Paper abstracts in systems biology (A. Siegel)

Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming

M. Gebser, C. Guziolowski, M. Ivanchev, T. Schaub, A. Siegel, P. Veber and S. Thiele KR'2010 - International Conference on the Principles of Knowledge Representation and Reasoning

We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we propose a range of different operations for altering experimental data and/or a biological network in order to reestablish their mutual consistency—an indispensable prerequisite for automated prediction. For accomplishing repair and prediction, we take advantage of the distinguished modeling and reasoning capacities of Answer Set Programming. We validate our framework by an empirical study on the widely investigated organism Escherichia coli.

Model of cap-dependent translation initiation in sea urchin. A step towards the eukaryotic translation regulation network

R. Bellé, S. Prigent, A. Siegel and P. Cormier, Molecular Reproduction and Development 77(3), 2010, 257-264

The large and rapid increase in the rate of protein synthesis following fertilization of the sea urchin egg has long been a paradigm of translational control, an important component of the regulation of gene expression in cells. This translational upregulation is linked to physiological changes that occur upon fertilization and is necessary for entry into first cell division cycle. Accumulated knowledge on capdependent initiation of translation makes it suited and timely to start integrating the data into a system view of biological functions. Using a programming environment for system biology coupled with model validation (named Biocham), we have built an integrative model for cap-dependent initiation of translation. The model is described by abstract rules. It contains 51 reactions involved in 74 molecular complexes. The model proved to be coherent with existing knowledge by using queries based on computational tree logic (CTL) as well as Boolean simulations. The model could simulate the change in translation occurring at fertilization in the sea urchin model. It could also be coupled with an existing model designed for cell-cycle control. Therefore, the cap-dependent translation initiation model can be considered a first step towards the eukaryotic translation regulation network.

A minimal model for hepatic fatty acid balance during fasting: Application to PPAR alpha-deficient mice ,

P. Blavy, F. Gondret, H. Guillou, S. Lagarrigue, P.G.P. Martin ,J. van Milgen, O. Radulescu and A. Siegel. Journal of Theoretical Biology, 261 (2009), pp. 266-278

The purpose of this study is to identify the hierarchy of importance amongst pathways involved in fatty acid (FA) metabolism and their regulators in the control of hepatic FA composition. A modeling approach was applied to experimental data obtained during fasting in PPAR alpha knockout (KO) mice and wild-type mice. A step-by-step procedure was used in which every simple model was completed by additional pathways until the model fitted correctly the measured quantities of FA in the liver. The resulting model included FA uptake by the liver, FA oxidation, elongation and desaturation of FA, which were found active in both genotypes during fasting. From the model analysis we concluded that PPARa had a strong effect on FA oxidation. There were no indications that this effect changes during the fasting period, and it was thus considered to be constant. In PPARalpha KO mice, FA uptake was identified as the main pathway responsible for FA variation in the liver. The models showed that FA were oxidized at a constant and small rate, whereas desaturation of FA also occurred during fasting. The latter observation was rather unexpected, but was confirmed experimentally by the measurement of delta-6-desaturase mRNA using real-time quantitative PCR(QPCR). These results confirm that mathematical models can be a useful tool in identifying new biological hypotheses and nutritional routes in metabolism.

BioQuali Cytoscape plugin: analysing the global consistency of regulatory networks,

C. Guziolowski, A. Bourdé, F. Moreews and A. Siegel, BMC genomics, 2009, 10:244

Background: A usual method to analyse regulatory networks is the in silico simulation of fluctuations in network components when a network is perturbed. Nevertheless, confronting experimental data with a regulatory network faces many difficulties such as the incomplete state-of-art of the regulatory knowledge, the large-scale dimension of the regulatory models, the heterogeneity in the available data, and the repeatedly incorrect assumption that mRNA expression is correlated to protein activity.
Results: We introduce a plugin for the Cytoscape environment to facilitate automatic reasoning on regulatory networks. The BioQuali plugin enhances user-friendly conversions of regulatory networks (including reference databases) into signed directed graphs. BioQuali performs automatic global reasoning in order to decide which products in the network need to be up or down regulated (active or inactive) to globally explain experimental data. It highlights incomplete regions in the network, meaning that gene expression levels do not globally correlate with existing knowledge on regulation carried by the topology of the network.
Conclusions: The BioQuali plugin facilitates the in silico exploration of large-scale regulatory networks by combining the user-friendly tools of Cytoscape environment with high-performance automatic reasoning algorithms. As a main feature, the plugin guides further investigations on a system by highlighting regions in the network that are not accurately described and deserve specific studies.

Curating a large-scale regulatory network by evaluating its consistency with expression datasets

C. Guziolowski, J. Gruel, O. Radulescu and A. Siegel, CIBB 2008: Computational Intelligence Methods for Bioinformatics and Biostatistics - Selected revised papers Lecture Notes in Computer Sciences LNCS, Springer-Verlag, volume 5488, p.144-155, 2009.

The analysis of large-scale regulatory models using data issued from genome-scale high-throughput experimental techniques is an actual challenge in the systems biology field. This kind of analysis faces three common problems: the size of the model, the uncertainty in the expression datasets, and the heterogeneity of the data. On that account,
we propose a method that analyses large-scale networks with small – but reliable – expression datasets. Our method relates regulatory knowledge with heterogeneous expression datasets using a simple consistency rule. If a global consistency is found, we predict the changes in gene expression or protein activity of some components of the network. When the whole model is inconsistent, we highlight regions in the network where the regulatory knowledge is incomplete. Confronting our predictions with mRNA expression experiments allows us to determine the missing post-transcriptional interactions of our model.We tested this approach with the transcriptional network of E. coli.

Inferring the role of transcription factors in regulatory networks

P Veber, C Guziolowski, M Le Borgne, O Radulescu, and A Siegel, BMC BioInformatics 9, 2008:228

Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple organisms up to higher eukaryotes. Admittedly, a key ingredient in developing a reconstruction method is its ability to integrate heterogeneous sources of information, as well as to comply with practical observability issues: measurements can be scarce or noisy. In this work, we show how to combine a network of genetic regulations with a set of expression profiles, in order to infer the functional effect of the regulations, as inducer or repressor. Our approach is based on a consistency rule between a network and the signs of variation given by expression arrays.
We evaluate our approach in several settings of increasing complexity. First, we generate artificial expression data on a transcriptional network of \emph{E.~coli} extracted from the literature (1529 nodes and 3802 edges), and we estimate that 30\% of the regulations can be annotated with about 30 profiles. We additionally prove that at most 40.8\% of the network can be inferred using our approach. Second, we use this network in order to validate the predictions obtained with a compendium of real expression profiles. We describe a filtering algorithm that generates particularly reliable predictions. Finally, we apply our inference approach to {\em S.~cerevisiae } transcriptional network (2419 nodes and 4344 interactions), by combining ChIP-chip data and 15 expression profiles . We are able to detect and isolate inconsistencies between the expression profiles and a significant portion of the model (15\% of all the interactions). In addition, we report predictions for 14.5\% of all interactions.
Our approach does not require accurate expression levels nor times series. Nevertheless, we show on both data, real and artificial, that a relatively small number of perturbation experiments are enough to determine a significant portion of regulatory effects. This is a key practical asset compared to statistical methods for network reconstruction. We demonstrate that our approach is able to provide accurate predictions, even when the network is incomplete and the data is noisy.

Optimiser un plan d'expérience à partir de modèles qualitatifs?

A. Siegel, C. Guziolowski, P. Veber, O. Radulescu, M. Le Borgne BioFutur (275), 2007, 27-31

Un biologiste modifie la concentration d'une entrée d’un système initialement stable, et attend qu’il se stabilise à nouveau. On observe un déplacement d'équilibre sous l'effet d'une perturbation. Les techniques de production de données en masse renseignent sur ces déplacements d'équilibre mais des observations se révèlent plus utiles que d'autres. Nous discutons et évaluons l'intérêt que présente l'observation d'un composant par rapport à un autre.

Checking Consistency Between Expression Data and Large Scale Regulatory Networks: A Case Study

C Guziolowski, P Veber, M Le Borgne, O Radulescu, and A Siegel Journal of Biological Physics and Chemistry (7) 2007, 37-43

We proposed in previous articles a qualitative approach to check the compatibility between a model of interactions and gene expression data. The purpose of the present work is to validate this methodology on a real-size setting. We study the response of \emph{E.coli} regulatory network to nutritional stress, and compare it to publicly available DNA microarray experiments. We show how the incompatibilities we found reveal missing interactions in the network, as well as observations in contradiction with available literature.

Complex Qualitative Models in Biology: a new approach

P. Veber, M. Le Borgne, A. Siegel, S. Lagarrique, O. Radulescu Complexus 2, 2004/2005, pp. 140-151 Revised paper from ECCS, Paris, November 2005

We advocate the use of qualitative models in the analysis of large biological systems. We show how qualitative
models are linked to theoretical differential models and practical graphical models of biological networks. A new technique for analyzing qualitative models is introduced, which is based on an efficient representation of
qualitative systems. As shown through several applications, this representation is a relevant tool for the understanding and testing of large and complex biological networks.

Topology and linear response of interaction networks in molecular biology

Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks