33 research outputs found
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
Toward a comprehensive language for biological systems
Rule-based modeling has become a powerful approach for modeling intracellular networks, which are characterized by rich molecular diversity. Truly comprehensive models of cell behavior, however, must address spatial complexity at both the intracellular level and at the level of interacting populations of cells, and will require richer modeling languages and tools. A recent paper in BMC Systems Biology represents a signifcant step toward the development of a unified modeling language and software platform for the development of multi-level, multiscale biological models
A framework for mapping, visualisation and automatic model creation of signal-transduction networks
An intuitive formalism for reconstructing cellular networks from empirical data is presented, and used to build a comprehensive yeast MAP kinase network. The accompanying rxncon software tool can convert networks to a range of standard graphical formats and mathematical models
First passage events in biological systems with non-exponential inter-event times
It is often possible to model the dynamics of biological systems as a series of discrete transitions between a finite set of observable states (or compartments). When the residence times in each state, or inter-event times more generally, are exponentially distributed, then one can write a set of ordinary differential equations, which accurately describe the evolution of mean quantities. Non-exponential inter-event times can also be experimentally observed, but are more difficult to analyse mathematically. In this paper, we focus on the computation of first passage events and their probabilities in biological systems with non-exponential inter-event times. We show, with three case studies from Molecular Immunology, Virology and Epidemiology, that significant errors are introduced when drawing conclusions based on the assumption that inter-event times are exponentially distributed. Our approach allows these errors to be avoided with the use of phase-type distributions that approximate arbitrarily distributed inter-event times
Integration of rule-based models and compartmental models of neurons
Synaptic plasticity depends on the interaction between electrical activity in
neurons and the synaptic proteome, the collection of over 1000 proteins in the
post-synaptic density (PSD) of synapses. To construct models of synaptic
plasticity with realistic numbers of proteins, we aim to combine rule-based
models of molecular interactions in the synaptic proteome with compartmental
models of the electrical activity of neurons. Rule-based models allow
interactions between the combinatorially large number of protein complexes in
the postsynaptic proteome to be expressed straightforwardly. Simulations of
rule-based models are stochastic and thus can deal with the small copy numbers
of proteins and complexes in the PSD. Compartmental models of neurons are
expressed as systems of coupled ordinary differential equations and solved
deterministically. We present an algorithm which incorporates stochastic
rule-based models into deterministic compartmental models and demonstrate an
implementation ("KappaNEURON") of this hybrid system using the SpatialKappa and
NEURON simulators.Comment: Presented to the Third International Workshop on Hybrid Systems
Biology Vienna, Austria, July 23-24, 2014 at the International Conference on
Computer-Aided Verification 201
RKappa: Statistical sampling suite for Kappa models
We present RKappa, a framework for the development and analysis of rule-based
models within a mature, statistically empowered R environment. The
infrastructure allows model editing, modification, parameter sampling,
simulation, statistical analysis and visualisation without leaving the R
environment. We demonstrate its effectiveness through its application to Global
Sensitivity Analysis, exploring it in "parallel" and "concurrent"
implementations.
The pipeline was designed for high performance computing platforms and aims
to facilitate analysis of the behaviour of large-scale systems with limited
knowledge of exact mechanisms and respectively sparse availability of parameter
values, and is illustrated here with two biological examples.
The package is available on github: https://github.com/lptolik/R4KappaComment: Hybrid Systems and Biology 2014, Vienn