Programming biological models in Python using PySB

López, Carlos F.; Muhlich, Jeremy; Bachman, John A.; Sorger, Peter K.

doi:10.1038/msb.2013.1

Cited by 231 publications

(262 citation statements)

References 83 publications

(212 reference statements)

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“…The rule-based model depicted in Fig. 4 was built in the PySB modeling framework, and parameter fitting was performed with the DREAM (Z) module in PyMC (27)(28)(29). Details are in SI Methods.…”

Section: Methodsmentioning

confidence: 99%

Competition and allostery govern substrate selectivity of cyclooxygenase-2

Mitchener

Hermanson

Shockley

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Cyclooxygenase-2 (COX-2) oxygenates arachidonic acid (AA) and its ester analog, 2-arachidonoylglycerol (2-AG), to prostaglandins (PGs) and prostaglandin glyceryl esters (PG-Gs), respectively. Although the efficiency of oxygenation of these substrates by COX-2 in vitro is similar, cellular biosynthesis of PGs far exceeds that of PG-Gs. Evidence that the COX enzymes are functional heterodimers suggests that competitive interaction of AA and 2-AG at the allosteric site of COX-2 might result in differential regulation of the oxygenation of the two substrates when both are present. Modulation of AA levels in RAW264.7 macrophages uncovered an inverse correlation between cellular AA levels and PG-G biosynthesis. In vitro kinetic analysis using purified protein demonstrated that the inhibition of 2-AG oxygenation by high concentrations of AA far exceeded the inhibition of AA oxygenation by high concentrations of 2-AG. An unbiased systems-based mechanistic model of the kinetic data revealed that binding of AA or 2-AG at the allosteric site of COX-2 results in a decreased catalytic efficiency of the enzyme toward 2-AG, whereas 2-AG binding at the allosteric site increases COX-2’s efficiency toward AA. The results suggest that substrates interact with COX-2 via multiple potential complexes involving binding to both the catalytic and allosteric sites. Competition between AA and 2-AG for these sites, combined with differential allosteric modulation, gives rise to a complex interplay between the substrates, leading to preferential oxygenation of AA.

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Section: Methodsmentioning

confidence: 99%

Competition and allostery govern substrate selectivity of cyclooxygenase-2

Mitchener

Hermanson

Shockley

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…BioNetGen and NFsim, which offer the ability to specify rate laws that have arbitrary functional forms [23, 81], can each be accessed from the command line, through scripting, or through a graphical user interface (GUI), which is provided by RuleBender [76, 77]. The Python-based framework PySB (for systems biology modeling) provides an additional means for using these and other tools [69]. The RuleBender/BioNetGen/NFsim software stack can be freely downloaded as a bundle, along with documentation, and installed (typically without a need for compilation) on commonly used platforms, including Windows, Mac OS, and Linux [122].…”

Section: A Brief Survey Of Useful Methods and Software Toolsmentioning

confidence: 99%

Modeling for (physical) biologists: an introduction to the rule-based approach

et al. 2015

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Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.

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“…Embedded DSLs have already been identified by others as a promising technique to facilitate scientific programming (e.g., [Hinsen 2013]), and are emerging for various domains, such as systems biology modeling (e.g., PySB [Lopez et al 2013], embedded in Python), machine learning (e.g., OptiML [Sujeeth et al 2011], embedded in Scala), and numerical analysis (e.g., Liszt [DeVito et al 2011], embedded in Scala). Other DSLs embedded in Scala deal, for example, with database access [Garcia et al 2010] or term rewriting [Sloane 2008].…”

Section: Related Workmentioning

confidence: 99%

Sessl

Ewald

Uhrmacher

2014

ACM Trans. Model. Comput. Simul.

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This article introduces SESSL (Simulation Experiment Specification via a Scala Layer), an embedded domain-specific language for simulation experiments. It serves as an additional software layer between users and simulation systems and is implemented in Scala. SESSL supports multiple simulation systems and offers various features (e.g., for experiment design, performance analysis, result reporting, and simulation-based optimization). It supports "cutting-edge" experiments by allowing to add custom code, enables a reuse of functionality across simulation systems, and improves the reproducibility of simulation experiments. ACM Reference Format:Roland Ewald and Adelinde M. Uhrmacher. 2014. SESSL: A domain-specific language for simulation experiments. ACM Trans. Model.

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Programming biological models in Python using PySB

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.

Cited by 231 publications

References 83 publications

Competition and allostery govern substrate selectivity of cyclooxygenase-2

Competition and allostery govern substrate selectivity of cyclooxygenase-2

Modeling for (physical) biologists: an introduction to the rule-based approach

Sessl

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