The use of non-traditional computing devices is growing rapidly. One paradigm of interest is chemical reaction networks (CRNs) which can model and use chemical interactions for computation. These CRNs are used to develop programs at the nanoscale for applications such as intelligent drug delivery. In practice, these programs are developed in simulation environments, and then compiled into physical systems. A challenge when designing CRNs for computation is the lack of techniques to verify and validate correctness. In this work, we adapt software testing and repair techniques for use in this domain. In initial work, we designed a testing framework to handle the challenges presented by CRN programs; this includes distributed computation and stochastic behavior. We extended this framework to implement automated program repair of CRN models and automated test generation via program invariants. For future work, we will develop a notion of fault localization for these programs, develop a theory of mutation generation, and address issues regarding flakiness present in this computing paradigm.
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Catalyst: Fast and flexible modeling of reaction networks
We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst’s broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.
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- Award ID(s):
- 1902854
- PAR ID:
- 10523288
- Editor(s):
- Ouzounis, Christos A
- Publisher / Repository:
- PLOS Comp Bio
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 19
- Issue:
- 10
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1011530
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1011530
