Stochastic simulation of chemical reactions in spatially complex media

Discrete stochastic simulations, via techniques such as the Stochastic Simulation Algorithm (SSA) are a powerful tool for understanding the dynamics of chemical kinetics when there are low numbers of certain molecular species. However, an important constraint is the assumption of well-mixedness and homogeneity. In this paper, we show how to use Monte Carlo simulations to estimate an anomalous diffusion parameter that encapsulates the crowdedness of the spatial environment. We then use this parameter to replace the rate constants of bimolecular reactions by a time-dependent power law to produce an SSA valid in cases where anomalous diffusion occurs or the system is not well-mixed (ASSA). Simulations then show that ASSA can successfully predict the temporal dynamics of chemical kinetics in a spatially constrained environment.

Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns

Almost all metapopulation modelling assumes that connectivity between patches is only a function of distance, and is therefore symmetric. However, connectivity will not depend only on the distance between the patches, as some paths are easy to traverse, while others are difficult. When colonising organisms interact with the heterogeneous landscape between patches, connectivity patterns will invariably be asymmetric. There have been few attempts to theoretically assess the effects of asymmetric connectivity patterns on the dynamics of metapopulations. In this paper, we use the framework of complex networks to investigate whether metapopulation dynamics can be determined by directly analysing the asymmetric connectivity patterns that link the patches. Our analyses focus on “patch occupancy” metapopulation models, which only consider whether a patch is occupied or not. We propose three easily calculated network metrics: the “asymmetry” and “average path strength” of the connectivity pattern, and the “centrality” of each patch. Together, these metrics can be used to predict the length of time a metapopulation is expected to persist, and the relative contribution of each patch to a metapopulation’s viability. Our results clearly demonstrate the negative effect that asymmetry has on metapopulation persistence. Complex network analyses represent a useful new tool for understanding the dynamics of species existing in fragmented landscapes, particularly those existing in large metapopulations.

Systematics of the Ustilago-Sporisorium-Macalpinomyces complex of smut fungi

Smut fungi are important pathogens of grasses, including the cultivated crops maize, sorghum and sugarcane. Typically, smut fungi infect the inflorescence of their host plants. Three genera of smut fungi (Ustilago, Sporisorium and Macalpinomyces) form a complex with overlapping morphological characters, making species placement problematic. For example, the newly described Macalpinomyces mackinlayi possesses a combination of morphological characters such that it cannot be unambiguously accommodated in any of the three genera. Previous attempts to define Ustilago, Sporisorium and Macalpinomyces using morphology and molecular phylogenetics have highlighted the polyphyletic nature of the genera, but have failed to produce a satisfactory taxonomic resolution. A detailed systematic study of 137 smut species in the Ustilago-Sporisorium- Macalpinomyces complex was completed in the current work. Morphological and DNA sequence data from five loci were assessed with maximum likelihood and Bayesian inference to reconstruct a phylogeny of the complex. The phylogenetic hypotheses generated were used to identify morphological synapomorphies, some of which had previously been dismissed as a useful way to delimit the complex. These synapomorphic characters are the basis for a revised taxonomic classification of the Ustilago-Sporisorium-Macalpinomyces complex, which takes into account their morphological diversity and coevolution with their grass hosts. The new classification is based on a redescription of the type genus Sporisorium, and the establishment of four genera, described from newly recognised monophyletic groups, to accommodate species expelled from Sporisorium. Over 150 taxonomic combinations have been proposed as an outcome of this investigation, which makes a rigorous and objective contribution to the fungal systematics of these important plant pathogens.

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

A new geological model of Cenozoic New Zealand suggests a complex phylogeographic history for New Zealand terrestrial taxa

The new model of North Island Cenozoic palaeogeography developed by Kamp et al. has a range of important implications for the evolution of New Zealand terrestrial taxa over the past 30 Ma. Key aspects include the prolonged isolation of the biota on the North Island landmass from the larger and more diverse greater South Island, and the founding of North Island taxa from the potentially unusual ecosystem of a small island around Northland. The prolonged period of isolation is expected to have generated deep phylogenetic splits within taxa present on both islands, and an important current aim should be to identify such signals in surviving endemics to start building a picture of the historical phylogeography, and inferred ecology of both islands through the Cenozoic. Given the potential differences in founding terrestrial species and climatic conditions, it seems likely that the ecology may have been very diferent between the North and South Islands. New genetic data from the 10 or so species of extinct moa suggest that the radiation of moa was much more recent than previously suggested, and reveals a complex pattern that is inferred to result from the interplay of the Cenozoic biogeography, marine barriers, and glacial cycles.

Sampling environmental acoustic recordings to determine species richness : I

Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.

Maize streak virus: An old and complex 'emerging' pathogen

Maize streak virus (MSV; Genus Mastrevirus, Family Geminiviridae) occurs throughout Africa, where it causes what is probably the most serious viral crop disease on the continent. It is obligately transmitted by as many as six leafhopper species in the Genus Cicadulina, but mainly by C. mbila Naudé and C. storeyi. In addition to maize, it can infect over 80 other species in the Family Poaceae. Whereas 11 strains of MSV are currently known, only the MSV-A strain is known to cause economically significant streak disease in maize. Severe maize streak disease (MSD) manifests as pronounced, continuous parallel chlorotic streaks on leaves, with severe stunting of the affected plant and, usuallly, a failure to produce complete cobs or seed. Natural resistance to MSV in maize, and/or maize infections caused by non-maize-adapted MSV strains, can result in narrow, interrupted streaks and no obvious yield losses. MSV epidemiology is primarily governed by environmental influences on its vector species, resulting in erratic epidemics every 3-10 years. Even in epidemic years, disease incidences can vary from a few infected plants per field, with little associated yield loss, to 100% infection rates and complete yield loss. Taxonomy: The only virus species known to cause MSD is MSV, the type member of the Genus Mastrevirus in the Family Geminiviridae. In addition to the MSV-A strain, which causes the most severe form of streak disease in maize, 10 other MSV strains (MSV-B to MSV-K) are known to infect barley, wheat, oats, rye, sugarcane, millet and many wild, mostly annual, grass species. Seven other mastrevirus species, many with host and geographical ranges partially overlapping those of MSV, appear to infect primarily perennial grasses. Physical properties: MSV and all related grass mastreviruses have single-component, circular, single-stranded DNA genomes of approximately 2700 bases, encapsidated in 22 × 38-nm geminate particles comprising two incomplete T = 1 icosahedra, with 22 pentameric capsomers composed of a single 32-kDa capsid protein. Particles are generally stable in buffers of pH 4-8. Disease symptoms: In infected maize plants, streak disease initially manifests as minute, pale, circular spots on the lowest exposed portion of the youngest leaves. The only leaves that develop symptoms are those formed after infection, with older leaves remaining healthy. As the disease progresses, newer leaves emerge containing streaks up to several millimetres in length along the leaf veins, with primary veins being less affected than secondary or tertiary veins. The streaks are often fused laterally, appearing as narrow, broken, chlorotic stripes, which may extend over the entire length of severely affected leaves. Lesion colour generally varies from white to yellow, with some virus strains causing red pigmentation on maize leaves and abnormal shoot and flower bunching in grasses. Reduced photosynthesis and increased respiration usually lead to a reduction in leaf length and plant height; thus, maize plants infected at an early stage become severely stunted, producing undersized, misshapen cobs or giving no yield at all. Yield loss in susceptible maize is directly related to the time of infection: Infected seedlings produce no yield or are killed, whereas plants infected at later times are proportionately less affected. Disease control: Disease avoidance can be practised by only planting maize during the early season when viral inoculum loads are lowest. Leafhopper vectors can also be controlled with insecticides such as carbofuran. However, the development and use of streak-resistant cultivars is probably the most effective and economically viable means of preventing streak epidemics. Naturally occurring tolerance to MSV (meaning that, although plants become systemically infected, they do not suffer serious yield losses) has been found, which has primarily been attributed to a single gene, msv-1. However, other MSV resistance genes also exist and improved resistance has been achieved by concentrating these within individual maiz genotypes. Whereas true MSV immunity (meaning that plants cannot be symptomatically infected by the virus) has been achieved in lines that include multiple small-effect resistance genes together with msv-1, it has proven difficult to transfer this immunity into commercial maize genotypes. An alternative resistance strategy using genetic engineering is currently being investigated in South Africa. Useful websites: 〈http://www.mcb.uct.ac.za/MSV/mastrevirus.htm〉; 〈http://www. danforthcenter.org/iltab/geminiviridae/geminiaccess/mastrevirus/Mastrevirus. htm〉. © 2009 Blackwell Publishing Ltd.

A review of the Ustilago-Sporisorium-Macalpinomyces complex

The fungal genera Ustilago, Sporisorium and Macalpinomyces represent an unresolved complex. Taxa within the complex often possess characters that occur in more than one genus, creating uncertainty for species placement. Previous studies have indicated that the genera cannot be separated by morphology alone. Here we chronologically review the history of the Ustilago-Sporisorium-Macalpinomyces complex, argue for its resolution and suggest methods to accomplish a stable taxonomy. A combined molecular and morphological approach is required to identify synapomorphic characters that underpin a new classification. Ustilago, Sporisorium and Macalpinomyces require explicit re-description and new genera, based on monophyletic groups, are needed to accommodate taxa that no longer fit the emended descriptions. A resolved classification will end the taxonomic confusion that surrounds generic placement of these smut fungi.

Incorporation of exogenous RGD peptide and inter-species blending as strategies for enhancing human corneal limbal epithelial cell growth on Bombyx mori silk fibroin membranes

While fibroin isolated from the cocoons of domesticated silkworm Bombyx mori supports growth of human corneal limbal epithelial (HLE) cells, the mechanism of cell attachment remains unclear. In the present study we sought to enhance the attachment of HLE cells to membranes of Bombyx mori silk fibroin (BMSF) through surface functionalization with an arginine-glycine-aspartic acid (RGD)-containing peptide. Moreover, we have examined the response of HLE cells to BMSF when blended with the fibroin produced by a wild silkworm, Antheraea pernyi, which is known to contain RGD sequences within its primary structure. A procedure to isolate A. pernyi silk fibroin (APSF) from the cocoons was established, and blends of the two fibroins were prepared at five different BMSF/APSF ratios. In another experiment, BMSF surface was modified by binding chemically the GRGDSPC peptide using a water-soluble carbodiimide. Primary HLE were grown in the absence of serum on membranes made of BMSF, APSF, and their blends, as well as on RGD-modified BMSF. There was no statistically significant enhancing effect on the cell attachment due to the RGD presence. This suggests that the adhesion through RGD ligands may have a complex mechanism, and the investigated strategies are of limited value unless the factors contributing to this mechanism become better known.

Effects of soluble dietary cellulose on specific growth rate, survival and digestive enzyme activities in three freshwater crayfish (Cherax) species

Three native freshwater crayfish Cherax species are farmed in Australia namely; Redclaw (Cherax quadricarinatus), Marron (C. tenuimanus), and Yabby (C. destructor). Lack of appropriate data on specific nutrient requirements for each of these species, however, has constrained development of specific formulated diets and hence current use of over-formulated feeds or expensive marine shrimp feeds, limit their profitability. A number of studies have investigated nutritional requirements in redclaw that have focused on replacing expensive fish meal in formulated feeds with non-protein, less expensive substitutes including plant based ingredients. Confirmation that freshwater crayfish possess endogenous cellulase genes, suggests their potential ability to utilize complex carbohydrates like cellulose as nutrient sources in their diet. To date, studies have been limited to only C. quadricarinatus and C. destructor and no studies have compared the relative ability of each species to utilize soluble cellulose in their diets. Individual feeding trials of late-juveniles of each species were conducted separately in an automated recirculating culture system over 12 week cycles. Animals were fed either a test diet (TD) that contained 20% soluble cellulose or a reference diet (RD) substituted with the same amount of corn starch. Water temperature, conductivity and pH were maintained at constant and optimum levels for each species. Animals were fed at 3% of their body weight twice daily and wet body weight was recorded bi-weekly. At the end of experiment, all animals were harvested, measured and midgut gland extracts assayed for alpha-amylase, total protease and cellulase activity levels. After the trial period, redclaw fed with RD showed significantly higher (p<0.05) specific growth rate (SGR) compare with animals fed the TD while SGR of marron and yabby fed the two diets were not significantly different (p<0.05). Cellulase expression levels in redclaw were not significantly different between diets. Marron and yabby showed significantly higher cellulase activity when fed the RD. Amylase and protease activity in all three species were significantly higher in the animals fed with RD (Table 1). These results indicate that test animals of all species can utilize starch better than dietary soluble cellulose in their diet and inclusion of 20% soluble cellulose in diets does not appear to have any significant negative effect on their growth rate but survival was impacted in C. quadricarinatus while not in C. tenuimanus or C. destructor.

Genomic evidence for the parallel evolution of coastal forms in the Senecio lautus complex

Instances of parallel ecotypic divergence where adaptation to similar conditions repeatedly cause similar phenotypic changes in closely related organisms are useful for studying the role of ecological selection in speciation. Here we used a combination of traditional and next generation genotyping techniques to test for the parallel divergence of plants from the Senecio lautus complex, a phenotypically variable groundsel that has adapted to disparate environments in the South Pacific. Phylogenetic analysis of a broad selection of Senecio species showed that members of the S. lautus complex form a distinct lineage that has diversified recently in Australasia. An inspection of thousands of polymorphisms in the genome of 27 natural populations from the S. lautus complex in Australia revealed a signal of strong genetic structure independent of habitat and phenotype. Additionally, genetic differentiation between populations was correlated with the geographical distance separating them, and the genetic diversity of populations strongly depended on geographical location. Importantly, coastal forms appeared in several independent phylogenetic clades, a pattern that is consistent with the parallel evolution of these forms. Analyses of the patterns of genomic differentiation between populations further revealed that adjacent populations displayed greater genomic heterogeneity than allopatric populations and are differentiated according to variation in soil composition. These results are consistent with a process of parallel ecotypic divergence in face of gene flow.

Sampling environmental acoustic recordings to determine bird species richness

Acoustic sensors can be used to estimate species richness for vocal species such as birds. They can continuously and passively record large volumes of data over extended periods. These data must subsequently be analyzed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced surveyors can produce accurate results; however the time and effort required to process even small volumes of data can make manual analysis prohibitive. This study examined the use of sampling methods to reduce the cost of analyzing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilizing five days of manually analyzed acoustic sensor data from four sites, we examined a range of sampling frequencies and methods including random, stratified, and biologically informed. We found that randomly selecting 120 one-minute samples from the three hours immediately following dawn over five days of recordings, detected the highest number of species. On average, this method detected 62% of total species from 120 one-minute samples, compared to 34% of total species detected from traditional area search methods. Our results demonstrate that targeted sampling methods can provide an effective means for analyzing large volumes of acoustic sensor data efficiently and accurately. Development of automated and semi-automated techniques is required to assist in analyzing large volumes of acoustic sensor data. Read More: http://www.esajournals.org/doi/abs/10.1890/12-2088.1

Timed Probabilistic Automaton : a bridge between Raven and Song Scope for automatic species recognition

Raven and Song Scope are two automated sound anal-ysis tools based on machine learning technique for en-vironmental monitoring. Many research works have been conducted upon them, however, no or rare explo-ration mentions about the performance and comparison between them. This paper investigates the comparisons from six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential application. Through deep exploration one critical gap is identified that there is a lack of approach to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables first and clusters them into complex structures after.

Drosophila 3′ UTRs are more complex than protein-coding sequences

The 3′ UTRs of eukaryotic genes participate in a variety of post-transcriptional (and some transcriptional) regulatory interactions. Some of these interactions are well characterised, but an undetermined number remain to be discovered. While some regulatory sequences in 3′ UTRs may be conserved over long evolutionary time scales, others may have only ephemeral functional significance as regulatory profiles respond to changing selective pressures. Here we propose a sensitive segmentation methodology for investigating patterns of composition and conservation in 3′ UTRs based on comparison of closely related species. We describe encodings of pairwise and three-way alignments integrating information about conservation, GC content and transition/transversion ratios and apply the method to three closely related Drosophila species: D. melanogaster, D. simulans and D. yakuba. Incorporating multiple data types greatly increased the number of segment classes identified compared to similar methods based on conservation or GC content alone. We propose that the number of segments and number of types of segment identified by the method can be used as proxies for functional complexity. Our main finding is that the number of segments and segment classes identified in 3′ UTRs is greater than in the same length of protein-coding sequence, suggesting greater functional complexity in 3′ UTRs. There is thus a need for sustained and extensive efforts by bioinformaticians to delineate functional elements in this important genomic fraction. C code, data and results are available upon request.