Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs

Melas, Ioannis N.; Samaga, Regina; Alexopoulos, Leonidas G.; Klamt, Steffen

doi:10.1371/journal.pcbi.1003204

Cited by 44 publications

(61 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed ILP formulation is based on the formulation by Melas et al, 12 modified at key points to address the computational complexity of very large (tens of thousands of nodes) signaling networks, and attempts to combine gene expression data upon perturbation with the interrogated drugs with prior knowledge of protein connectivity and transcription regulation, and identify the interactions that appear to be functional based on the data at hand. The resulting/optimized network originates at the drug targets, spans across the signaling level, goes through the layer of transcription factors and terminates at the gene expression level at the deregulated genes.…”

Section: Ilp Formulation -Basic Definitions and Core Formulationmentioning

confidence: 99%

“…However, if n nodes form a positive cycle (a cycle where all reactions are positive), then one node will be able to activate the next all the way around the cycle, without the need for an external perturbation (or an incoming interaction transitively connected to a perturbation). In the formulation by Melas et al, 12 positive feedback cycles had been removed manually before the optimization procedure. However, when very large signaling networks are interrogated, manual curation is not feasible.…”

Section: Ilp Formulation -Removal Of Feedback Loops From the Signalinmentioning

confidence: 99%

“…At the heart of our method is an Integer Linear Programming (ILP) formulation based on the one by Melas et al, 12 modified at key points to address the complexity of large-scale signaling networks. The methodology combines gene expression data with a Prior Knowledge Network (PKN) based on signed and directed causal interactions, such as those that can be curated from the literature, and it identifies subsets of the PKN that appear to be functional based on the data at hand.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

Self Cite

View full text Add to dashboard Cite

Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury. Insight, innovation, integrationIn this manuscript we introduce a novel approach for the identification of signaling pathways that are functional in a specific biological context, by leveraging gene expression data and prior knowledge of protein connectivity. In more detail, we introduce a linear programming formulation to model signal transduction from one node to the next in a Prior Knowledge Network (PKN), and by minimizing the mismatch between model predictions and experimental data, we are able to identify subsets of the PKN that are most probably functional in the specific biological context. More specifically, we address the problem of identifying the modes of action of drugs that have been reported to induce respiratory side effects, based on their gene expression profiles, and subsequently, merge the drug specific pathways together to construct a signaling network that captures the signaling mechanisms underlying Drug Induced Lung Disease (DILD). Moreover, to demonstrate the predictive power and biological relevance of the DILD network, we use it to suggest potential drug repositioning for treating lung injury.

show abstract

Section: Ilp Formulation -Basic Definitions and Core Formulationmentioning

confidence: 99%

Section: Ilp Formulation -Removal Of Feedback Loops From the Signalinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another very important aspect of the LP-based strategies is that those strategies were developed to infer cell-specific signaling pathways with experimental proteomic data [9, 81, 82, 86]. The rational of this type of approaches is that the relationships (states) between a child node and its connected parental nodes were defined by a set of constraints.…”

Section: Several Classical Systemic Modeling Approachesmentioning

confidence: 99%

Mathematical and Computational Modeling in Complex Biological Systems

Yan

et al. 2017

BioMed Research International

View full text Add to dashboard Cite

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

show abstract

“…Later, in [3, 22], Answer Set Programming (ASP) [23] was used to find admissible node labelings adhering to the posed constraints, and optimal repairs to restore sign-consistency were proposed. A related formalism was presented in [17]. Major differences to previous studies were (i) consideration of three node labels (increase, decrease, 0-change), (ii) the representation of the constraints as an integer linear programming (ILP) problem, and (iii) the introduction of new repair operations minimizing inconsistencies between the IG structure and the experiments.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

Self Cite

View full text Add to dashboard Cite

BackgroundA 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.ResultsWe present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles. We furthermore introduce a new constraint to filter undesired model behaviors induced by positive feedback loops. Finally, we generalize the way predictions can be made by the sign consistency approach. In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach. We then demonstrate the applicability of our framework by confronting a large-scale gene regulatory network model of Escherichia coli with high-throughput transcriptomic measurements.ConclusionOverall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networksElectronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0733-7) contains supplementary material, which is available to authorized users.

show abstract

Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs

Cited by 44 publications

References 29 publications

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Mathematical and Computational Modeling in Complex Biological Systems

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

Contact Info

Product

Resources

About