Modeling Protein Interaction Networks with Answer Set Programming

Fayruzov, Timur; Cock, Martine De; Cornelis, Chris; Vermeir, Dirk

doi:10.1109/bibm.2009.9

Cited by 14 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the capability of solvers has increased such that ASP started to be applied to solve hard combinatorial problems arising in bioinformatics and systems biology. Applications include expanding metabolic networks (Schaub and Thiele, 2009), repairing inconsistencies in gene regulatory networks (Gebser et al , 2010), modeling the dynamics of regulatory networks (Fayruzov et al , 2009), inferring functional dependencies from time-series data, (Durzinsky et al , 2011), integrating gene expression with pathway information (Papatheodorou et al , 2012) and analyzing the dynamics of reactions networks (Ray and Soh, 2012). …”

Section: Methodsmentioning

confidence: 99%

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

et al. 2013

View full text Add to dashboard Cite

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. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions.Results: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input–output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design.Availability: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/.Supplementary information: Supplementary materials are available at Bioinformatics online.Contact: santiago.videla@irisa.fr

show abstract

Section: Methodsmentioning

confidence: 99%

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

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Gebser et al have addressed the problem consisting of detecting inconsistencies and repairing in large biological networks [24,22]. Fayruzov et al have used ASP to represent the dynamics in Boolean networks and find their attractors [15,16]. Ray et al have integrated numerical and logical information in order to find the most likely states of a biological system under various constraints [48].…”

Section: Related Workmentioning

confidence: 99%

Learning Boolean logic models of signaling networks with ASP

Videla

Guziolowski

Eduati

et al. 2015

Theoretical Computer Science

View full text Add to dashboard Cite

International audienceBoolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in a separate appendix

show abstract

“…In particular, we propose to represent gene and protein regulatory networks by answer set programs, as an extension of our previous work presented in [4]. We extend this framework with new functionalities and provide a more efficient implementation that allows to…”

Section: Introductionmentioning

confidence: 99%

Extending boolean regulatory network models with answer set programming

Fayruzov

Janssen

Cornelis

et al. 2010

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

Self Cite

View full text Add to dashboard Cite

Abstract-Because of their simplicity, boolean networks are a popular formalism to model gene regulatory networks. However, they have their limitations, including their inability to formally and unambiguously define network behaviour, and their lack of the possibility to model meta interactions, i.e., interactions that target other interactions. In this paper we develop an answer set programming (ASP) framework that supports threshold boolean network semantics and extends it with the capability to model meta interactions. The framework is easy to use but sufficiently flexible to express intricate interactions that go beyond threshold network semantics as we illustrate with an example of a Mammalian cell cycle network. Moreover, readily available answer set solvers can be used to find the steady states of the network.

show abstract

Modeling Protein Interaction Networks with Answer Set Programming

Cited by 14 publications

References 12 publications

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

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

Learning Boolean logic models of signaling networks with ASP

Extending boolean regulatory network models with answer set programming

Contact Info

Product

Resources

About