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ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

ABSTRACT: This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors.We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature.The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.

SUBMITTER: Ben Abdallah E

PROVIDER: S-EPMC5557630 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BackgroundBoolean models are increasingly used to study biological signaling networks. In a Boolean network, nodes represent biological entities such as genes, proteins or protein complexes, and edges indicate activating or inhibiting influences of one node towards another. Depending on the input of activators or inhibitors, Boolean networks categorize nodes as either active or inactive. The formalism is appealing because for many biological relationships, we lack quantitative information about binding constants or kinetic parameters and can only rely on a qualitative description of the type "A activates (or inhibits) B". A central aim of Boolean network analysis is the determination of attractors (steady states and/or cycles). This problem is known to be computationally complex, its most important parameter being the number of network nodes. Various algorithms tackle it with considerable success. In this paper we present an algorithm, which extends the size of analyzable networks thanks to simple and intuitive arguments.ResultsWe present lnet, a software package which, in fully asynchronous updating mode and without any network reduction, detects the fixed states of Boolean networks with up to 150 nodes and a good part of any present cycles for networks with up to half the above number of nodes. The algorithm goes through a complete enumeration of the states of appropriately selected subspaces of the entire network state space. The size of these relevant subspaces is small compared to the full network state space, allowing the analysis of large networks. The subspaces scanned for the analyses of cycles are larger, reducing the size of accessible networks. Importantly, inherent in cycle detection is a classification scheme based on the number of non-frozen nodes of the cycle member states, with cycles characterized by fewer non-frozen nodes being easier to detect. It is further argued that these detectable cycles are also the biologically more important ones. Furthermore, lnet also provides standard Boolean analysis features such as node loop detection.Conclusionslnet is a software package that facilitates the analysis of large Boolean networks. Its intuitive approach helps to better understand the network in question.

Project description:BackgroundClinical Record (CR) writing is a fundamental skill for healthcare professionals, but the best e-learning methods for teaching it remain unstudied. Therefore, we investigated speech therapy students' differences in the quality production of CR at the placement and their experience after following asynchronous or synchronous e-learning courses.MethodsA multi-method randomised controlled trial. Fifty speech therapist students were equally and randomly divided into two groups attending asynchronous or synchronous e-learning classes to learn how to write a CR. The quality of the CR was tested through an ad hoc checklist (score 0-32) and the groups' scores were compared. The assessors and the statistician were blinded to students' group assignment. Students' experience was assessed through semi-structured interviews analysed with a reflexive thematic analysis.ResultsNo score differences between the two groups were found (Cohen's d = 0.1; 95% Confidence Interval [-0.6; 0.7]). Four themes were generated: (1) 'Different Forms of Learning Interaction', as the synchronous group reported a positive experience with being fed back immediately by the lecturer, whereas the asynchronous group reported that pushing back the question time allows for reflecting more on the learning experience; (2) 'Different Ways to Manage the Time', as the synchronous group had to stick to the lecturer's schedule and the asynchronous group felt the possibility to manage its time; (3) 'To Be or Not To Be (Present)?' due to the different experiences of having (or not) the lecturer in front of them; (4) 'Inspiring Relationships With The Peers', where both groups preferred a peer-to-peer discussion instead of contacting the lecturer.DiscussionAsynchronous and synchronous e-learning courses appeared equally effective in teaching CR writing. However, students perceive and experience these methods differently. The choice or blend of these methods should be based on students' needs and preferences, teacher input, as well as organisational requirements rather than solely on students' attended performance.

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ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

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