Spouses and Siblings in Sa Stories1

Earlier functionalist and structuralist approaches treat myths as texts rather than as stories told by people speaking in specific and variable contexts. An analysis of variations in the telling of two Sa stories from South Pentecost, Vanuatu, suggests that myths are not so much collective charters or manifestations of a deep unconscious structure of the mind but are stories which might be more biographically and historically situated.     

Cultural and instrumental approaches to ethnic nationalism

This article identifies a set of assumptions that underlie culturalist approaches to ethnic nationalism and it assesses these assumptions from a particular instrumentalist point of view ‐ collective‐choice theory. It is argued that cultural approaches are structuralist, leaving little room for intentional explanations and, when agent‐centred explanations are used, they are typically embedded within a moral economic theory of groups. In contrast, collective‐choice theory is intentionalist and political‐economic in orientation. From the perspective of these different approaches, the article examines a common dilemma of mobilization in nationalist movements ‐ how popular support can be mobilized by activists who, for entrepreneurial or ideological reasons, have formed a nationalist organization. Empirical illustrations are drawn from interwar Brittany and contemporary Quebec.     

Ancient DNA-typing approaches for the determination of kinship in a disturbed collective burial site

Several DNA-typing approaches are applied for identification and kinship analysis. Autosomal Short Tandem Repeat (STR) typing produces the genetic fingerprint that is unique to an individual. Y-chromosomal STR typing identifies individuals of the same paternal lineage, and sequence analysis of the hypervariable region of the mitochondrion can identify maternally related individuals. The combined approach of these DNA-typing methods allows the determination of kinship even in complex collective burial situations. In a bronze age collective site, the typing methods were tested for applicability to ancient DNA. For each approach, results were obtained, leading to the conclusion that the determination of kinship is achievable.     

Systems biology of virus entry in mammalian cells

In this article, we define systems biology of virus entry in mammalian cells as the discipline that combines several approaches to comprehensively understand the collective physical behaviour of virus entry routes, and to understand the coordinated operation of the functional modules and molecular machineries that lead to this physical behaviour. Clearly, these are extremely ambitious aims, but recent developments in different life science disciplines slowly allow us to set them as realistic, although very distant, goals. Besides classical approaches to obtain high-resolution information of the molecules, particles and machines involved, we require approaches that can monitor collective behaviour of many molecules, particles and machines simultaneously, in order to reveal design principles of the systems as a whole. Here we will discuss approaches that fall in the latter category, namely time-lapse imaging and single-particle tracking (SPT) combined with computational analysis and modelling, and genome-wide RNA interference approaches to reveal the host components required for virus entry. These techniques should in the future allow us to assign host genes to the systems' functions and characteristics, and allow emergence-driven, in silico assembly of networks that include interactions with increasing hierarchy (molecules-multiprotein complexes-vesicles and organelles), and kinetics and subcellular spatiality, in order to allow realistic simulations of virus entry in real time.     

Locust Collective Motion and Its Modeling

Over the past decade, technological advances in experimental and animal tracking techniques have motivated a renewed theoretical interest in animal collective motion and, in particular, locust swarming. This review offers a comprehensive biological background followed by comparative analysis of recent models of locust collective motion, in particular locust marching, their settings, and underlying assumptions. We describe a wide range of recent modeling and simulation approaches, from discrete agent-based models of self-propelled particles to continuous models of integro-differential equations, aimed at describing and analyzing the fascinating phenomenon of locust collective motion. These modeling efforts have a dual role: The first views locusts as a quintessential example of animal collective motion. As such, they aim at abstraction and coarse-graining, often utilizing the tools of statistical physics. The second, which originates from a more biological perspective, views locust swarming as a scientific problem of its own exceptional merit. The main goal should, thus, be the analysis and prediction of natural swarm dynamics. We discuss the properties of swarm dynamics using the tools of statistical physics, as well as the implications for laboratory experiments and natural swarms. Finally, we stress the importance of a combined-interdisciplinary, biological-theoretical effort in successfully confronting the challenges that locusts pose at both the theoretical and practical levels.     

Simulating nanoscale functional motions of biomolecules

We are describing efficient dynamics simulation methods for the characterization of functional motion of biomolecules on the nanometer scale. Multivariate statistical methods are widely used to extract and enhance functional collective motions from molecular dynamics (MD) simulations. A dimension reduction in MD is often realized through a principal component analysis (PCA) or a singular value decomposition (SVD) of the trajectory. Normal mode analysis (NMA) is a related collective coordinate space approach, which involves the decomposition of the motion into vibration modes based on an elastic model. Using the myosin motor protein as an example we describe a hybrid technique termed amplified collective motions (ACM) that enhances sampling of conformational space through a combination of normal modes with atomic level MD. Unfortunately, the forced orthogonalization of modes in collective coordinate space leads to complex dependencies that are not necessarily consistent with the symmetry of biological macromolecules and assemblies. In many biological molecules, such as HIV-1 protease, reflective or rotational symmetries are present that are broken using standard orthogonal basis functions. We present a method to compute the plane of reflective symmetry or the axis of rotational symmetry from the trajectory frames. Moreover, we develop an SVD that best approximates the given trajectory while respecting the symmetry. Finally, we describe a local feature analysis (LFA) to construct a topographic representation of functional dynamics in terms of local features. The LFA representations are low-dimensional, and provide a reduced basis set for collective motions, but unlike global collective modes they are sparsely distributed and spatially localized. This yields a more reliable assignment of essential dynamics modes across different MD time windows.     

Bringing Web 2.0 to bioinformatics

Enabling deft data integration from numerous, voluminous andheterogeneous data sources is a major bioinformatic challenge.Several approaches have been proposed to address this challenge,including data warehousing and federated databasing. Yet despitethe rise of these approaches, integration of data from multiplesources remains problematic and toilsome. These two approachesfollow a user-to-computer communication model for data exchange,and do not facilitate a broader concept of data sharing or collaborationamong users. In this report, we discuss the potential of Web2.0 technologies to transcend this model and enhance bioinformaticsresearch. We propose a Web 2.0-based Scientific Social Community(SSC) model for the implementation of these technologies. Byestablishing a social, collective and collaborative platformfor data creation, sharing and integration, we promote a webservices-based pipeline featuring web services for computer-to-computerdata exchange as users add value. This pipeline aims to simplifydata integration and creation, to realize automatic analysis,and to facilitate reuse and sharing of data. SSC can fostercollaboration and harness collective intelligence to createand discover new knowledge. In addition to its research potential,we also describe its potential role as an e-learning platformin education. We discuss lessons from information technology,predict the next generation of Web (Web 3.0), and describe itspotential impact on the future of bioinformatics studies.      

All Together Now: The Need for a Combined Empirical and Modeling Approach When Studying Primate Group Coordination

Studies of collective decision making attempt to explain the simultaneous behaviors of many individuals and how these contribute to the behavior observed at a collective level. However, this can be problematic to achieve given the general constraints of field or experimental data. This is particularly the case for primates, and results in limited reproducibility of events and it is difficult to separate the effects of different variables. Advocates of theoretical models have proposed that simple rules of interaction successfully reproduce different phases of group movement and the transitions between them, and greatly contribute to our knowledge of complex phenomena. Models can simulate practically any situation and tell us what response would emerge from it, including complex situations such as group decision making. The general heuristic value of these models has been universally recognized. However, the modeling approach tends to oversimplify real situations, and very few biological validations yet exist. I here suggest that it is essential to confront theoretical results with real data and that the combination of the 2 approaches will substantially improve our comprehension of collective decision making.     

Four points on Lennart von Post and the invention of “Pollen Statistics”

This essay is a contribution to the historiography of Lennart von Post and the early development of quantitative pollen analysis. Based on von Post’s own publications and source material from the archives of Stockholm University College, where he was appointed professor in 1929, the essay offers four points on von Post’s scientific identity and the collective work through which quantitative pollen analysis, or “pollen statistics”, came into being. The four points are, first, that von Post made his career as a geologist; second, that he framed pollen analysis as a means to tackle Quaternary geological issues; third, that his work benefitted from collective work, both in the field and in the laboratory; and fourth, that quantitative pollen analysis was not accepted without criticism, taking some years to break through beyond the Geological Survey, where von Post worked before he became professor.     

Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism

Understanding the impact of gene knockouts on cellular physiology, and metabolism in particular, is centrally important to quantitative systems biology and metabolic engineering. Here, we present a comprehensive physiological characterization of wild-type Escherichia coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including the PTS system, glycolysis, pentose phosphate pathway and Entner–Doudoroff pathway. Our results reveal significant metabolic changes that are affected by specific gene knockouts. Analysis of collective trends and correlations in the data using principal component analysis (PCA) provide new, and sometimes surprising, insights into E. coli physiology. Additionally, by comparing the data-to-model predictions from constraint-based approaches such as FBA, MOMA and RELATCH we demonstrate the important role of less well-understood kinetic and regulatory effects in central carbon metabolism.     

THE EFFECT OF COLLECTIVE DISPERSAL ON THE GENETIC STRUCTURE OF A SUBDIVIDED POPULATION

Correlated dispersal paths between two or more individuals are widespread across many taxa. The population genetic implications of this collective dispersal have received relatively little attention. Here we develop two‐sample coalescent theory that incorporates collective dispersal in a finite island model to predict expected coalescence times, genetic diversities, and F‐statistics. We show that collective dispersal reduces mixing in the system, which decreases expected coalescence times and increases F_ST. The effects are strongest in systems with high migration rates. Collective dispersal breaks the invariance of within‐deme coalescence times to migration rate, whatever the deme size. It can also cause F_ST to increase with migration rate because the ratio of within‐ to between‐deme coalescence times can decrease as migration rate approaches unity. This effect is most biologically relevant when deme size is small. We find qualitatively similar results for diploid and gametic dispersal. We also demonstrate with simulations and analytical theory the strong similarity between the effects of collective dispersal and anisotropic dispersal. These findings have implications for our understanding of the balance between drift–migration–mutation in models of neutral evolution. This has applied consequences for the interpretation of genetic structure (e.g., chaotic genetic patchiness) and estimation of migration rates from genetic data.     

Utility of mass spectrometry for proteome analysis: part II. Ion-activation methods,statistics, bioinformatics and annotation

This is the second article in a series, intended as a tutorial to provide the interested reader with an overview of the concepts not covered in part I, such as: the principles of ion-activation methods, the ability of mass-spectrometric methods to interface with various proteomic strategies, analysis techniques, bioinformatics and data interpretation and annotation. Although these are different topics, it is important that a reader has a basic and collective understanding of all of them for an overall appreciation of how to carry out and analyze a proteomic experiment. Different ion-activation methods for MS/MS, such as collision-induced dissociation (including postsource decay) and surface-induced dissociation, electron capture and electron-transfer dissociation, infrared multiphoton and blackbody infrared radiative dissociation have been discussed since they are used in proteomic research. The high dimensionality of data generated from proteomic studies requires an understanding of the underlying analytical procedures used to obtain these data, as well as the development of improved bioinformatics tools and data-mining approaches for efficient and accurate statistical analyses of biological samples from healthy and diseased individuals, in addition to determining the utility of the interpreted data. Currently available strategies for the analysis of the proteome by mass spectrometry, such as those employed for the analysis of substantially purified proteins and complex peptide mixtures, as well as hypothesis-driven strategies, have been elaborated upon. Processing steps prior to the analysis of mass spectrometry data, statistics and the several informatics steps currently used for the analysis of shotgun proteomic experiments, as well as proteomics ontology, are also discussed.     

Fellow travellers: emergent properties of collective cell migration

Cells can migrate individually or collectively. Collective movement is common during normal development and is also a characteristic of some cancers. This review discusses recent insights into features that are unique to collective cell migration, as well as properties that emerge from these features. The first feature is that cells of the collective affect each other through adhesion, force‐dependent and signalling interactions. The second feature is that cells of the collective differ from one another: leaders from followers, tip from stalk and front from back. These are dynamic differences that are important for directional movement. Last, an unexpected property is discussed: epithelial cells can rotate persistently in constrained spaces.     

Effects of surface water on protein dynamics studied by a novel coarse-grained normal mode approach

Normal mode analysis (NMA) has received much attention as a direct approach to extract the collective motions of macromolecules. However, the stringent requirement of computational resources by classical all-atom NMA limits the size of the macromolecules to which the method is normally applied. We implemented a novel coarse-grained normal mode approach based on partitioning the all-atom Hessian matrix into relevant and nonrelevant parts. It is interesting to note that, using classical all-atom NMA results as a reference, we found that this method generates more accurate results than do other coarse-grained approaches, including elastic network model and block normal mode approaches. Moreover, this new method is effective in incorporating the energetic contributions from the nonrelevant atoms, including surface water molecules, into the coarse-grained protein motions. The importance of such improvements is demonstrated by the effect of surface water to shift vibrational modes to higher frequencies and by an increase in overlap of the coarse-grained eigenvector space (the motion directions) with that obtained from molecular dynamics simulations of solvated protein in a water box. These results not only confirm the quality of our method but also point out the importance of incorporating surface structural water in studying protein dynamics.     

Effects of Disease Causing Mutations on the Essential Motions in Proteins

Abstract

This study embodies a detailed comparative analysis of the essential motions of the Wild type and the eight different disease mutant forms of the Human CYPlbl. The mutations considered in this study have been implicated in Primary Congenital Glaucoma, an in-born, genetic disorder associated with eye-abnormality. The principal component analysis for Wild type and the Mutants was carried out using the stabilized molecular dynamics trajectories, which ranged from 35 to 45 nanoseconds. Investigations revealed the nature of the collective motions that characterize functionally relevant ‘essential motions’. The essential motions in Wild type are characterized by the collective motions of the Substrate Access Channel including the β-rich domain and the loops in the region of p450-reductase interaction. Comparative analysis of the essential motions of the Wild type and Mutants, especially those involving the functionally important regions indicated distinct differences in their magnitudes as well as the residue-wise distribution. The Mutants in general are associated with higher root mean square fluctuations, and involve some of the relatively intact core regions of the protein, in large collective motions. This study sheds light on the possible effects of disease causing mutations on the large functionally important collective motions in proteins.     

Collective animal behavior from Bayesian estimation and probability matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.