Modeling of dynamic systems with Petri nets and fuzzy logic

Aktuelle Methoden zur dynamischen Modellierung von biologischen Systemen sind für Benutzer ohne mathematische Ausbildung oft wenig verständlich. Des Weiteren fehlen sehr oft genaue Daten und detailliertes Wissen über Konzentrationen, Reaktionskinetiken oder regulatorische Effekte. Daher erfordert eine computergestützte Modellierung eines biologischen Systems, mit Unsicherheiten und grober Information umzugehen, die durch qualitatives Wissen und natürlichsprachliche Beschreibungen zur Verfügung gestellt wird. Der Autor schlägt einen neuen Ansatz vor, mit dem solche Beschränkungen überwunden werden können. Dazu wird eine Petri-Netz-basierte graphische Darstellung von Systemen mit einer leistungsstarken und dennoch intuitiven Fuzzy-Logik-basierten Modellierung verknüpft. Der Petri Netz und Fuzzy Logik (PNFL) Ansatz erlaubt eine natürlichsprachlich-basierte Beschreibung von biologischen Entitäten sowie eine Wenn-Dann-Regel-basierte Definition von Reaktionen. Beides kann einfach und direkt aus qualitativem Wissen abgeleitet werden. PNFL verbindet damit qualitatives Wissen und quantitative Modellierung.Current approaches in dynamic modeling of biological systems often lack comprehensibility,n especially for users without mathematical background. Additionally, exact data or detailed knowledge about concentrations, reaction kinetics or regulatory effects is missing. Thus, computational modeling of a biological system requires dealing with uncertainty and rough information provided by qualitative knowledge and linguistic descriptions. The author proposes a new approach to overcome such limitations by combining the graphical representation provided by Petri nets with the modeling of dynamics by powerful yet intuitive fuzzy logic based systems. The Petri net and fuzzy logic (PNFL) approach allows natural language based descriptions of biological entities as well as if-then rule based definitions of reactions, both of which can be easily and directly derived from qualitative knowledge. PNFL bridges the gap between qualitative knowledge and quantitative modeling

How to analyze dynamic network patterns of high performing teams

AbstractThe dynamic communication network within teams affects the performance of teams. But how can we analyze these emerging networks? We identified three research areas that have to be included for this purpose. First we summarize empirical studies concerning team networks and performance to point out the need of longitudinal investigations. Second we present the multi-level multi-theoretical model by Monge and Contractor (2003) which provides a theoretical framework to explain the evolution of communication networks within teams. Third a stochastic model is introduced that allows analyzing event based data, like e-mail streams, using exponential random graph models. We propose to include these three research areas that enable researchers and practitioners to analyze dynamic network patterns of virtual teams

Refining Ensembles of Predicted Gene Regulatory Networks Based on Characteristic Interaction Sets

Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate predictions for a gene regulatory network, e. g. due to probabilistic optimization or a cross-validation procedure

Strain-specific genes of Helicobacter pylori: genome evolution driven by a novel type IV secretion system and genomic island transfer

The availability of multiple bacterial genome sequences has revealed a surprising extent of variability among strains of the same species. The human gastric pathogen Helicobacter pylori is known as one of the most genetically diverse species. We have compared the genome sequence of the duodenal ulcer strain P12 and six other H. pylori genomes to elucidate the genetic repertoire and genome evolution mechanisms of this species. In agreement with previous findings, we estimate that the core genome comprises about 1200 genes and that H. pylori possesses an open pan-genome. Strain-specific genes are preferentially located at potential genome rearrangement sites or in distinct plasticity zones, suggesting two different mechanisms of genome evolution. The P12 genome contains three plasticity zones, two of which encode type IV secretion systems and have typical features of genomic islands. We demonstrate for the first time that one of these islands is capable of self-excision and horizontal transfer by a conjugative process. We also show that excision is mediated by a protein of the XerD family of tyrosine recombinases. Thus, in addition to its natural transformation competence, conjugative transfer of genomic islands has to be considered as an important source of genetic diversity in H. pylori

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

Background: The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions: The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters

Kommunikationsflüsse im Bild. Dynamische Netzwerkvisualisierung in der internen Organisationskommunikation anhand des Fallbeispiels eines Universitätsinstituts

Die Stärken der Sozialen Netzwerkanalyse in der Darstellung tatsächlicher Kommunikationsflüsse und informeller Muster der Zusammenarbeit in Gruppen und Organisationen wurden bereits weitgehend diskutiert. Verschiedene Kombinationen von Methoden und Algorithmen stehen zu Verfügung, um kommunikative Beziehungsmuster zu erheben und als statische Netzwerke zu visualisieren. Die laufende Veränderung dieser Netzwerke über die Zeit bringt jedoch die Frage mit sich, wie diese Dynamiken sichtbar gemacht werden können. Im vorliegenden Artikel werden vier Methoden der dynamischen Netzwerkvisualisierung vorgestellt, um die Veränderung von sozialen Netzwerken visuell zu analysieren. Anhand des Fallbeispiels eines Universitätsinstituts wird deren Anwendung demonstriert und aufgezeigt, wie dadurch kommunikationswissenschaftliche Forschung in Organisationen unterstützt werden kann

A visual analytics approach to dynamic social networks

The visualization and analysis of dynamic networks have become increasingly important in several fields, for instance sociology or economics. The dynamic and multi-relational nature of this data poses the challenge of understanding both its topological structure and how it changes over time. In this paper we propose a visual analytics approach for analyzing dynamic networks that integrates: a dynamic layout with user-controlled trade-off between stability and consistency; three temporal views based on different combinations of node-link diagrams (layer superimposition, layer juxtaposition, and two-and-a-halfdimensional view); the visualization of social network analysis metrics; and specific interaction techniques for tracking node trajectories and node connectivity over time. This integration of visual, interactive, and automatic methods supports the multifaceted analysis of dynamically changing networks

Entropy and AUPRC evaluation results.

<p>(A) The entropy of group-ensembles is on average decreased to 45% as compared to the entropy of the ensemble of all networks (full-ensemble). This is caused by the reduced fraction of low confidence interactions. (B) AUPRCs of group-ensembles are increased if their characteristic sets are present in the reference. Characteristic set precision ranges between 1 (all interactions are present in the reference) and 0 (no interaction is present in the reference). A small amount of horizontal jitter (<0.02) was added to the precision values for better visualization. The red lines indicate identity. (C) Rejecting alternative hypothesis by testing for the presence of characteristic set interactions (white boxplots) in general increases AUPRC, while testing for other low confidence interactions (gray boxplots) has a less pronounced or even negative effect. Thus, interactions that are predicted to be co-occurring with other interactions are preferred targets of further experimental verification. The full-ensemble AUPRC distributions are shown as dark-gray boxplots.</p