Predicting weighted unobserved nodes in a regulatory network using answer set programming.

Background: The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network.

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma.

Background: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study.

Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data.

Protein signaling networks are static views of dynamic processes where proteins go through many biochemical modifications such as ubiquitination and phosphorylation to propagate signals that regulate cells and can act as feed-back systems. Understanding the precise mechanisms underlying protein interactions can elucidate how signaling and cell cycle progression occur within cells in different diseases such as cancer. Large-scale protein signaling networks contain an important number of experimentally verified protein relations but lack the capability to predict the outcomes of the system, and therefore to be trained with respect to experimental measurements.

Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification.

The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step.

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.

Background: The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks.

Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming.

Background: During the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment.

Therapeutic target discovery using Boolean network attractors: improvements of kali.

In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modelling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are (i) the possibility to work on asynchronous Boolean networks, (ii) a finer assessment of therapeutic targets and (iii) the possibility to use multivalued logic.

Logic programming reveals alteration of key transcription factors in multiple myeloma.

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP.

Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming.

Background: Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour.

caspo: a toolbox for automated reasoning on the response of logical signaling networks families.

Summary: We introduce the caspo toolbox, a python package implementing a workflow for reasoning on logical networks families. Our software allows researchers to (i) a family of logical networks derived from a given topology and explaining the experimental response to various perturbations; (ii) all logical networks in a given family by their input-output behaviors; (iii) the response of the system to every possible perturbation based on the ensemble of predictions; (iv) new experimental perturbations to discriminate among a family of logical networks; and (v) a family of logical networks by finding all interventions strategies forcing a set of targets into a desired steady state.

Availability And Implementation: caspo is open-source software distributed under the GPLv3 license.

Deciphering transcriptional regulations coordinating the response to environmental changes.

Background: Gene co-expression evidenced as a response to environmental changes has shown that transcriptional activity is coordinated, which pinpoints the role of transcriptional regulatory networks (TRNs). Nevertheless, the prediction of TRNs based on the affinity of transcription factors (TFs) with binding sites (BSs) generally produces an over-estimation of the observable TF/BS relations within the network and therefore many of the predicted relations are spurious.

Results: We present LOMBARDE, a bioinformatics method that extracts from a TRN determined from a set of predicted TF/BS affinities a subnetwork explaining a given set of observed co-expressions by choosing the TFs and BSs most likely to be involved in the co-regulation.

Directed random walks and constraint programming reveal active pathways in hepatocyte growth factor signaling.

An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways.

Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies.

Background: A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions.

Designing Experiments to Discriminate Families of Logic Models.

Logic models of signaling pathways are a promising way of building effective in silico functional models of a cell, in particular of signaling pathways. The automated learning of Boolean logic models describing signaling pathways can be achieved by training to phosphoproteomics data, which is particularly useful if it is measured upon different combinations of perturbations in a high-throughput fashion. However, in practice, the number and type of allowed perturbations are not exhaustive.

Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models.

Automatic generation of causal networks linking growth factor stimuli to functional cell state changes.

Despite the increasing number of growth factor-related signalling networks, their lack of logical and causal connection to factual changes in cell states frequently impairs the functional interpretation of microarray data. We present a novel method enabling the automatic inference of causal multi-layer networks from such data, allowing the functional interpretation of growth factor stimulation experiments using pathway databases. Our environment of evaluation was hepatocyte growth factor-stimulated cell migration and proliferation in a keratinocyte-fibroblast co-culture.

Designing logical rules to model the response of biomolecular networks with complex interactions: an application to cancer modeling.

We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational interactions, we consider two types of nodes in the network, corresponding to proteins and to RNA. Microarray data provides very lacunar information for such types of networks because protein nodes, although needed in the model, are not observed.

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

Background: The method most commonly used 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 entails many difficulties, such as the incomplete state-of-art of regulatory knowledge, the large-scale of regulatory models, heterogeneity in the available data and the sometimes violated assumption that mRNA expression is correlated to protein activity.

Results: We have developed a plugin for the Cytoscape environment, designed to facilitate automatic reasoning on regulatory networks.

Inferring the role of transcription factors in regulatory networks.

Background: 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.

Mapping sequences by parts.

Background: We present the N-map method, a pairwise and asymmetrical approach which allows us to compare sequences by taking into account evolutionary events that produce shuffled, reversed or repeated elements. Basically, the optimal N-map of a sequence s over a sequence t is the best way of partitioning the first sequence into N parts and placing them, possibly complementary reversed, over the second sequence in order to maximize the sum of their gapless alignment scores.

Results: We introduce an algorithm computing an optimal N-map with time complexity O (|s| x |t| x N) using O (|s| x |t| x N) memory space.

JUICE: a data management system that facilitates the analysis of large volumes of information in an EST project workflow.

Background: Expressed sequence tag (EST) analyses provide a rapid and economical means to identify candidate genes that may be involved in a particular biological process. These ESTs are useful in many Functional Genomics studies. However, the large quantity and complexity of the data generated during an EST sequencing project can make the analysis of this information a daunting task.