Synthesizing and tuning stochastic chemical reaction networks with specified behaviours

Murphy, Niall; Petersen, Rasmus Lerchedahl; Phillips, Andrew; Yordanov, Boyan; Dalchau, Neil

doi:10.1098/rsif.2018.0283

Cited by 10 publications

(6 citation statements)

References 51 publications

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“…Recent work due to Murphy et al [42] is close to ours in spirit, but focuses on discrete systems (integer molecular counts of the species). Cardelli et al [7] take a program synthesis approach to generate CRNs that follow properties provided by a certain "sketch" language (i.e., a template) using SMT solvers on the back end [4,14].…”

Section: Related Workmentioning

confidence: 87%

CRNs Exposed: Systematic Exploration of Chemical Reaction Networks

Vasić¹,

Soloveichik²,

Khurshid³

2019

Preprint

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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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Section: Related Workmentioning

confidence: 87%

CRNs Exposed: Systematic Exploration of Chemical Reaction Networks

Vasić¹,

Soloveichik²,

Khurshid³

2019

Preprint

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“…We report the experimental data employed for the Gibson assembly protocol in Section 3.1 (from [9] Figure 2a). For simplicity we report the output values of only species O and omit unit of measurement, which are mM for concentration, µL for volume, Celsius degrees for temperature, and seconds for equilibration time: d 1 = ((1, 0, 0, 0, 1, 20), (1, 0), 0) d 2 = ((1, 1, 0, 0, 0, 1, 20), (1, 120), 0) d 3 = ((1, 0, 0, 0, 1, 20), (1,240), 0.05) d 4 = ((1, 0, 0, 0, 1, 20), (1,360), 0.56) d 5 = ((1, 0, 0, 0, 1, 20), (1, 480), 0.8) d 6 = ((1, 0, 0, 0, 1, 20), (1, 660), 0.86) d 5 = ((1, 0, 0, 0, 1, 20), (1, 840), 0.9) d 6 = ((1, 0, 0, 0, 1, 20), (1, 960), 0.88), where, for example, in d 1 we have that the initial concentration for AB and BA is 1 (note that in this case the optimization variables are x BA and T, hence in d 1 the vector (1, 0) represents the value assigned to those variables during the particular experiment) and all other species are not present at time 0, volume and temperature at which the experiment is performed are 1 and 20 and the observed value for O at the end of the protocol for T = 0 is 0. We assume an additive Gaussian observation noise (noise in the collection of the data) with standard deviation σ = 0.1.…”

Section: Appendix B Data For Gibson Assemblymentioning

confidence: 99%

“…Automation is becoming ubiquitous in all laboratory activities: protocols are run under reproducible and auditable software control, data are collected by high-throughput machinery, experiments are automatically analyzed, and further experiments are selected to maximize knowledge acquisition. However, while progress is being made towards the routine integration of sophisticated end-to-end laboratory workflows and towards the remote access to laboratory facilities and procedures [1][2][3][4][5], the integration between laboratory protocols and mathematical models is still lacking. Models describe physical processes, either mechanistically or by inference from data, while protocols define the steps carried out during an experiment in order to obtain experimental data.…”

Section: Introductionmentioning

confidence: 99%

A Language for Modeling and Optimizing Experimental Biological Protocols

Cardelli¹,

Kwiatkowska²,

Laurenti³

2021

Computation

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Automation is becoming ubiquitous in all laboratory activities, moving towards precisely defined and codified laboratory protocols. However, the integration between laboratory protocols and mathematical models is still lacking. Models describe physical processes, while protocols define the steps carried out during an experiment: neither cover the domain of the other, although they both attempt to characterize the same phenomena. We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection. To this end, we present a language to model and optimize experimental biochemical protocols that facilitates such an integrated description, and that can be combined with experimental data. We provide probabilistic semantics for our language in terms of Gaussian processes (GPs) based on the linear noise approximation (LNA) that formally characterizes the uncertainties in the data collection, the underlying model, and the protocol operations. In a set of case studies, we illustrate how the resulting framework allows for automated analysis and optimization of experimental protocols, including Gibson assembly protocols.

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“…With the advent of the modern statistical theory, the research in this area got boosted. Not only in the field of computer science we also encounter its application in different fields from chemical networks [104,106,191,192], molecular biology [100,102] and even in neurology [203,204].…”

Section: Thermodynamics Of Algorithmmentioning

confidence: 99%

Revisiting thermodynamics in computation and information theory

Chattopadhyay,

Paul

2021

Preprint

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One of the primary motivations of the research in the field of computation is to optimize the cost of computation. The major ingredient that a computer needs is the energy to run a process, i.e., the thermodynamic cost. The analysis of the thermodynamic cost of computation is one of the prime focuses of research. It started back since the seminal work of Landauer where it was commented that the computer spends kBT ln2 amount of energy to erase a bit of information (here T is the temperature of the system and kB represents the Boltzmann's constant). The advancement of statistical mechanics has provided us the necessary tool to understand and analyze the thermodynamic cost for the complicated processes that exist in nature, even the computation of modern computers. The advancement of physics has helped us to understand the connection of the statistical mechanics (the thermodynamics cost) with computation. Another important factor that remains a matter of concern in the field of computer science is the error correction of the error that occurs while transmitting the information through a communication channel.Here in this article, we have reviewed the progress of the thermodynamics of computation starting from Landauer's principle to the latest model, which simulates the modern complex computation mechanism. After exploring the salient parts of computation in computer science theory and information theory, we have reviewed the thermodynamic cost of computation and error correction. We have also discussed about the alternative computation models that have been proposed with thermodynamically cost-efficient.

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Synthesizing and tuning stochastic chemical reaction networks with specified behaviours

Cited by 10 publications

References 51 publications

CRNs Exposed: Systematic Exploration of Chemical Reaction Networks

CRNs Exposed: Systematic Exploration of Chemical Reaction Networks

A Language for Modeling and Optimizing Experimental Biological Protocols

Revisiting thermodynamics in computation and information theory

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