Annotations for Rule-Based Models

The chapter reviews the syntax to store machine-readable annotations and describes the mapping between rule-based modelling entities (e.g., agents and rules) and these annotations. In particular, we review an annotation framework and the associated guidelines for annotating rule-based models of molecular interactions, encoded in the commonly used Kappa and BioNetGen languages, and present prototypes that can be used to extract and query the annotations. An ontology is used to annotate models and facilitate their description

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization

Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo. The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf. Contact: or [email protected]

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization

Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general

Rule-based epidemic models

Rule-based models generalise reaction-based models with reagents that have internal state and may be bound together to form complexes, as in chemistry. An important class of system that would be intractable if expressed as reactions or ordinary differential equations can be efficiently simulated when expressed as rules. In this paper we demonstrate the utility of the rule-based approach for epidemiological modelling presenting a suite of seven models illustrating the spread of infectious disease under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing and contact tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. Rule-based models allow to combine transparent modelling approach with scalability and compositionality and therefore can facilitate the study of aspects of infectious disease propagation in a richer context than would otherwise be feasible

Developing a framework for semi-automated rule-based modelling for neuroscience research

Dynamic modelling has significantly improved our understanding of the complex molecular mechanisms underpinning neurobiological processes. The detailed mechanistic insights these models offer depend on the availability of a diverse range of experimental observations. Despite the huge increase in biomolecular data generation from novel high-throughput technologies and extensive research in bioinformatics and dynamical modelling, efficient creation of accurate dynamical models remains highly challenging. To study this problem, three perspectives are considered: comparison of modelling methods, prioritisation of results and analysis of primary data sets. Firstly, I compare two models of the DARPP-32 signalling network: a classically defined model with ordinary differential equations (ODE) and its equivalent, defined using a novel rule-based (RB) paradigm. The RB model recapitulates the results of the ODE model, but offers a more expressive and flexible syntax that can efficiently handle the “combinatorial complexity” commonly found in signalling networks, and allows ready access to fine-grain details of the emerging system. RB modelling is particularly well suited to encoding protein-centred features such as domain information and post-translational modification sites. Secondly, I propose a new pipeline for prioritisation of molecular species that arise during model simulation using a recently developed algorithm based on multivariate mutual information (CorEx) coupled with global sensitivity analysis (GSA) using the RKappa package. To efficiently evaluate the importance of parameters, Hilber-Schmidt Independence Criterion (HSIC)-based indices are aggregated into a weighted network that allows compact analysis of the model across conditions. Finally, I describe an approach for the development of disease-specific dynamical models using genes known to be associated with Attention Deficit Hyperactivity Disorder (ADHD) as an exemplar. Candidate disease genes are mapped to a selection of datasets that are potentially relevant to the modelling process (e.g. interactions between proteins and domains, protein-domain and kinase-substrates mappings) and these are jointly analysed using network clustering and pathway enrichment analyses to evaluate their coverage and utility in developing rule-based models

A Rule-Based Model of Base Excision Repair

International audienceThere are ongoing debates in the DNA repair community on whether the coordination of DNA repair is achieved by means of direct protein-protein interactions or whether substrate specificity is sufficient to explain how DNA intermediates are channeled from one repair enzyme to the other. In order to address these questions we designed a model of the Base Excision Repair pathway in Kappa, a rule based formalism for modeling protein-protein and protein-DNA interactions. We use this model to shed light on the key role of the scaffolding protein XRCC1 in coordinating the repair process