47 research outputs found
BioNetGen 2.2: Advances in Rule-Based Modeling
BioNetGen is an open-source software package for rule-based modeling of
complex biochemical systems. Version 2.2 of the software introduces numerous
new features for both model specification and simulation. Here, we report on
these additions, discussing how they facilitate the construction, simulation,
and analysis of larger and more complex models than previously possible.Comment: 3 pages, 1 figure, 1 supplementary text file. Supplementary text
includes a brief discussion of the RK-PLA along with a performance analysis,
two tables listing all new actions/arguments added in BioNetGen 2.2, and the
"BioNetGen Quick Reference Guide". Accepted for publication in Bioinformatic
AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models
Ordinary differential equation models facilitate the understanding of
cellular signal transduction and other biological processes. However, for large
and comprehensive models, the computational cost of simulating or calibrating
can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB
that provides efficient simulation and sensitivity analysis routines tailored
for scalable, gradient-based parameter estimation and uncertainty
quantification.
AMICI is published under the permissive BSD-3-Clause license with source code
publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are
archived on Zenodo
RuleVis: Constructing Patterns and Rules for Rule-Based Models
We introduce RuleVis, a web-based application for defining and editing
"correct-by-construction" executable rules that model biochemical
functionality, which can be used to simulate the behavior of protein-protein
interaction networks and other complex systems. Rule-based models involve
emergent effects based on the interactions between rules, which can vary
considerably with regard to the scale of a model, requiring the user to inspect
and edit individual rules. RuleVis bridges the graph rewriting and systems
biology research communities by providing an external visual representation of
salient patterns that experts can use to determine the appropriate level of
detail for a particular modeling context. We describe the visualization and
interaction features available in RuleVisand provide a detailed example
demonstrating how RuleVis can be used to reason about intracellular
interactions
PEtab -- interoperable specification of parameter estimation problems in systems biology
Reproducibility and reusability of the results of data-based modeling studies
are essential. Yet, there has been -- so far -- no broadly supported format for
the specification of parameter estimation problems in systems biology. Here, we
introduce PEtab, a format which facilitates the specification of parameter
estimation problems using Systems Biology Markup Language (SBML) models and a
set of tab-separated value files describing the observation model and
experimental data as well as parameters to be estimated. We already implemented
PEtab support into eight well-established model simulation and parameter
estimation toolboxes with hundreds of users in total. We provide a Python
library for validation and modification of a PEtab problem and currently 20
example parameter estimation problems based on recent studies. Specifications
of PEtab, the PEtab Python library, as well as links to examples, and all
supporting software tools are available at https://github.com/PEtab-dev/PEtab,
a snapshot is available at https://doi.org/10.5281/zenodo.3732958. All original
content is available under permissive licenses
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
BioSimulator.jl: Stochastic simulation in Julia
Biological systems with intertwined feedback loops pose a challenge to
mathematical modeling efforts. Moreover, rare events, such as mutation and
extinction, complicate system dynamics. Stochastic simulation algorithms are
useful in generating time-evolution trajectories for these systems because they
can adequately capture the influence of random fluctuations and quantify rare
events. We present a simple and flexible package, BioSimulator.jl, for
implementing the Gillespie algorithm, -leaping, and related stochastic
simulation algorithms. The objective of this work is to provide scientists
across domains with fast, user-friendly simulation tools. We used the
high-performance programming language Julia because of its emphasis on
scientific computing. Our software package implements a suite of stochastic
simulation algorithms based on Markov chain theory. We provide the ability to
(a) diagram Petri Nets describing interactions, (b) plot average trajectories
and attached standard deviations of each participating species over time, and
(c) generate frequency distributions of each species at a specified time.
BioSimulator.jl's interface allows users to build models programmatically
within Julia. A model is then passed to the simulate routine to generate
simulation data. The built-in tools allow one to visualize results and compute
summary statistics. Our examples highlight the broad applicability of our
software to systems of varying complexity from ecology, systems biology,
chemistry, and genetics. The user-friendly nature of BioSimulator.jl encourages
the use of stochastic simulation, minimizes tedious programming efforts, and
reduces errors during model specification.Comment: 27 pages, 5 figures, 3 table
Biophysical assay for tethered signaling reactions reveals tether-controlled activity for the phosphatase SHP-1
Tethered enzymatic reactions are ubiquitous in signaling networks but are poorly understood. A previously unreported mathematical analysis is established for tethered signaling reactions in surface plasmon resonance (SPR). Applying the method to the phosphatase SHP-1 interacting with a phosphorylated tether corresponding to an immune receptor cytoplasmic tail provides five biophysical/biochemical constants from a single SPR experiment: two binding rates, two catalytic rates, and a reach parameter. Tether binding increases the activity of SHP-1 by 900-fold through a binding-induced allosteric activation (20-fold) and a more significant increase in local substrate concentration (45-fold). The reach parameter indicates that this local substrate concentration is exquisitely sensitive to receptor clustering. We further show that truncation of the tether leads not only to a lower reach but also to lower binding and catalysis. This work establishes a new framework for studying tethered signaling processes and highlights the tether as a control parameter in clustered receptor signaling