Model Revision of Boolean Regulatory Networks at Stable State

Gouveia, Filipe; Lynce, Inês; Monteiro, Pedro T.

doi:10.1007/978-3-030-20242-2_9

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…In previous works, we have successfully addressed the model revision problem considering only stable state observations (Gouveia et al, 2018(Gouveia et al, , 2019a. Here, we expand some of the techniques of these works to be able to consider the model dynamics and time-series observations.…”

Section: Approachmentioning

confidence: 99%

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

Gouveia

Lynce

Monteiro

2020

Preprint

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Motivation: Complex cellular processes can be represented by biological regulatory networks. Computational models of such networks have successfully allowed the reprodution of known behaviour and to have a better understanding of the associated cellular processes. However, the construction of these models is still mainly a manual task, and therefore prone to error. Additionally, as new data is acquired, existing models must be revised. Here, we propose a model revision approach of Boolean logical models capable of repairing inconsistent models confronted with time-series observations. Moreover, we account for both synchronous and asynchronous dynamics. Results: The proposed tool is tested on five well known biological models. Different time-series observations are generated, consistent with these models. Then, the models are corrupted with different random changes. The proposed tool is able to repair the majority of the corrupted models, considering the generated time-series observations. Moreover, all the optimal solutions to repair the models are produced. Contact:

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Section: Approachmentioning

confidence: 99%

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

Gouveia

Lynce

Monteiro

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Given a model, and a set of experimental observations, the previously published ASP computes the set of minimum inconsistencies (Gouveia et al, 2018). Then, given the model, the set of experimental observations, and the computed set of inconsistencies, we developed a procedure to try to repair the model using the repair operations in Table 1 (Gouveia et al, 2019).…”

Section: Approachmentioning

confidence: 99%

“…An optimization criterion is also proposed to determine an optimal set of repair operations to render an inconsistent model consistent. We extend a previous work (Gouveia et al, 2019) by tackling the problem of repairing an inconsistent node with multiple reasons of inconsistency, preserving the proposed optimization criteria.…”

Section: Introductionmentioning

confidence: 99%

Revision of Boolean Models of Regulatory Networks Using Stable State Observations

Gouveia

Lynce

Monteiro

2020

Journal of Computational Biology

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Models of biological regulatory networks are essential to understand cellular processes. However, the definition of such models is still mostly manually performed, and consequently prone to error. Moreover, as new experimental data are acquired, models need to be revised and updated. Here, we propose a model revision procedure and associated tool, capable of providing the set of minimal repairs to render a model consistent with a set of experimental observations. We consider four possible repair operations, using a lexicographic optimization criterion, giving preference to function repairs over topological ones. Also, we consider observations at stable state discarding the model dynamics. In this article, we extend our previous work to tackle the problem of repairing nodes with multiple reasons of inconsistency. We evaluate our tool on five publicly available logical models. We perform random changes considering several parameter configurations to assess the tool repairing capabilities. Whenever a model is repaired under the time limit, the tool successfully produces the optimal solutions to repair the model. Instances were generated without the previous limitation to validate this extended approach.

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Model Revision of Boolean Regulatory Networks at Stable State

Cited by 2 publications

References 16 publications

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

Revision of Boolean Models of Regulatory Networks Using Stable State Observations

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