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

Petri nets in biology

Danas se u molekularnoj biologiji sve više pozornosti stavlja na istraživanje staničnih puteva, jer promjena u njihovoj signalizaciji može dovesti do pojave raznih i teških bolesti kao što je rak. Kombinacijom računalne snage i molekularne biologije možemo razviti dinamične mreže proteina, kojima zatim istražujemo signalizaciju staničnih puteva. Petrijeve mreže su nova metoda u računalnoj biologiji koju se može koristiti za dinamičko modeliranje staničnih regulatornih mreža. U ovom radu opisan je princip rada Petrijevih mreža i osnovne vrste Petrijevih mreža, te je dan primjer kako se Petrijeve mreže mogu koristiti za istraživanje bioloških problema.In comparison to single molecular interactions, more and more emphasis in being given to the signaling of cellular pathways, as their alteration can lead to outcomes such as cancer. Combining computational power with molecular biology is used to develop dynamic networks of proteins, which are then used to explore the network signaling. Petri Nets are a novel tool in bioinformatics that can be used for the dynamic modeling of cellular regulatory networks. This work describes the methodology of Petri Nets and the types of Petri Nets, and additionally, gives an example on how they can be applied in biological research

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

Desarrollo de un enfoque de trabajo para el análisis y diseño de sistemas discretos y dinámicos : Aplicación a la simulación de la demanda eléctrica de la ciudad de Salta

La reunión de las disciplinas orientadas a los problemas y las soluciones puede llevar a importantes avances en ambas áreas. Las redes de Petri (RdP) proporcionan un medio excelente para modelar aspectos concurrentes y se han extendido de muchas maneras para hacer frente a muchos problemas. Las RdP se han aplicado con éxito muchas veces a varios problemas de ingeniería de software. Sin embargo, las dos disciplinas no pasan por un período de fertilización cruzada particularmente fuerte. Este trabajo trata de analizar algunos aspectos de la ingeniería de software, señalando aspectos en los que las RdP se han propuesto o se pueden proponer como soluciones a problemas críticos. En esta tesis, se propone el desarrollo de un enfoque de trabajo para realizar el análisis y diseño de sistemas discretos, dinámicos y estocásticos. Estos sistemas, se caracterizan por estar íntimamente relacionados con restricciones temporales y concurrentes, que por las características de los modelos desarrollados por UML, no pueden ser representadas; con lo cual es necesario complementar las herramientas con otras, que permitan modelar las características antes mencionadas; una de estas, son las RdP. Una RdP es un lenguaje útil para analizar y modelar formalmente varios sistemas. Recientemente, muchas RdP dedican sus esfuerzos a mejorar y extender el poder expresivo de las RdP. Uno de estos esfuerzos es extender las RdP con conceptos orientados a objetos. Un paradigma orientado a objetos proporciona conceptos excelentes para modelar problemas del mundo real. Los conceptos orientados a objetos nos permiten construir sistemas de software de forma fácil, intuitiva y natural. Se sugieren varias RdP de alto nivel con el concepto de objetos. Estas redes no son totalmente compatibles con el concepto orientado a objetos, por lo que no pueden llamarse RdP orientadas a objetos. La sintaxis formal y la semántica del enfoque propuesto se explican en detalle, adoptando una amplia gama de características del análisis y diseño orientados a objetos. Además, este enfoque es compatible con una variedad de mecanismos de análisis, como los métodos de descomposición, red e incrementales de los sistemas en evolución, el despliegue, a un nivel más bajo de la RdP y el análisis de accesibilidad incremental para los modelos desarrollado. Por último, se demuestra la eficiencia y la utilidad del enfoque desarrollado, a partir de la aplicación del mismo al caso de estudio, esto es, la simulación que explica el comportamiento y la demanda eléctrica residencial de la Ciudad de Salta, a partir de la cantidad y tipo de artefactos presentes en cada vivienda y el comportamiento humano para el encendido y apagado de los mismos.Facultad de Informátic

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