Abstract
PySB is a framework for creating biological models as Python programs using a
high-level, action-oriented vocabulary that promotes transparency, extensibility and
reusability. PySB interoperates with many existing modeling tools and supports
distributed model development.
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PySB models are programs and leverage existing programming tools for documentation, testing, and collaborative development.
Reusable functions can encode common low-level biochemical processes as well as high-level modules, making models transparent and concise.
Modeling workflow is accelerated through close integration with Python numerical tools and interoperability with existing modeling software.
We demonstrate the use of PySB to encode 15 alternative hypotheses for the mitochondrial regulation of apoptosis, including a new ‘Embedded Together' model based on recent biochemical findings.
Mathematical equations are fundamental to modeling biological networks, but as
networks get large and revisions frequent, it becomes difficult to manage equations
directly or to combine previously developed models. Multiple simultaneous efforts to
create graphical standards, rule-based languages, and integrated software
workbenches aim to simplify biological modeling but none fully meets the need for
transparent, extensible, and reusable models. In this paper we describe PySB, an
approach in which models are not only created using programs, they are programs.
PySB draws on programmatic modeling concepts from little b and ProMot, the
rule-based languages BioNetGen and Kappa and the growing library of Python numerical
tools. Central to PySB is a library of macros encoding familiar biochemical actions
such as binding, catalysis, and polymerization, making it possible to use a
high-level, action-oriented vocabulary to construct detailed models. As Python
programs, PySB models leverage tools and practices from the open-source software
community, substantially advancing our ability to distribute and manage the work of
testing biochemical hypotheses. We illustrate these ideas using new and previously
published models of apoptosis.
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