ReviewiScience2020
The Merging of Biological and Electronic Circuits.
Review in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Simulation of reaction-diffusion equations with reaction-reaction analog circuits.Biophysical journal · 2026Article
- Harnessing synthetic biology for energy-efficient bioinspired electronics: applications for logarithmic data converters.Communications engineering · 2026Article
- A novel pulse-modulated closed-loop artificial pancreas based on intravenous administration of insulin and glucagon.Scientific reports · 2024Article
- Special Issue-Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience?Biosensors · 2024Article
- Drug Cocktail Formulation via Circuit Design.IEEE transactions on molecular, biological, and multi-scale communications · 2023Article
- A Compact and Power-Efficient Noise Generator for Stochastic Simulations.IEEE transactions on circuits and systems. I, Regular papers : a publication of the IEEE Circuits and Systems Society · 2023Article
- Rapid modeling of experimental molecular kinetics with simple electronic circuits instead of with complex differential equations.Frontiers in bioengineering and biotechnology · 2022Article
- Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware.ACS synthetic biology · 2021Article
- Cytomorphic Electronic Systems: A review and perspective.IEEE nanotechnology magazine · 2021Article
- Measuring and modeling energy and power consumption in living microbial cells with a synthetic ATP reporter.BMC biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Biological circuits and systems within even a single cell need to be represented by large-scale feedback networks of nonlinear, stochastic, stiff, asynchronous, non-modular coupled differential equations governing complex molecular interactions. Thus, rational drug discovery and synthetic biological design is difficult. We suggest that a four-pronged interdisciplinary approach merging biology and electronics can help: (1) The mapping of biological circuits to electronic circuits via quantitatively exact schematics; (2) The use of existing electronic circuit software for hierarchical modeling, design, and analysis with such schematics; (3) The use of cytomorphic electronic hardware for rapid stochastic simulation of circuit schematics and associated parameter discovery to fit measured biological data; (4) The use of bio-electronic reporting circuits rather than bio-optical circuits for measurement. We suggest how these approaches can be combined to automate design, modeling, analysis, simulation, and quantitative fitting of measured data from a synthetic biological operational amplifier circuit in living microbial cells.
Indexed as
Identifiers
What OpenQuestion holds
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.