ArticleFrontiers in bioengineering and biotechnology2015
Signal-to-Noise Ratio Measures Efficacy of Biological Computing Devices and Circuits.
Article in Frontiers in bioengineering and biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- An Open-Quantum-Systems Theory of Quantum-Biological Communication Channels.IEEE transactions on molecular, biological, and multi-scale communications · 2026Article
- Small molecule-inducible gene regulatory systems in mammalian cells: progress and design principles.Current opinion in biotechnology · 2022Review
- Single-cell measurement quality in bits.PloS one · 2022Article
- Engineering digitizer circuits for chemical and genetic screens in human cells.Nature communications · 2021Article
- Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals.Communications biology · 2021Article
- Comparison of bias and resolvability in single-cell and single-transcript methods.Communications biology · 2021Article
- Harnessing the central dogma for stringent multi-level control of gene expression.Nature communications · 2021Article
- Computer-aided biochemical programming of synthetic microreactors as diagnostic devices.Molecular systems biology · 2018Article
- Engineering Diagnostic and Therapeutic Gut Bacteria.Microbiology spectrum · 2017Review
- Design Automation in Synthetic Biology.Cold Spring Harbor perspectives in biology · 2017Review
- Editorial - Synthetic Biology: Engineering Complexity and Refactoring Cell Capabilities.Frontiers in bioengineering and biotechnology · 2015Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Engineering biological cells to perform computations has a broad range of important potential applications, including precision medical therapies, biosynthesis process control, and environmental sensing. Implementing predictable and effective computation, however, has been extremely difficult to date, due to a combination of poor composability of available parts and of insufficient characterization of parts and their interactions with the complex environment in which they operate. In this paper, the author argues that this situation can be improved by quantitative signal-to-noise analysis of the relationship between computational abstractions and the variation and uncertainty endemic in biological organisms. This analysis takes the form of a ΔSNRdB function for each computational device, which can be computed from measurements of a device's input/output curve and expression noise. These functions can then be combined to predict how well a circuit will implement an intended computation, as well as evaluating the general suitability of biological devices for engineering computational circuits. Applying signal-to-noise analysis to current repressor libraries shows that no library is currently sufficient for general circuit engineering, but also indicates key targets to remedy this situation and vastly improve the range of computations that can be used effectively in the implementation of biological applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.