Dialogue and causality: global description from local observations and vague communications

We propose a dialogue-based society model which explains how the transitive law of the causality is originated. Causality is, in general, formalized by using axiomatic approaches. Instead of using axiomatic methods, we, however, compose a model consisting of agents who have knowledge about causal relations among objects. The model society can reveal the transitive law through interactional dialogues among themselves. The agents are reciprocally influenced, if they have either completely same opinions, or a particular pattern of opinions, that are regarded as the extension of such exact accordance. In addition, we add some vagueness to the dialogue, which is closer to a real communication than the former model. A set of knowledge of each agent is expressed as a directed graph, hence the every model can be construed as mere transformations of directed graphs through the interactions among directed graphs themselves. Following this perspective, the models are the systems that connect local logic with global one, while the union of the directed graphs is regarded as the global.     

Detecting hierarchical structure in molecular characteristics of disease using transitive approximations of directed graphs

MOTIVATION: Molecular diagnostics aims at classifying diseases into clinically relevant sub-entities based on molecular characteristics. Typically, the entities are split into subgroups, which might contain several variants yielding a hierarchical model of the disease. Recent years have introduced a plethora of new molecular screening technologies to molecular diagnostics. As a result molecular profiles of patients became complex and the classification task more difficult. RESULTS: We present a novel tool for detecting hierarchical structure in binary datasets. We aim for identifying molecular characteristics, which are stochastically implying other characteristics. The final hierarchical structure is encoded in a directed transitive graph where nodes represent molecular characteristics and a directed edge from a node A to a node B denotes that almost all cases with characteristic B also display characteristic A. Naturally, these graphs need to be transitive. In the core of our modeling approach lies the problem of calculating good transitive approximations of given directed but not necessarily transitive graphs. By good transitive approximation we understand transitive graphs, which differ from the reference graph in only a small number of edges. It is known that the problem of finding optimal transitive approximation is NP-complete. Here we develop an efficient heuristic for generating good transitive approximations. We evaluate the computational efficiency of the algorithm in simulations, and demonstrate its use in the context of a large genome-wide study on mature aggressive lymphomas. AVAILABILITY: The software used in our analysis is freely available from http://compdiag.uni-regensburg.de/software/transApproxs.shtml.     

Graph reconstruction using covariance-based methods

Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data.     

Discrete Model of Opinion Changes Using Knowledge and Emotions as Control Variables

We present a new model of opinion changes dependent on the agents emotional state and their information about the issue in question. Our goal is to construct a simple, yet nontrivial and flexible representation of individual attitude dynamics for agent based simulations, that could be used in a variety of social environments. The model is a discrete version of the cusp catastrophe model of opinion dynamics in which information is treated as the normal factor while emotional arousal (agitation level determining agent receptiveness and rationality) is treated as the splitting factor. Both variables determine the resulting agent opinion, which itself can be in favor of the studied position, against it, or neutral. Thanks to the flexibility of implementing communication between the agents, the model is potentially applicable in a wide range of situations. As an example of the model application, we study the dynamics of a set of agents communicating among themselves via messages. In the example, we chose the simplest, fully connected communication topology, to focus on the effects of the individual opinion dynamics, and to look for stable final distributions of agents with different emotions, information and opinions. Even for such simplified system, the model shows complex behavior, including phase transitions due to symmetry breaking by external propaganda.     

Evolution: geometrical and dynamical aspects.

We develop a possible axiomatic approach to the evolution theory that has been previously discussed in Freguglia [2002]. The axioms synthesize the fundamental ideas of evolution theory and allow a geometrical and dynamical interpretation of the generation law. Using the axioms we derive a simple reaction-diffusion model which introduces the species as self-organized stationary distribution of a finite population and simulates the evolution of a phenotypic character under the effect of an external perturbing action. The dynamical properties of the model are briefly presented using numerical simulations.     

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.     

Are opinions based on science: modelling social response to scientific facts

As scientists we like to think that modern societies and their members base their views, opinions and behaviour on scientific facts. This is not necessarily the case, even though we are all (over-) exposed to information flow through various channels of media, i.e. newspapers, television, radio, internet, and web. It is thought that this is mainly due to the conflicting information on the mass media and to the individual attitude (formed by cultural, educational and environmental factors), that is, one external factor and another personal factor. In this paper we will investigate the dynamical development of opinion in a small population of agents by means of a computational model of opinion formation in a co-evolving network of socially linked agents. The personal and external factors are taken into account by assigning an individual attitude parameter to each agent, and by subjecting all to an external but homogeneous field to simulate the effect of the media. We then adjust the field strength in the model by using actual data on scientific perception surveys carried out in two different populations, which allow us to compare two different societies. We interpret the model findings with the aid of simple mean field calculations. Our results suggest that scientifically sound concepts are more difficult to acquire than concepts not validated by science, since opposing individuals organize themselves in close communities that prevent opinion consensus.     

Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms

MOTIVATION: Information from fully sequenced genomes makes it possible to reconstruct strain-specific global metabolic network for structural and functional studies. These networks are often very large and complex. To properly understand and analyze the global properties of metabolic networks, methods for rationally representing and quantitatively analyzing their structure are needed. RESULTS: In this work, the metabolic networks of 80 fully sequenced organisms are in silico reconstructed from genome data and an extensively revised bioreaction database. The networks are represented as directed graphs and analyzed by using the 'breadth first searching algorithm to identify the shortest pathway (path length) between any pair of the metabolites. The average path length of the networks are then calculated and compared for all the organisms. Different from previous studies the connections through current metabolites and cofactors are deleted to make the path length analysis physiologically more meaningful. The distribution of the connection degree of these networks is shown to follow the power law, indicating that the overall structure of all the metabolic networks has the characteristics of a small world network. However, clear differences exist in the network structure of the three domains of organisms. Eukaryotes and archaea have a longer average path length than bacteria. AVAILABILITY: The reaction database in excel format and the programs in VBA (Visual Basic for Applications) are available upon request. SUPPLEMENTARY MATERIAL: Bioinformatics Online.     

A graph-theoretic approach to comparing and integrating genetic, physical and sequence-based maps

For many species, multiple maps are available, often constructed independently by different research groups using different sets of markers and different source material. Integration of these maps provides a higher density of markers and greater genome coverage than is possible using a single study. In this article, we describe a novel approach to comparing and integrating maps by using abstract graphs. A map is modeled as a directed graph in which nodes represent mapped markers and edges define the order of adjacent markers. Independently constructed graphs representing corresponding maps from different studies are merged on the basis of their common loci. Absence of a path between two nodes indicates that their order is undetermined. A cycle indicates inconsistency among the mapping studies with regard to the order of the loci involved. The integrated graph thus produced represents a complete picture of all of the mapping studies that comprise it, including all of the ambiguities and inconsistencies among them. The objective of this representation is to guide additional research aimed at interpreting these ambiguities and inconsistencies in locus order rather than presenting a "consensus order" that ignores these problems.     

Migration as Adventure: Narrative Self-representation among Caribbean Migrants in Denmark

Narratives of adventure constitute a well-established convention of describing travel experiences, yet the significance of this narrative genre in individuals’ accounts of their migration and life abroad has been little investigated. Drawing on Simmel and Bakhtin, among others, this article analyses how Caribbean immigrants in Denmark present themselves through narratives of adventure as travellers of the world eager to experience different cultures and societies. In this way they assert themselves as actors within a global arena in contrast to a Denmark that, like many other nations in the global north, presents itself today as increasingly inward oriented and anxious to protect the local culture and society against foreign intruders. It is suggested that ethnographic analysis of migrants’ narrative self-representation in receiving societies can offer new insights into migrants as acting and reflecting subjects at a time when many societies display growing scepticism towards people on the move.     

Graph theoretical methods for comparing phytosociological structures

This paper describes some methods that can be used to compare the phytosociological structure of plant communities using some graph theoretic properties of the directed graphs that represent them. In such a graph, the species are represented by points and the association of species A with species B is represented by a directed line segment going from B to A. Two communities can be compared using simple indices to measure the similarity of their species-lists (point similarity) and of the species associations in them (line similarity). A more sophisticated and informative measure of line similarity is the probability that, given the number of points shared by two graphs, they have at least as many lines in common as they are observed to have. A formula for calculating that probability is given here. The graphs of community structure can also be compared with respect to the homogeneity of the distribution of the lines among the points, a property related to the number of species that are important in determining the composition of the community. These techniques are illustrated using the graphs of the phytosociological structure of intertidal seaweed communities on the southeast coast of Nova Scotia.Nomenclature follows: South and Cardinal (1970)     

Semi-Markov graph dynamics

In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first ingredient is a Markov chain on the space of possible graphs. The second ingredient is a semi-Markov counting process of renewal type. The model consists in subordinating the Markov chain to the semi-Markov counting process. In simple words, this means that the chain transitions occur at random time instants called epochs. The model is quite rich and its possible connections with algebraic geometry are briefly discussed. Moreover, for the sake of simplicity, we focus on the space of undirected graphs with a fixed number of nodes. However, in an example, we present an interbank market model where it is meaningful to use directed graphs or even weighted graphs.     

The emergence of scaling in sequence-based physical models of protein evolution

It has recently been discovered that many biological systems, when represented as graphs, exhibit a scale-free topology. One such system is the set of structural relationships among protein domains. The scale-free nature of this and other systems has previously been explained using network growth models that, although motivated by biological processes, do not explicitly consider the underlying physics or biology. In this work we explore a sequence-based model for the evolution protein structures and demonstrate that this model is able to recapitulate the scale-free nature observed in graphs of real protein structures. We find that this model also reproduces other statistical feature of the protein domain graph. This represents, to our knowledge, the first such microscopic, physics-based evolutionary model for a scale-free network of biological importance and as such has strong implications for our understanding of the evolution of protein structures and of other biological networks.     

TransMiner: Mining transitive associations among biological objects from text

Associations among biological objects such as genes, proteins, and drugs can be discovered automatically from the scientific literature. TransMiner is a system for finding associations among objects by mining the Medline database of the scientific literature. The direct associations among the objects are discovered based on the principle of co-occurrence in the form of an association graph. The principle of transitive closure is applied to the association graph to find potential transitive associations. The potential transitive associations that are indeed direct are discovered by iterative retrieval and mining of the Medline documents. Those associations that are not found explicitly in the entire Medline database are transitive associations and are the candidates for hypothesis generation. The transitive associations were ranked based on the sum of weight of terms that cooccur with both the objects. The direct and transitive associations are visualized using a graph visualization applet. TransMiner was tested by finding associations among 56 breast cancer genes and among 24 objects in the calpain signal transduction pathway. TransMiner was also used to rediscover associations between magnesium and migraine.     

Absence of transitive and systemic pathways allows cell-specific and isoform-specific RNAi in Drosophila

RNA interference (RNAi) designates the multistep process by which double-stranded RNA induces the silencing of homologous endogenous genes. Some aspects of RNAi appear to be conserved throughout evolution, including the processing of trigger dsRNAs into small 21-23-bp siRNAs and their use to guide the degradation of complementary mRNAs. Two remarkable features of RNAi were uncovered in plants and Caenorhabditid elegans. First, RNA-dependent RNA polymerase activities allow the synthesis of siRNA complementary to sequences upstream of or downstream from the initial trigger region in the target mRNA, leading to a transitive RNAi with sequences that had not been initially targeted. Secondly, systemic RNAi may cause the targeting of gene silencing in one tissue to spread to other tissues. Using transgenes expressing dsRNA, we investigated whether transitive and systemic RNAi occur in DROSOPHILA: DsRNA-producing transgenes targeted RNAi to specific regions of alternative mRNA species of one gene without transitive effect directed to sequences downstream from or upstream of the initial trigger region. Moreover, specific expression of a dsRNA, using either cell-specific GAL4 drivers or random clonal activation of a GAL4 driver, mediated a cell-autonomous RNAi. Together, our results provide evidence that transitive and systemic aspects of RNAi are not conserved in Drosophila and demonstrate that dsRNA-producing transgenes allow powerful reverse genetic approaches to be conducted in this model organism, by knocking down gene functions at the resolution of a single-cell type and of a single isoform.     

A comparative study of cells in inflammation,EAE and MS using biomedical literature data mining

Summary Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average γ = 2.1) that are commonly found in biological networks.     

Transitive or Not: A Critical Appraisal of Transitive Inference in Animals

Transitive inference has been historically touted as a hallmark of human cognition. However, the ability of non‐human animals to perform this type of inference is being increasingly investigated. Experimentally, three main methods are commonly used to evaluate transitivity in animals: those that investigate social dominance relationships, the n‐term task series and the less well known associative transitivity task. Here, we revisit the question of what exactly constitutes transitive inference based upon a formal and habitual definition and propose two essential criteria for experimentally testing it in animals. We then apply these criteria to evaluate the existing body of work on this fundamental aspect of cognition using exemplars. Our evaluation reveals that some methods rely heavily on salient assumptions that are both questionable and almost impossible to verify in order to make a claim of transitive inference in animals. For example, we found shortcomings with most n‐term task designs in that they often do not provide an explicit transitive relationship and/or and ordered set on which transitive inference can be performed. Consequently, they rely on supplementary assumptions to make a claim of transitive inference. However, as these assumptions are either impossible or are extremely difficult to validate in non‐human animals, the results obtained using these specific n‐term tasks cannot be taken as unambiguous demonstrations (or the lack thereof) of transitive inference. This realisation is one that is generally overlooked in the literature. In contrast, the associative transitivity task and the dominance relationship test both meet the criteria for transitive inference. However, although the dominance relationship test can disambiguate between transitive inference accounts and associative ones, the associative transitivity test cannot. Our evaluation also highlights the limitations and future challenges of current associative models of transitive inference. We propose three new experimental methods that can be applied within any theoretical framework to ensure that the experimental behaviour observed is indeed the result of transitive inference whilst removing the need for supplementary assumptions: the test for the opposite transitive relation, the discrimination test between two separate and previously non‐reinforced items, and the control for absolute knowledge.     

Predicting Helical Topologies in RNA Junctions as Tree Graphs

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires knowledge of their tertiary structures. Though computational RNA folding approaches exist, they often require manual manipulation and expert intuition; predicting global long-range tertiary contacts remains challenging. Here we develop a computational approach and associated program module (RNAJAG) to predict helical arrangements/topologies in RNA junctions. Our method has two components: junction topology prediction and graph modeling. First, junction topologies are determined by a data mining approach from a given secondary structure of the target RNAs; second, the predicted topology is used to construct a tree graph consistent with geometric preferences analyzed from solved RNAs. The predicted graphs, which model the helical arrangements of RNA junctions for a large set of 200 junctions using a cross validation procedure, yield fairly good representations compared to the helical configurations in native RNAs, and can be further used to develop all-atom models as we show for two examples. Because junctions are among the most complex structural elements in RNA, this work advances folding structure prediction methods of large RNAs. The RNAJAG module is available to academic users upon request.