Complex Qualitative Models in Biology: A New Approach

Veber, Philippe; Borgne, Michel Le; Siegel, Anne; Lagarrigue, Sandrine; Radulescu, Ovidiu

doi:10.1159/000093686

Cited by 14 publications

(21 citation statements)

References 48 publications

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“…Note that, if the profile provides us with a sign for each vertex of the influence graph, the task can be accomplished simply by checking whether each non-input vertex receives at least one influence matching its variation. However, as soon as the experimental profile has missing values (which is very likely in practice), the problem becomes NP-hard (Veber et al 2004). In fact, a Boolean satisfiability problem over clauses C 1 , .…”

Section: Checking Consistencymentioning

confidence: 99%

Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

Gebser

Schaub

Thiele

et al. 2008

Lecture Notes in Computer Science

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We introduce an approach to detecting inconsistencies in large biological networks by using Answer Set Programming (ASP). To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on ASP to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.

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Section: Checking Consistencymentioning

confidence: 99%

Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

Gebser

Schaub

Thiele

et al. 2008

Lecture Notes in Computer Science

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“…Qualitative Reasoning is in general computationally intensive, and the complexity can be NP-complete (Dormoy, 1988;Veber et al, 2006). The rich representation of inequality relations in Garp3, although powerful, causes the inequality reasoning inferences to be vulnerable to combinatorial explosion.…”

Section: Computational Efficiencymentioning

confidence: 99%

Garp3 — Workbench for qualitative modelling and simulation

Bredeweg

Linnebank

Bouwer

et al. 2009

Ecological Informatics

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a b s t r a c t a r t i c l e i n f oEasy to use workbenches for Qualitative Reasoning and modelling have been virtually nonexistent. This has a limiting effect on the use of this Artificial Intelligence technology and its uptake by a larger audience. We present Garp3, a user-friendly workbench that allows modellers to build, simulate, and inspect qualitative models of system behaviour. The workbench employs diagrammatic representations for users to interact with model content and simulation results, and provides seamless interoperability between the different modes of use. Domain experts can use Garp3 to create conceptual models in situations where numerical information is sparse or unavailable, or when they want to formalise their conceptual understanding of how systems behave. Garp3 can be applied to stakeholder management or dissemination activities to illustrate and explain phenomena, and facilitate discussion among participants. The workbench can also be used in formal education to have learners express concepts, or interact with existing models, and support them in developing their understanding of 'how things work'. Garp3 incorporates a range of techniques from Artificial Intelligence known as knowledge-based techniques. The main goal of this paper is to present the representation and reasoning methods of these techniques as they have been developed and fine-tuned within the Garp3 workbench. The focus hereby is on the symbolic, non-numerical calculations that are required to generate the state-graph of a system's behaviour efficiently, while taking into account that users need to be able to track and understand this reasoning, both in terms of the end result and the intermediate results it delivers.

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“…Finally, let us mention that we provided in [4] an efficient representation of qualitative systems, leading to effective algorithms, some of them could be used to get further insights into the model under study. We shall see in the next section, that these algorithms are able to deal with large scale networks.…”

Section: Analyzing a Network: A Simple Examplementioning

confidence: 99%

“…Following this line of research, we showed in previous papers (see [2,3,4]) how to define and to check consistency between experimental measurements and a graphical regulatory model formalized as an interaction graph. The purpose of the present work is to validate this methodology on a realsize setting.…”

Section: Introductionmentioning

confidence: 98%

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Checking consistency between expression data and large scale regulatory networks: a case study

Guziolowski¹

2007

JBPC

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We proposed in previous articles a qualitative approach to check the compatibility between a model of interactions and gene expression data. The purpose of the present work is to validate this methodology on a real-size setting. We study the response of E.coli regulatory network to nutritional stress, and compare it to publicly available DNA microarray experiments. We show how the incompatibilities we found reveal missing interactions in the network, as well as observations in contradiction with available literature.

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Complex Qualitative Models in Biology: A New Approach

Cited by 14 publications

References 48 publications

Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

Garp3 — Workbench for qualitative modelling and simulation

Checking consistency between expression data and large scale regulatory networks: a case study

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