Patterns of coexistence of two species of freshwater turtles are affected by spatial scale

Inferring biotic interactions from the examination of patterns of species occurrences has been a central tenet in community ecology, and it has recently gained interest in the context of single-species distribution modelling. However, understanding of how spatial extent and grain size affect such inferences remains elusive. For example, would inferences of biotic interactions from broad-scale patterns of coexistence provide a surrogate for patterns at finer spatial scales? In this paper we examine how the spatial and environmental association between two closely related species of freshwater turtles in the Iberian Peninsula is affected by the geographical extent and resolution of the analysis. Species coexistence was compared across spatial scales using five datasets at varying spatial extents and resolutions. Both similarities in the two species’ use of space and in their responses to environmental variables were explored by means of regression analyses. We show that a positive association between the two species measured at broader scales can switch to a negative association at finer scales. We demonstrate that without examination of the effects of spatial scale when investigating biotic interactions using co-occurrence patterns observed at coarse resolutions, conclusions can be deeply misleading.     

Biomolecular network motif counting and discovery by color coding

Protein-protein interaction (PPI) networks of many organisms share global topological features such as degree distribution, k-hop reachability, betweenness and closeness. Yet, some of these networks can differ significantly from the others in terms of local structures: e.g. the number of specific network motifs can vary significantly among PPI networks. Counting the number of network motifs provides a major challenge to compare biomolecular networks. Recently developed algorithms have been able to count the number of induced occurrences of subgraphs with k < or = 7 vertices. Yet no practical algorithm exists for counting non-induced occurrences, or counting subgraphs with k > or = 8 vertices. Counting non-induced occurrences of network motifs is not only challenging but also quite desirable as available PPI networks include several false interactions and miss many others. In this article, we show how to apply the 'color coding' technique for counting non-induced occurrences of subgraph topologies in the form of trees and bounded treewidth subgraphs. Our algorithm can count all occurrences of motif G' with k vertices in a network G with n vertices in time polynomial with n, provided k = O(log n). We use our algorithm to obtain 'treelet' distributions for k < or = 10 of available PPI networks of unicellular organisms (Saccharomyces cerevisiae Escherichia coli and Helicobacter Pyloris), which are all quite similar, and a multicellular organism (Caenorhabditis elegans) which is significantly different. Furthermore, the treelet distribution of the unicellular organisms are similar to that obtained by the 'duplication model' but are quite different from that of the 'preferential attachment model'. The treelet distribution is robust w.r.t. sparsification with bait/edge coverage of 70% but differences can be observed when bait/edge coverage drops to 50%.     

A new approach to the design of a sequence with the highest affinity for a molecular surface.

We describe an algorithm to design the primary structures for peptides which must have the strongest binding to a given molecular surface. This problem cannot be solved by a direct combinatorial sorting, because of an enormous number of possible primary and spatial structures. The approach to solve this problem is to describe a state of each residue by two variables: (i) amino acid type and (ii) 3-D coordinate, and to minimize binding energy over all these variables simultaneously. For short chains which have no long-range interactions within themselves, this minimization can be done easily and efficiently by dynamic programming. We also discuss the problem of how to estimate specificity of binding and how to deduce a sequence with maximal specificity for a given surface. We show that this sequence can be deduced by the same algorithm after some modification of energetic parameters.     

Probabilistic expert systems for DNA mixture profiling

We show how probabilistic expert systems can be used to structure and solve complex cases of forensic identification involving DNA traces that might be mixtures of several DNA profiles. In particular, this approach can readily handle cases where the number of contributors to the mixture cannot be regarded as known in advance. The flexible modularity of the networks used also allows us to handle still more complex cases, for example where the finding of a mixed DNA trace is compounded by such features as missing individuals or the possibility of unobserved alleles.     

Biotic element analysis in biogeography

Biotic element analysis is an alternative to the areas-of-endemism approach for recognizing the presence or absence of vicariance events in a given region. If an ancestral biota was fragmented by vicariance events, biotic elements or clusters of distribution areas should emerge. We propose a statistical test for clustering of distribution areas based on a Monte Carlo simulation with a null model that considers the spatial autocorrelation in the data. The hypothesis tested is that the observed degree of clustering of ranges can be explained by the range size distribution, the varying number of taxa per cell, and the spatial autocorrelation of the occurrences of a taxon alone. A method for the delimitation of biotic elements which uses model-based Gaussian clustering is introduced. We demonstrate our methods and show the importance of grid size by means of a case study, an analysis of the distribution patterns of southern African species of the weevil genus Scobius. The example highlights the difficulties in delimiting areas of endemism if dispersal has occurred and illustrates the advantages of the biotic element approach.     

Modelling and simulation of DNA-mediated self-assembly for superlattice design

ABSTRACT

Functionalisation of colloidal particles with DNA provides a powerful and flexible path towards self-assembly of ordered materials. Given the nearly limitless possibilities for constructing DNA-functionalised particles, and the wide range of conditions under which they can be assembled, it is crucial to gain an understanding of the principles governing self-assembly of these particles and how their properties affect the structures produced. A number of computational models for DNA-functionalised systems have successfully described their properties, and molecular simulation techniques have provided a unique insight into the factors underlying their assembly. Here, we discuss a variety of efforts using simulations to solve an important design problem in DNA-mediated assembly: how the properties of individual DNA-functionalised particles affect their interactions with each other, and ultimately how these interactions determine what structures can be produced.     

Logic in a Dynamic Brain

The ability of the human brain to carry out logical reasoning can be interpreted, in general, as a by-product of adaptive capacities of complex neural networks. Thus, we seek to base abstract logical operations in the general properties of neural networks designed as learning modules. We show that logical operations executable by McCulloch–Pitts binary networks can also be programmed in analog neural networks built with associative memory modules that process inputs as logical gates. These modules can interact among themselves to generate dynamical systems that extend the repertoire of logical operations. We demonstrate how the operations of the exclusive-OR or the implication appear as outputs of these interacting modules. In particular, we provide a model of the exclusive-OR that succeeds in evaluating an odd number of options (the exclusive-OR of classical logic fails in his case), thus paving the way for a more reasonable biological model of this important logical operator. We propose that a brain trained to compute can associate a complex logical operation to an orderly structured but temporary contingent episode by establishing a codified association among memory modules. This explanation offers an interpretation of complex logical processes (eventually learned) as associations of contingent events in memorized episodes. We suggest, as an example, a cognitive model that describes these “logical episodes”.     

Optimal Sequencing Rules for Some Large-Scale Flexible Manufacturing Problems Under the Manhattan and Chebychev Metrics

The purpose of this paper is to develop optimal tool partitioning policies and strip sequencing strategies for a class of flexible manufacturing problems. The problems under consideration involve a large number of operations to be performed by a series of tools on a two-dimensional object. For example, these operations could consist of drilling holes in a metallic sheet. Tools are arranged in a carousel or along a toolbar according to a predetermined sequence. Operations are performed by repeatedly moving the sheet to bring the hole locations under the tool. During each pass, as all operations involving a series of consecutive tools are executed, two main problems are to be solved: (1) how to move the sheet during each pass, (2) how to partition the tools into blocks of consecutive tools. A strip strategy is used to move the sheet. Given this policy, optimal strip widths and tool partitioning policies are determined jointly. Analytical solutions are derived under two metrics corresponding to different operating modes. A numerical example is provided.     

Searching RNA motifs and their intermolecular contacts with constraint networks

MOTIVATION: Searching RNA gene occurrences in genomic sequences is a task whose importance has been renewed by the recent discovery of numerous functional RNA, often interacting with other ligands. Even if several programs exist for RNA motif search, none exists that can represent and solve the problem of searching for occurrences of RNA motifs in interaction with other molecules. RESULTS: We present a constraint network formulation of this problem. RNA are represented as structured motifs that can occur on more than one sequence and which are related together by possible hybridization. The implemented tool MilPat is used to search for several sRNA families in genomic sequences. Results show that MilPat allows to efficiently search for interacting motifs in large genomic sequences and offers a simple and extensible framework to solve such problems. New and known sRNA are identified as H/ACA candidates in Methanocaldococcus jannaschii. AVAILABILITY: http://carlit.toulouse.inra.fr/MilPaT/MilPat.pl.     

Sedimentation of Clusters of Spheres

We describe a numerical method for calculating hydrodynamic interactions between spherical particles efficiently and accurately, both for particles immersed in an infinite liquid and for systems with periodic boundary conditions. Our method is based on a multipole expansion in Cartesian tensors. We then show how to solve the equations of motion for translational and rotational motion of suspended particles at large Peclet numbers. As an example we study the sedimentation of an array of spheres with and without periodic boundary conditions. We also study the effect of perturbations on the stability of the trajectories.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1007/s0089460020227     

Seriation in paleontological data using markov chain Monte Carlo methods

Given a collection of fossil sites with data about the taxa that occur in each site, the task in biochronology is to find good estimates for the ages or ordering of sites. We describe a full probabilistic model for fossil data. The parameters of the model are natural: the ordering of the sites, the origination and extinction times for each taxon, and the probabilities of different types of errors. We show that the posterior distributions of these parameters can be estimated reliably by using Markov chain Monte Carlo techniques. The posterior distributions of the model parameters can be used to answer many different questions about the data, including seriation (finding the best ordering of the sites) and outlier detection. We demonstrate the usefulness of the model and estimation method on synthetic data and on real data on large late Cenozoic mammals. As an example, for the sites with large number of occurrences of common genera, our methods give orderings, whose correlation with geochronologic ages is 0.95.     

Object-oriented Programming Paradigms for Molecular Modeling

This paper discusses the application of object-oriented programming (OOP) design concepts to the development of molecular simulation code. A number of new languages such as Fortran 90 (F90) have been developed over the last decade that support the OOP design philosophy. We briefly describe the salient features of F90 and some basic object-oriented design principles. As an illustration of the design concepts we implement a general interface in F90 for calculating pairwise interactions that can be extended easily to any number of different forcefield models. The ideas presented here are used in the development of a mu ltipurpose si mulation c ode, named Music. An example of the use of Music for grand canonical Monte Carlo (GCMC) simulations of flexible sorbate molecules in zeolites is given. The example illustrates how OOP allowed existing code for molecular dynamics and GCMC to be easily combined to perform hybrid GCMC simulations with minimal coding effort.     

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases.     

Decisions,patterns, and oscillations in nonlinear competitive systems with applications to Volterra-Lotka systems

This paper describes new properties of competitive systems which arise in population biology, ecology, psychophysiology, and developmental biology. These properties yield a global method for analyzing the geometric design and qualitative behavior, e.g. limits or oscillations, of competitive systems. The method explicates a main theme about competitive systems: who is winning the competition? The systems can undergo a complicated series of discrete decisions, or jumps, whose structure can, for example, yield global pattern formation or sustained oscillations, as in the voting paradox. The method illustrates how a parallel continuous system can be analyzed in terms of discrete serial operations, but notes that the next operation can be predicted only from the parallel interactions. It is shown that binary approximations to sigmoid signals in nonlinear networks are not valid in general, It is also shown how a temporal series of nested dynamic boundaries can be induced by purely nonlinear interactive effects. These boundaries restrict the fluctuations of population sizes or activities to ever finer intervals. The method can be used where Lyapunov methods fail and often obviates the need for local stability analysis. The paper also strengthens and corrects some previous results on the voting paradox.     

Algorithms for phylogenetic footprinting.

Phylogenetic footprinting is a technique that identifies regulatory elements by finding unusually well conserved regions in a set of orthologous noncoding DNA sequences from multiple species. We introduce a new motif-finding problem, the Substring Parsimony Problem, which is a formalization of the ideas behind phylogenetic footprinting, and we present an exact dynamic programming algorithm to solve it. We then present a number of algorithmic optimizations that allow our program to run quickly on most biologically interesting datasets. We show how to handle data sets in which only an unknown subset of the sequences contains the regulatory element. Finally, we describe how to empirically assess the statistical significance of the motifs found. Each technique is implemented and successfully identifies a number of known binding sites, as well as several highly conserved but uncharacterized regions. The program is available at http://bio.cs.washington.edu/software.html.     

Generation of synthetic data and experimental designs in evaluating interactions for association studies

Complex diseases, by definition, involve multiple factors, including gene-gene interactions and gene-environment interactions. Researchers commonly rely on simulated data to evaluate their approaches for detecting high-order interactions in disease gene mapping. A publicly available simulation program to generate samples involving complex genetic and environmental interactions is of great interest to the community. We have developed a software package named gs1.0, which has been widely used since its publication. In this article, we present an upgraded version gs2.0, which not only inherits its capacity to generate realistic genotype data but also provides great functionality and flexibility to simulate various interaction models. In addition to a standalone version, a user-friendly web server (http://cbc.case.edu/gs) has been set up to help users to build complex interaction models. Furthermore, by utilizing three three-locus models as an example, we have shown how realistic model parameters can be chosen in generating simulated data.     

Using mathematical models to understand metabolism,genes, and disease

Mathematical models are a useful tool for investigating a large number of questions in metabolism, genetics, and gene–environment interactions. A model based on the underlying biology and biochemistry is a platform for in silico biological experimentation that can reveal the causal chain of events that connect variation in one quantity to variation in another. We discuss how we construct such models, how we have used them to investigate homeostatic mechanisms, gene–environment interactions, and genotype–phenotype mapping, and how they can be used in precision and personalized medicine.