Applying dynamic Bayesian networks to perturbed gene expression data

BACKGROUND: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments. RESULTS: We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed. CONCLUSION: We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough

Evolutionary Dynamics in Gene Networks and Inference Algorithms

Dynamical interactions among sets of genes (and their products) regulate developmental processes and some dynamical diseases, like cancer. Gene regulatory networks (GRNs) are directed networks that define interactions (links) among different genes/proteins involved in such processes. Genetic regulation can be modified during the time course of the process, which may imply changes in the nodes activity that leads the system from a specific state to a different one at a later time (dynamics). How the GRN modifies its topology, to properly drive a developmental process, and how this regulation was acquired across evolution are questions that the evolutionary dynamics of gene networks tackles. In the present work we review important methodology in the field and highlight the combination of these methods with evolutionary algorithms. In recent years, this combination has become a powerful tool to fit models with the increasingly available experimental data.Junta de Andalucía FQM-12

Comparison of evolutionary algorithms in gene regulatory network model inference

Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very di±cult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insu±cient. Results: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and o®er a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Conclusions: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identi¯ed and a platform for development of appropriate model formalisms is established

JCell : a Java framework for inferring genetic networks

JCell is a framework for reconstructing and simulating genetic networks in the field of molecular biology. It is completely implemented in Java. The main goal of JCell is to gain deep insights of molecular processes within a cell or tissue under various conditions such as drug concentrations or pathogenic mutations. This question has recently become a major area of research in the field of bioinformatics, because understanding the regulating dependencies enables new therapies of diseases like cancer or Alzheimer. To address the mentioned inference problem, several mathematical models and algorithms have been developed and implemented, which try to infer genetic relationships from genomic experiment data. The program consists of a modular structure, which enables users to utilize the framework also in other research areas such as metabolic pathway reconstruction, signalling cascade analysis or general biochemical processes. Further on, JCell can also be used in other contexts to identify dynamic systems from time series data such as financial applications or engineering problems. Usability was always the primary focus during development, so that even users without a strong computer science background are able to use the program. Another focus was the ability of JCell to natively import as much file formats as possible to be compatible with the most commonly used analysis tools. Due to the usage of the programming language Java, the framework is platform independent and thus able to work on most hardware/software systems. This is especially important for those research facilities where no expensive hardware can purchased and where no restrictions for the used operating systems can be implied. Further more, the framework is open to public development and new modules can be easily implemented.JCell ist ein komplett in Java realisiertes Framework zur Rekonstruktion und Simulation von genetischen Netzwerken in verschiedenen Bereichen der Molekularbiologie. Ziel ist die eingehende Untersuchung von Abläufen innerhalb einer Zelle oder eines Gewebetyps bei gleichzeitiger Zugabe von Wirkstoffen oder im Falle von krankhafter Entartung. Diese Fragestellung ist zur Zeit eines der wichtigsten Themengebiete der Bioinformatik, da das Verständnis von genetischer Regulation tiefgreifende Möglichkeiten der Diagnostik und Therapie von Krankheiten wie Krebs oder Alzheimer eröffnet. Zur Lösung des so genannten Netzwerk-Inferenzproblems wurden verschiedene Algorithmen und mathematische Modelle implementiert, die aus gegebenen genomischen Experimentdaten versuchen, regulatorische Interaktionen zu rekonstruieren. Da die gewählte Programmstruktur modular aufgebaut ist, wurden im Laufe der Entwicklung weitere Einsatzgebiete erschlossen. So kann JCell nun auch in anderen Gebieten der Systembiologie, wie zum Beispiel der Forschung im Bereich metabolischer Systeme und der Rekonstruktion von biochemischen Signalwegen innerhalb einer Zelle, eingesetzt werden. Des Weiteren liegen Anfragen von Biotech-Firmen vor, die dynamische Prozesse in biotechnologischen Anlagen besser verstehen wollen. Bei der Entwicklung war stets die einfache Benutzbarkeit der Applikation das primäre Ziel, damit auch Computer-Laien in der Lage sind, das Programm zu bedienen. Ein weiteres Augenmerk lag auf der Implementierung von Methoden zum Einlesen verschiedenster Dateiformate, sodass die gängigsten Analysetools für Genomexperimente unterstützt werden. Durch Verwendung der Programmiersprache Java ist eine weitreichende Plattformunabhängigkeit gewährleistet, sodass JCell auf den meisten Rechnerarchitekturen läuft. Dies hat den Vorteil, dass Anwender keine spezielle Hardware bereitstellen müssen und auch keinerlei Einschränkungen bei der Auswahl eines Betriebssystems haben. Daneben bietet Java noch den Vorteil, dass fremde Entwickler schnell eigene Module in das bestehende Framework einbinden können, was besonders im Hinblick auf die Open-Source-Verfügbarkeit eine wichtige Rolle spielt

A survey of models for inference of gene regulatory networks

In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper