Revision of Boolean Models of Regulatory Networks Using Stable State Observations

Abstract

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.

Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells

“…MaBoSS can be used in a routine with tools that perform mutant analyses (cf. PINT [80] ) or more formal stable state analyses [81] , [82] , [83] .…”

Automated Model Refinement Using Perturbation-Observation Pairs

“…The previous works most relevant to our method involve genetic-algorithm-based inference of a Boolean model based on a directed network that integrates prior knowledge of interactions and regulatory relationships [21][22][23][24][25]36] These algorithms take as input information a compendium of steady state values of all the nodes in the unperturbed system, complemented by the steady state values obtained for perturbations. Another type of relevant prior work develops answer-set-programming methods to revise an existing Boolean model to better align with steady state or time course measurements of the unperturbed system [37,38]. We summarize in S2 Text the goals and use cases of relevant previous algorithms.…”

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

“…Moreover, results show that in most cases consistent functions are actually found with the previous search method. For these reasons, although we give the option to the user to force an exhaustive search, by default we only do this partial search as described in Gouveia et al (2019b). Only when in the presence of inconsistent nodes with both type of inconsistencies, is a more exhaustive search automatically performed.…”