Evolutionary stasis, constraint and other terminology describing evolutionary patterns

Current use of terms to describe evolutionary patterns is vague and inconsistent. In this paper, logical definitions of terms that describe specific evolutionary patterns are proposed. Evolutionary inertia is defined in a manner analogous to inertia in physics. A character in a static state of evolutionary inertia represents evolutionary stasis while a character showing consistent directional evolutionary change represents evolutionary thrust. I argue that evolutionary stasis should serve as the null hypothesis in all character evolution studies. Deviations from this null model consistent with alternative hypotheses (e.g. random drift, adaptation) can then give us insight into evolutionary processes. Failure to reject a null hypothesis of evolutionary stasis should not be used as a serious explanation of data. The term evolutionary constraint is appropriate only when a selective advantage for a character state transition is established but this transition is prevented by specific, identified factors. One type of evolutionary constraint discussed is evolutionary momentum. A final pattern of evolutionary change discussed is closely related to evolutionary thrust and is referred to as evolutionary acceleration. I provide examples of how this set of definitions can improve our ability to communicate interpretations of evolutionary patterns.     

A supervised approach for identifying discriminating genotype patterns and its application to breast cancer data

MOTIVATION: Large-scale association studies, investigating the genetic determinants of a phenotype of interest, are producing increasing amounts of genomic variation data on human cohorts. A fundamental challenge in these studies is the detection of genotypic patterns that discriminate individuals exhibiting the phenotype under study from individuals that do not possess it. The difficulty stems from the large number of single nucleotide polymorphism (SNP) combinations that have to be tested. The discrimination problem becomes even more involved when additional high-throughput data, such as gene expression data, are available for the same cohort. RESULTS: We have developed a graph theoretic approach for identifying discriminating patterns (DPs) for a given phenotype in a genotyped population. The method is based on representing the SNP data as a bipartite graph of individuals and their SNP states, and identifying fully connected subgraphs of this graph that relate individuals enriched for a given phenotypic group. The method can handle additional data types such as expression profiles of the genotyped population. It is reminiscent of biclustering approaches with the crucial difference that its search process is guided by the phenotype under consideration in a supervised manner. We tested our approach in simulations and on real data. In simulations, our method was able to retrieve planted patterns with high success rate. We then applied our approach to a dataset of 72 breast cancer patients with available gene expression profiles, genotyped over 695 SNPs. We detected several DPs that were highly significant with respect to various clinical phenotypes, and investigated the groups of patients and the groups of genes they defined. We found the patient groups to be highly enriched for other phenotypes and to display expression coherency among their profiles. The gene groups displayed functional coherency and involved genes with known role in cancer, providing additional support to their involvement. AVAILABILITY: The program is available upon request.     

The fluorescence studies of the sol-gel transition by styrylpyridine derivative

The gelation process of tetraethylorthosilanes in acid environment was monitored with the trans-4-(p-N,N-dimethylaminostyryl)-N-vinylbenzylopyridinium chloride (vbDMASP) fluorescent probe. The fluorescence steady-state and anisotropy measurements of material during sol-gel transition are reported. The results are compared with fluorescence studies of the probe in a modeled viscous system of water-glycerol mixtures. A strong increase of anisotropy, from 0.1 to 0.9, with gelation time as well with wavelength, was observed. Although the increase of anisotropy with wavelength is due to specificity of the compounds exhibiting charge transfer properties, the increase of the anisotropy with gelation time is due to an increase of microviscosity. On this basis, suitability of the applied fluorophore in recording of viscosity changes during sol-gel transition is discussed. The molecular structure of vbDMASP in the excited states in dependence on environmental polarity was optimized using the HyperChem and Amsol program. The dynamics of torsional angle C35-C34-N31-C28 of the multichromophore dye in correlation with micropolarity and microviscosity of the network formation during the sol-gel transition is discussed.     

Geographic patterns of mtDNA diversity in Europe

Genetic diversity in Europe has been interpreted as a reflection of phenomena occurring during the Paleolithic ( approximately 45,000 years before the present [BP]), Mesolithic ( approximately 18,000 years BP), and Neolithic ( approximately 10,000 years BP) periods. A crucial role of the Neolithic demographic transition is supported by the analysis of most nuclear loci, but the interpretation of mtDNA evidence is controversial. More than 2,600 sequences of the first hypervariable mitochondrial control region were analyzed for geographic patterns in samples from Europe, the Near East, and the Caucasus. Two autocorrelation statistics were used, one based on allele-frequency differences between samples and the other based on both sequence and frequency differences between alleles. In the global analysis, limited geographic patterning was observed, which could largely be attributed to a marked difference between the Saami and all other populations. The distribution of the zones of highest mitochondrial variation (genetic boundaries) confirmed that the Saami are sharply differentiated from an otherwise rather homogeneous set of European samples. However, an area of significant clinal variation was identified around the Mediterranean Sea (and not in the north), even though the differences between northern and southern populations were insignificant. Both a Paleolithic expansion and the Neolithic demic diffusion of farmers could have determined a longitudinal cline of mtDNA diversity. However, additional phenomena must be considered in both models, to account both for the north-south differences and for the greater geographic scope of clinical patterns at nuclear loci. Conversely, two predicted consequences of models of Mesolithic reexpansion from glacial refugia were not observed in the present study.     

Bonding patterns in transition metal clusters

Bonding patterns and electron counts of high-symmetry transition metal cluster compounds are discussed using a simplified Tensor Surface Harmonic (TSH) treatment. An n-vertex metal cluster has 9n valence atomic orbitals. From this set orbitals concerned in metal–ligand bonding are eliminated to yield the effective valence orbitals available to cluster bonding. Applying TSH theory to this smaller set gives a clear classification of the cluster MOs and their bonding/antibonding characteristics. Orbital mixing allowed by point group symmetry gives a final, qualitative cluster MO diagram. Results for triangular, tetrahedral and octahedral clusters are compared with other models.     

Gas exchange patterns of bumble bee foragers before and after exposing to lowered temperature

The gas exchange patterns are known to vary between insect species, individuals and even intra-individually. Using volumetric-manometric and flow-through respirometry combined with IR-actography we studied how periods of low temperature affect the respiratory patterns of bumble bee Bombus terrestris foragers. We have shown, in this study, that there is a change in the respiratory patterns of individual B. terrestris foragers after exposing to low temperatures. The bumble bees seemed to become more inactive. The different respiratory patterns appeared in succession and the transition from one pattern to another was associated with the change from an active to a resting state. Typical patterns after exposition to low temperature were discontinuous gas exchange cycles (DGCs).     

Kinetics of actin-myosin binding. II. Two-variable model and actin gelation

We consider a model of actin-myosin interaction in which the sites belonging to each seven-site regulated actin unit are subdivided into two classes, "internal" and "external." The time evolution of each class of sites is considered separately, leading to a pair of coupled differential equations that may be integrated numerically. We also consider the critical sol-gel transition point for actin filaments crosslinked by two-headed heavy meromyosin (HMM). The possibility of new types of chemical oscillation and pattern formation arising from periodic sol-gel transitions is discussed.     

Formation of band-type electric patterns in Characean cells

It is observed by experiments that band patterns of alternating acid and alkaline zones are formed on the surface of Characean cells under illumination. In order to understand theoretically such pattern formation, we employ a reaction-diffusion equation model with activator-inhibitor interaction. We study the existence problem of band patterns and hysteresis phenomena such as patterns appearing and disappearing with changing light intensity by using singular perturbation methods.     

Psychophysical and computational studies of random-dot Moire patterns

Random-dot Moire dot patterns epitomize the local/global gap, the problem of perceiving global structures when only physical local information is available to the perceptual system. At present, no single theory appears to be able to account for all the phenomena these physically simple patterns generate.     

A hidden Markov model approach to neuron firing patterns.

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.     

Regional and time-resolved mutation patterns of the human genome

MOTIVATION: Substantial regional variations of substitutional processes have recently been reported from human/mouse comparisons. However, several features including the C + G dependence and the CpG-based transition effect remain obscure. RESULTS: Utilizing the vast amount of transposable elements in the human genome, we performed detailed analysis of the substitutional and insertion/deletion patterns along the human lineage in a regional and time-resolved fashion. We observed a drastic increase in the CpG-based transition frequency at about the time of the mammalian radiation. We also observed clear regional biases of substitution patterns, most notably a bias to enrich the C+G content toward the telomeres. AVAILABILITY: The programs used are available upon request from the authors.     

Bistability and regular spatial patterns in arid ecosystems

A variety of patterns observed in ecosystems can be explained by resource–concentration mechanisms. A resource–concentration mechanism occurs when organisms increase the lateral flow of a resource toward them, leading to a local concentration of this resource and to its depletion from areas farther away. In resource–concentration systems, it has been proposed that certain spatial patterns could indicate proximity to discontinuous transitions where an ecosystem abruptly shifts from one stable state to another. Here, we test this hypothesis using a model of vegetation dynamics in arid ecosystems. In this model, a resource–concentration mechanism drives a positive feedback between vegetation and soil water availability. We derived the conditions leading to bistability and pattern formation. Our analysis revealed that bistability and regular pattern formation are linked in our model. This means that, when regular vegetation patterns occur, they indicate that the system is along a discontinuous transition to desertification. Yet, in real systems, only observing regular vegetation patterns without identifying the pattern-driving mechanism might not be enough to conclude that an ecosystem is along a discontinuous transition because similar patterns can emerge from different ecological mechanisms.     

Formation and deformation of patterns through diffusion

Simulating various patterns exhibited on biological forms with mathematical models has become an important supplement to theoretical biology. Models based on a certain mechanism are intended to provide explanations to the formation of a basic pattern. However, in real phenomena, among a basic pattern there always exist some difference between any two individuals. Such differences are consequences of environmental factors posed during the developmental processes. These factors, such as temperature, affect the diffusion rates of corresponding morphogenes which, in turn, alter a basic pattern to certain extent. We provide, in this paper, a quantitative characterization of this effect for a class of reaction-diffusion models.Mathematically, we study the emergence of stationary patterns and their dependence on diffusion rates for this class of models (RD-equations) with no-flux boundary conditions. The results are generalized to systems with homogeneous Dirichlet boundary conditions when the kinetic terms are odd functions. Through an analysis of the phase dynamics, we show that the deformation of stationary patterns, as the diffusion rates change, is governed by the variation of certain plane curves in the phase space. A constructive proof is given which shows explicitly how to obtain such curves.Applications of this study are illustrated with three model examples. We use these models to explain the biological implications of the mathematical features we investigated. Results from computer simulations are presented and compared with physical patterns.     

Hamiltonian energy and coexistence of hidden firing patterns from bidirectional coupling between two different neurons

In this paper, bidirectional-coupled neurons through an asymmetric electrical synapse are investigated. These coupled neurons involve 2D Hindmarsh–Rose (HR) and 2D FitzHugh–Nagumo (FN) neurons. The equilibria of the coupled neurons model are investigated, and their stabilities have revealed that, for some values of the electrical synaptic weight, the model under consideration can display either self-excited or hidden firing patterns. In addition, the hidden coexistence of chaotic bursting with periodic spiking, chaotic spiking with period spiking, chaotic bursting with a resting pattern, and the coexistence of chaotic spiking with a resting pattern are also found for some sets of electrical synaptic coupling. For all the investigated phenomena, the Hamiltonian energy of the model is computed. It enables the estimation of the amount of energy released during the transition between the various electrical activities. Pspice simulations are carried out based on the analog circuit of the coupled neurons to support our numerical results. Finally, an STM32F407ZE microcontroller development board is exploited for the digital implementation of the proposed coupled neurons model.     

Weightlifting performance is related to kinematic and kinetic patterns of the hip and knee joints

The purpose of this study was to investigate the correlations between biomechanical outcome measures and weightlifting performance. Joint kinematics and kinetics of the hip, knee, and ankle were calculated while 10 subjects performed a clean at 85% of 1 repetition maximum (1RM). Kinematic and kinetic time-series patterns were extracted with principal components analysis. Discrete scores for each time-series pattern were calculated and used to determine how each pattern was related to body mass-normalized 1RM. Two hip kinematic and 2 knee kinetic patterns were significantly correlated with relative 1RM. The kinematic patterns captured hip and trunk motions during the first pull and hip joint motion during the movement transition between the first and second pulls. The first kinetic pattern captured a peak in the knee extension moment during the second pull. The second kinetic pattern captured a spatiotemporal shift in the timing and amplitude of the peak knee extension moment. The kinematic results suggest that greater lift mass was associated with steady trunk position during the first pull and less hip extension motion during the second-knee bend transition. Further, the kinetic results suggest that greater lift mass was associated with a smaller knee extensor moments during the first pull, but greater knee extension moments during the second pull, and an earlier temporal transition between knee flexion-extension moments at the beginning of the second pull. Collectively, these results highlight the importance of controlled trunk and hip motions during the first pull and rapid employment of the knee extensor muscles during the second pull in relation to weightlifting performance.     

Detecting conserved interaction patterns in biological networks.

Molecular interaction data plays an important role in understanding biological processes at a modular level by providing a framework for understanding cellular organization, functional hierarchy, and evolutionary conservation. As the quality and quantity of network and interaction data increases rapidly, the problem of effectively analyzing this data becomes significant. Graph theoretic formalisms, commonly used for these analysis tasks, often lead to computationally hard problems due to their relation to subgraph isomorphism. This paper presents an innovative new algorithm, MULE, for detecting frequently occurring patterns and modules in biological networks. Using an innovative graph simplification technique based on ortholog contraction, which is ideally suited to biological networks, our algorithm renders these problems computationally tractable and scalable to large numbers of networks. We show, experimentally, that our algorithm can extract frequently occurring patterns in metabolic pathways and protein interaction networks from the KEGG, DIP, and BIND databases within seconds. When compared to existing approaches, our graph simplification technique can be viewed either as a pruning heuristic, or a closely related, but computationally simpler task. When used as a pruning heuristic, we show that our technique reduces effective graph sizes significantly, accelerating existing techniques by several orders of magnitude! Indeed, for most of the test cases, existing techniques could not even be applied without our pruning step. When used as a stand-alone analysis technique, MULE is shown to convey significant biological insights at near-interactive rates. The software, sample input graphs, and detailed results for comprehensive analysis of nine eukaryotic PPI networks are available at www.cs.purdue.edu/homes/koyuturk/mule.     

Flow patterns around ciliated microorganisms and in ciliated ducts

Microscopic organisms and tracts of ciliated epithelia often generate complicated flow patterns such as eddies or jet-like phenomena. In addition, the presence of boundaries (e.g. microscope slide or coverslip) may also influence or severely restrict the resulting fluid motion. This paper develops simplified theoretical models of flow patterns due to microorganisms, or tracts of ciliated epithelia, to help enhance our understanding of the mechanisms creating these patterns. It is demonstrated that an active driving mechanism (such as flagella or cilia) in close proximity to a non-active region (e.g. inactive cilia, non-ciliated region, slide) can produce these complicated patterns.     

Theory predicts a rapid transition from niche-structured to neutral biodiversity patterns across a speciation-rate gradient

A central challenge in community ecology is to predict patterns of biodiversity with mechanistic models. The neutral model of biodiversity is a simple model that appears to provide parsimonious and accurate predictions of biodiversity patterns in some ecosystems, even though it ignores processes such as species interactions and niche structure. In a recent paper, we used analytical techniques to reveal why the mean predictions of the neutral model are robust to niche structure in high diversity but not low-diversity ecosystems. In the present paper, we explore this phenomenon further by generating stochastic simulated data from a spatially implicit hybrid niche-neutral model across different speciation rates. We compare the resulting patterns of species richness and abundance with the patterns expected from a pure neutral and a pure niche model. As the speciation rate in the hybrid model increases, we observe a surprisingly rapid transition from an ecosystem in which diversity is almost entirely governed by niche structure to one in which diversity is statistically indistinguishable from that of the neutral model. Because the transition is rapid, one prediction of our abstract model is that high-diversity ecosystems such as tropical forests can be approximated by one simple model—the neutral model—whereas low-diversity ecosystems such as temperate forests can be approximated by another simple model—the niche model. Ecosystems that require the hybrid model are predicted to be rare, occurring only over a narrow range of speciation rates.