CRN++: Molecular programming language

Vasić, Marko; Soloveichik, David; Khurshid, Sarfraz

doi:10.1007/s11047-019-09775-1

Cited by 23 publications

(37 citation statements)

References 21 publications

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“…This renders simple landscape exploration methods (e.g. exhaustive search as in [ 20 ] or Monte Carlo methods) unsuitable because of the computational cost.…”

Section: Domain-level Explorationmentioning

confidence: 99%

“…While optimization methods for DNA nanotechnology have been proposed to generate specific shapes [ 19 ], we are interested here in an exploration of the structures that can emerge from CRNs. Methods for the exhaustive domain-level search of the space of CRNs already exist [ 20 ], however, they do not scale to larger search spaces and can only be applied for very simple and constrained problems. In this work, only the smallest library can be exhaustively explored.…”

Section: Introductionmentioning

confidence: 99%

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Automated exploration of DNA-based structure self-assembly networks

2021

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Finding DNA sequences capable of folding into specific nanostructures is a hard problem, as it involves very large search spaces and complex nonlinear dynamics. Typical methods to solve it aim to reduce the search space by minimizing unwanted interactions through restrictions on the design (e.g. staples in DNA origami or voxel-based designs in DNA Bricks). Here, we present a novel methodology that aims to reduce this search space by identifying the relevant properties of a given assembly system to the emergence of various families of structures (e.g. simple structures, polymers, branched structures). For a given set of DNA strands, our approach automatically finds chemical reaction networks (CRNs) that generate sets of structures exhibiting ranges of specific user-specified properties, such as length and type of structures or their frequency of occurrence. For each set, we enumerate the possible DNA structures that can be generated through domain-level interactions, identify the most prevalent structures, find the best-performing sequence sets to the emergence of target structures, and assess CRNs' robustness to the removal of reaction pathways. Our results suggest a connection between the characteristics of DNA strands and the distribution of generated structure families.

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“…This renders simple landscape exploration methods (e.g. exhaustive search as in [ 20 ] or Monte Carlo methods) unsuitable because of the computational cost.…”

Section: Domain-level Explorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated exploration of DNA-based structure self-assembly networks

2021

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show abstract

Section: Introductionmentioning

confidence: 99%

“…This package is inspired by the molecular compilers developed by the DNA-strand displacement community and molecular programming communities which, broadly speaking, aim to compile models of DNA circuit implementations from simpler CRN specifications 11–13 or rudimentary programming languages. 14,15 However, BioCRNpyler differs from these tools for three main reasons: first, it is not focused only on DNA implementations of chemical computation; second, it does not take the form of a traditional programming language; and third, modeling assumptions and compilation schemas can be easily redefined by the user. BioCRNpyler combines specifications consisting of synthetic biological parts and systems biology motifs that can be reused and recombined in diverse biochemical contexts at customizable levels of model complexity.…”

Section: Introductionmentioning

confidence: 99%

BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts

Poole

Pandey

Shur

et al. 2020

Preprint

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Biochemical interactions in systems and synthetic biology are often modeled with Chemical Reaction Networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in python, that complies high-level design specifications to CRN representations. This compilation process offers three advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to represented succinctly with design choices propogated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. With these advantages offered by BioCRNpyler, users can quickly build and test multitude of models in different environments. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later.

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“…Much ongoing work explores the computational power of CRNs. Previous work showed the implementation of numerous complex behaviors, such as mapping polynomials to chemical reactions [46], programming logic gates [39], mapping discrete, control flow, algorithms [28], and a molecular programming language translating high-level specifications to chemical reactions [50]. However the complexity of these reaction systems can be infeasible, asking for novel techniques that answers what the natural way to compute "in reactions".…”

Section: Introductionmentioning

confidence: 99%

CRNs Exposed: Systematic Exploration of Chemical Reaction Networks

Vasić¹,

Soloveichik²,

Khurshid³

2019

Preprint

Self Cite

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Formal methods have enabled breakthroughs in many fields, such as in hardware verification, machine learning and biological systems. Our focus is on systems and synthetic biology, where a key object of interest is coupled chemical reactions in a well-mixed solution formalized as chemical reaction networks (CRNs). CRNs are pivotal for our understanding of biological regulatory and metabolic networks, as well as for programming engineered molecular behavior. Although it is clear that small CRNs are capable of complex dynamics and computational behavior, it remains difficult to explore the space of CRNs in search for desired functionality. We use Alloy, a tool for expressing structural constraints and behavior in software systems, to enumerate CRNs with declaratively specified properties. We show how this framework can enumerate CRNs with a variety of structural constraints including biologically motivated catalytic networks and metabolic networks, and see-saw networks motivated by DNA nanotechnology. We also use the framework to explore analog function computation in rate-independent CRNs. By computing the desired output value with stoichiometry rather than with reaction rates (in the sense that X → Y + Y computes multiplication by 2), such CRNs are completely robust to the choice of reaction rates or rate law. We find the smallest CRNs computing the max, abs, and ReLU (rectified linear unit) functions in a natural subclass of rate-independent CRNs where rate-independence follows from structural network properties.

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CRN++: Molecular programming language

Cited by 23 publications

References 21 publications

Automated exploration of DNA-based structure self-assembly networks

Automated exploration of DNA-based structure self-assembly networks

BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts

CRNs Exposed: Systematic Exploration of Chemical Reaction Networks

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