Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

Gebser, Martin; Schaub, Torsten; Thiele, Sven; Usadel, Björn; Veber, Philippe

doi:10.1007/978-3-540-89982-2_19

Cited by 62 publications

(81 citation statements)

References 38 publications

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“…The approach that is arguably closest to ours is the method introduced in [11]–[13]. This framework is also based on IG and uses a similar consistency rule as we did herein.…”

Section: Discussionmentioning

confidence: 99%

“…This extension seems to be essential, for example, when perturbation of a node cannot affect another node simply because (in the true topology) a path from to does not exist. Second, the four basic problem formulations presented herein go beyond the techniques introduced in [11]–[13]. In particular, the training of the topology, that is, the identification of inactive or missing interactions based on a library of stimulus-response experiments, was not considered in these works.…”

Section: Discussionmentioning

confidence: 99%

“…However, before such an analysis can be conducted one has to choose an appropriate modeling formalism. Common approaches used for modeling signal transduction networks are based on graphs [12], [13], [17], [18], Bayesian networks [15], some form of logical modeling including Boolean or constrained fuzzy logic [17], [19], [20], hybrid intelligent systems [18], [19], [21]–[23], or ordinary differential equations (ODEs) [24]–[26].…”

Section: Introductionmentioning

confidence: 99%

“…A related class of methods for detecting discrepancies between IG topology and experimental data relies on the sign consistency rule [11]–[13]. The key idea is that, in a steady-state shift experiment, the direction of change of the state of a node must be explainable by the direction of change of at least one of its predecessor nodes (except for the directly perturbed node(s)).…”

Section: Introductionmentioning

confidence: 99%

“…The sign consistency rule gives rise to constraints on the possible patterns of “ups and downs” of the nodes' activation levels in a given IG. These constraints can be encoded, for example, by Answer Set Programming [13]. Confronting these constraints with experimental data may then lead to the detection of topological inconsistencies, namely if no sign pattern complying with the given measurements and perturbations can be found [11]–[13].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2013

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Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.

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“…The approach that is arguably closest to ours is the method introduced in [11]–[13]. This framework is also based on IG and uses a similar consistency rule as we did herein.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2013

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Symbolic Representation and Inference of Regulatory Network Structures

Maimari¹,

Broda²,

Kakas³

et al. 2014

Logical Modeling of Biological Systems

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Recent results have demonstrated the usefulness of symbolic approaches for addressing various problems in systems biology. One of the fundamental challenges in systems biology is the extraction of integrated signaling-transcriptional networks from experimental data. In this chapter, we present a general logic-based framework, called Abductive Regulatory Network Inference (ARNI), where we formalize the network extraction problem as an abductive inference problem. A general logical model is provided that integrates prior knowledge on molecular interactions and other information for capturing signal-propagation principles and compatibility with experimental data. Solutions to our abductive inference problem define signed-directed networks that explain how genes are affected during the experiments. Using in-silico datasets provided by the dialogue for reverse engineering assessments and methods (DREAM)) consortium, we demonstrate the improved predictive power and complexity of our inferred network topologies compared with those generated by other non-symbolic inference approaches, showing the suitability of our approach for computing complete realistic networks. We also explore how the improved expressiveness together with the modularity and flexibility of the logic-based nature of our approach can support automated scientific discovery where the validity of hypothesized biological ideas can be examined and tested outside the laboratory.

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A Guided Tour of Artificial Intelligence Research

2020

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Systems Biology aims at elucidating the high-level functions of the cell from their biochemical basis at the molecular level. A lot of work has been done for collecting genomic and post-genomic data, making them available in databases and ontologies, building dynamical models of cell metabolism, signalling, division cycle, apoptosis, and publishing them in model repositories. In this chapter we review different applications of AI to biological systems modelling. We focus on cell processes at the unicellular level which constitutes most of the work achieved in the last two decades in the domain of Systems Biology. We show how rule-based languages and logical methods have played an important role in the study of molecular interaction networks and of their emergent properties responsible for cell behaviours. In particular, we present some results obtained with SAT and Constraint Logic Programming solvers for the static analysis of large interaction networks, with Model-Checking and Evolutionary Algorithms for the analysis and synthesis of dynamical models, and with Machine Learning techniques for the current challenges of infering mechanistic models from temporal data and automating the design of biological experiments.

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Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

Cited by 62 publications

References 38 publications

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

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

Symbolic Representation and Inference of Regulatory Network Structures

A Guided Tour of Artificial Intelligence Research

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