (1975) incidence curves, occurrence sequences (Schoener and Schoener 1983), and probability functions (Adler and Wilson 1985). The distributions of cicadas can be modelled by assuming that the species occupy sites independently of one another. These species of cicadas are unlikely to engage in interspecific competition, which is consistent with independence of distributions.     

New approaches for the analysis of Arabidopsis thaliana small RNAs

We are interested in the study of small RNAs (sRNAs) that over-accumulate during the plant hypersensitive response (HR), a form of programmed cell death that occurs in and around the site of infection when plants are challenged by pathogens. For this purpose, we have constructed, by subtractive hybridization, a cDNA library of Arabidopsis sRNAs that are enriched during HR. Sequencing of randomly chosen clones provided evidence for the specific accumulation of several microRNAs as well as previously unidentified sRNAs. In a second approach, we have tested the possibility to hybridize labelled cDNAs derived from sRNAs to a DNA tiling array. We could reproducibly hybridize a custom-made tiling array covering Arabidopsis chromosome 4, with small cDNAs as targets. Furthermore, we have found that the distribution of hybridized fragments with sRNAs extracted from control leaves is in good agreement with the abundance of Arabidopsis sRNAs that correspond to this chromosome as determined by massive parallel sequence signature (MPSS).     

New molecular approaches to tissue analysis.

The completion of the Human Genome Project will produce new opportunities for analysis of genes and their products in human tissue. The emergence of new technologies will enable investigators to directly examine human tissues for gene deletion, transposition, and amplification. In addition, we will be able to assess the complete gene expression of a tissue by examining the mRNA species using microarray chips. The emerging technologies of laser capture microdissection and RNA amplification enables these procedures to be carried out on groups of a few hundred cells, which will facilitate the examination of heterogeneous lesions. Finally, the application of tissue arrays and the capability of obtaining protein sequences in samples of only a few femtomoles of protein using desorption mass spectroscopy will revolutionize the analysis of protein expression.     

Benchmarking novel approaches for modelling species range dynamics

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process‐based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process‐based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.     

Comparing different ODE modelling approaches for gene regulatory networks

A fundamental step in synthetic biology and systems biology is to derive appropriate mathematical models for the purposes of analysis and design. For example, to synthesize a gene regulatory network, the derivation of a mathematical model is important in order to carry out in silico investigations of the network dynamics and to investigate parameter variations and robustness issues. Different mathematical frameworks have been proposed to derive such models. In particular, the use of sets of nonlinear ordinary differential equations (ODEs) has been proposed to model the dynamics of the concentrations of mRNAs and proteins. These models are usually characterized by the presence of highly nonlinear Hill function terms. A typical simplification is to reduce the number of equations by means of a quasi-steady-state assumption on the mRNA concentrations. This yields a class of simplified ODE models. A radically different approach is to replace the Hill functions by piecewise-linear approximations [Casey, R., de Jong, H., Gouz, J.-L., 2006. Piecewise-linear models of genetic regulatory networks: equilibria and their stability. J. Math. Biol. 52 (1), 27-56]. A further modelling approach is the use of discrete-time maps [Coutinho, R., Fernandez, B., Lima, R., Meyroneinc, A., 2006. Discrete time piecewise affine models of genetic regulatory networks. J. Math. Biol. 52, 524-570] where the evolution of the system is modelled in discrete, rather than continuous, time. The aim of this paper is to discuss and compare these different modelling approaches, using a representative gene regulatory network. We will show that different models often lead to conflicting conclusions concerning the existence and stability of equilibria and stable oscillatory behaviours. Moreover, we shall discuss, where possible, the viability of making certain modelling approximations (e.g. quasi-steady-state mRNA dynamics or piecewise-linear approximations of Hill functions) and their effects on the overall system dynamics.     

Inductive learning approaches to rainfall-runoff modelling

Trying to model the rainfall-runoff process is a complex activity as it is influenced by a number of implicit and explicit factors--for example, precipitation distribution, evaporation, transpiration, abstraction, watershed topography, and soil types. However, this kind of forecasting is particularly important as it is used to predict serious flooding, estimate erosion and identify problems associated with low flow. Inductive learning approaches (e.g. decision trees and artificial neural networks) are particularly well suited to problems of this nature as they can often interpret underlying factors (such as seasonal variations) which cannot be modelled by other techniques. In addition, these approaches can easily be trained on the explicit factors (e.g. rainfall) and the inexplicit factors (e.g. abstraction) that affect river flow. Inductive learning approaches can also be extended to account for new factors that emerge over a period of time. This paper evaluates the application of decision trees and two artificial neural network models (the multilayer perceptron and the radial basis function network) to river flow forecasting in two flood prone UK catchments using real hydrometric data. Comparisons are made between the performance of these approaches and conventional flood forecasting systems.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling

Statistical modelling of biological survey data in relation to remotely mapped environmental variables is a powerful technique for making more effective use of sparse data in regional conservation planning. Application of such modelling to planning in the northeast New South Wales (NSW) region of Australia represents one of the most extensive and longest running case studies of this approach anywhere in the world. Since the early 1980s, statistical modelling has been used to extrapolate distributions of over 2300 species of plants and animals, and a wide variety of higher-level communities and assemblages. These modelled distributions have played a pivotal role in a series of major land-use planning processes, culminating in extensive additions to the region's protected area system. This paper provides an overview of the analytical methodology used to model distributions of individual species in northeast NSW, including approaches to: (1) developing a basic integrated statistical and geographical information system (GIS) framework to facilitate automated fitting and extrapolation of species models; (2) extending this basic approach to incorporate consideration of spatial autocorrelation, land-cover mapping and expert knowledge; and (3) evaluating the performance of species modelling, both in terms of predictive accuracy and in terms of the effectiveness with which such models function as general surrogates for biodiversity.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling

Regional conservation planning can often make more effective use of sparse biological data by linking these data to remotely mapped environmental variables through statistical modelling. While modelling distributions of individual species is the best known and most widely used approach to such modelling, there are many situations in which more information can be extracted from available data by supplementing, or replacing, species-level modelling with modelling of communities or assemblages. This paper provides an overview of approaches to community-level modelling employed in a series of major land-use planning processes in the northeast New South Wales region of Australia, and evaluates how well communities and assemblages derived using these techniques function as surrogates in regional conservation planning. We also outline three new directions that may enhance the effectiveness of community-level modelling by: (1) more closely integrating modelling with traditional ecological mapping (e.g. vegetation mapping); (2) more tightly linking numerical classification and spatial modelling through application of canonical classification techniques; and (3) enhancing the applicability of modelling to data-poor regions through employment of a new technique for modelling spatial pattern in compositional dissimilarity.     

Using crisp and fuzzy modelling to identify favourability hotspots useful to perform gap analysis

In this study, we propose the use of a favourability function to perform Gap Analysis. To exemplify this, we modelled the distribution of terrestrial mammal species in Andalusia (South of Spain) on the basis of their presence/absence on a grid of 10 km × 10 km UTM cells (n = 961). Using logistic regression and 30 variables related with the environment, space and human influence, we obtained probabilities of occurrence for each species in each cell. We computed a crisp favourability index considering the areas as favourable or unfavourable for a species if the probability of occurrence was higher or lower than the species prevalence, respectively. We also used a favourability function and fuzzy logic to level all species to the same threshold of favourability, which allowed to compare and to combine species distributions. Adding up the fuzzy favourability values for each species in each cell we obtained a fuzzy favourability index that we compared with species richness (sum of species in each cell) and with the crisp favourability index. We performed Gap Analysis by overlapping these results with the current reserve network of Andalusia. Gaps were grouped in fewer and bigger zones after applying the favourability indices. Considerations and recommendations for the use of the favourability function to select areas of conservation interest are discussed.     

A comparison of statistical approaches for modelling fish species distributions

SUMMARY 1. The prediction of species distributions is of primary importance in ecology and conservation biology. Statistical models play an important role in this regard; however, researchers have little guidance when choosing between competing methodologies because few comparative studies have been conducted. 2. We provide a comprehensive comparison of traditional and alternative techniques for predicting species distributions using logistic regression analysis, linear discriminant analysis, classification trees and artificial neural networks to model: (1) the presence/absence of 27 fish species as a function of habitat conditions in 286 temperate lakes located in south‐central Ontario, Canada and (2) simulated data sets exhibiting deterministic, linear and non‐linear species response curves. 3. Detailed evaluation of model predictive power showed that approaches produced species models that differed in overall correct classification, specificity (i.e. ability to correctly predict species absence) and sensitivity (i.e. ability to correctly predict speciespresence) and in terms of which of the study lakes they correctly classified. Onaverage, neural networks outperformed the other modelling approaches, although all approaches predicted species presence/absence with moderate to excellent success. 4. Based on simulated non‐linear data, classification trees and neural networks greatly outperformed traditional approaches, whereas all approaches exhibited similar correct classification rates when modelling simulated linear data. 5. Detailed evaluation of model explanatory insight showed that the relative importance of the habitat variables in the species models varied among the approaches, where habitat variable importance was similar among approaches for some species and very different for others. 6. In general, differences in predictive power (both correct classification rate and identity of the lakes correctly classified) among the approaches corresponded with differences in habitat variable importance, suggesting that non‐linear modelling approaches (i.e. classification trees and neural networks) are better able to capture and model complex, non‐linear patterns found in ecological data. The results from the comparisons using simulated data further support this notion. 7. By employing parallel modelling approaches with the same set of data and focusing on comparing multiple metrics of predictive performance, researchers can begin to choose predictive models that not only provide the greatest predictive power, but also best fit the proposed application.