Modeling Biological Networks by Action Languages via Answer Set Programming

Dworschak, S.; Grell, Susanne; Nikiforova, Victoria J.; Schaub, Torsten; Selbig, Joachim

doi:10.1007/s10601-007-9031-y

Cited by 44 publications

(25 citation statements)

References 38 publications

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“…In Baral et al (2004), Dworschak et al (2008), Tran (2006) the authors propose to use ASP-based action languages to model, query or plan the execution of biological systems. Our approach, however, is different from theirs in several aspects.…”

Section: G4mentioning

confidence: 99%

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Modelling gene and protein regulatory networks with Answer Set Programming

Fayruzov¹,

Janssen²,

Vermeir³

et al. 2011

IJDMB

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Recently, many approaches to model regulatory networks have been proposed in the systems biology domain, however the task is far from being solved. In this paper we propose an answer set programming (ASP) based approach to model interaction networks. We build a general ASP framework that describes the network semantics and allows to model specific networks with little effort. ASP provides a rich and flexible toolbox that allows to expand the framework with desired features. In this paper we tune our framework to mimic boolean network behaviour and apply it to model the Budding Yeast and Fission Yeast cell cycle networks. Obtained steady states of these networks correspond to those of the boolean networks.

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Section: G4mentioning

confidence: 99%

“…and explain the results (given that a is active at a certain point, what was the initial state?). This work was further extended by Dworschak et al (2008) who introduced several extra concepts such as allowance and noconcurrency, as well as a special language for biological modeling called C T AID .…”

Section: Related Workmentioning

confidence: 99%

Modelling gene and protein regulatory networks with Answer Set Programming

Fayruzov¹,

Janssen²,

Vermeir³

et al. 2011

IJDMB

View full text Add to dashboard Cite

show abstract

“…and explain the results (given that a is active at a certain point, what was the initial state?). This work was further extended by Dworschak et al in [6] who introduced several extra concepts, such as allowance and noconcurrency, as well as a special language for biological modeling called C T AID .…”

Section: Related Workmentioning

confidence: 99%

“…It is important to emphasize the difference between action language-based approaches [2], [6] and the approach we propose here. The former requires that for each model the whole program is built from scratch and every interaction is defined as its own rule.…”

Section: Related Workmentioning

confidence: 99%

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Modeling Protein Interaction Networks with Answer Set Programming

Fayruzov

Cock

Cornelis

et al. 2009

2009 IEEE International Conference on Bioinformatics and Biomedicine

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Abstract-In this paper we propose the use of answer set programming (ASP) to model protein interaction networks. We argue that this declarative formalism rivals the popular boolean networks in terms of ease of use, while at the same time being more expressive. As we demonstrate for the particular case of a fission yeast network, all information present in a boolean network, as well as relevant background assumptions, can be expressed explicitly in an answer set program. Moreover, readily available answer set solvers can then be used to find the stable states of the network.

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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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Modeling Biological Networks by Action Languages via Answer Set Programming

Cited by 44 publications

References 38 publications

Modelling gene and protein regulatory networks with Answer Set Programming

Modelling gene and protein regulatory networks with Answer Set Programming

Modeling Protein Interaction Networks with Answer Set Programming

Symbolic Representation and Inference of Regulatory Network Structures

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