Abstract
Neuronal networks have driven advances in artificial intelligence, while molecular networks can provide powerful frameworks for energy-efficient information processing. Inspired by biological principles, we present a computational framework for mapping synthetic gene circuits into bio-inspired electronic architectures. In particular, we developed logarithmic Analog-to-Digital Converter (ADC), operating in current mode with a logarithmic encoding scheme, compresses an 80 dB dynamic range into three bits while consuming less than 1 µW, occupying only 0.02 mm², and operating at 4 kHz. Our bio-inspired approach achieves linear scaling of power, unlike conventional linear ADCs where power consumption increases exponentially with bit resolution, significantly improving efficiency in resource-constrained settings. Through a computational trade-off analysis, we demonstrate that logarithmic encoding maximizes spatial resource efficiency among power consumption and computational accuracy. By leveraging synthetic gene circuits as a model for efficient computation, this study provides a platform for the convergence of synthetic biology and bio-inspired electronic design.
Data availability
All data supporting the findings of this study are included in the article and its Supplementary Notes. Any additional requests for information can be directed to the corresponding author.
Code availability
Code will be made available on reasonable request
References
Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).
Kudithipudi, D. et al. Neuromorphic computing at scale. Nature 637, 801–812 (2025).
Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).
Sarpeshkar, R. Ultra Low Power Bioelectronics: Fundamentals, Biomedical Applications, and Bio-Inspired Systems, Vol. 9780521857277 (Cambridge University Press, 2010).
Sarpeshkar, R. Analog synthetic biology. Philos. Trans. A Math. Phys. Eng. Sci. 372, 20130110 (2014).
Teo, J. J. Y., Woo, S. S. & Sarpeshkar, R. Synthetic biology: a unifying view and review using analog circuits. IEEE Trans. Biomed. Circuits Syst. 9, 453–474 (2015).
Mandal, S. & Sarpeshkar, R. Log-domain circuit models of chemical reactions. In Proceedings—IEEE International Symposium on Circuits and Systems - ISCAS, 2697–2700 https://doi.org/10.1109/ISCAS.2009.5118358 (2009).
Hanna, H. A., Danial, L., Kvatinsky, S. & Daniel, R. Cytomorphic electronics with memristors for modeling fundamental genetic circuits. IEEE Trans. Biomed. Circuits Syst. 14, 386–401 (2020).
Woo, S. S., Kim, J. & Sarpeshkar, R. A digitally programmable cytomorphic chip for simulation of arbitrary biochemical reaction networks. IEEE Trans. Biomed. Circuits Syst. 12, 360–378 (2018).
Woo, S. S., Kim, J. & Sarpeshkar, R. A cytomorphic chip for quantitative modeling of fundamental bio-molecular circuits. IEEE Trans. Biomed. Circuits Syst. 9, 527–542 (2015).
Kim, J., Woo, S. S. & Sarpeshkar, R. Fast and precise emulation of stochastic biochemical reaction networks with amplified thermal noise in silicon chips. IEEE Trans. Biomed. Circuits Syst. 12, 379–389 (2018).
Cameron, D. E., Bashor, C. J. & Collins, J. J. A brief history of synthetic biology. Nat. Rev. Microbiol. 12, 381–390 (2014).
Brophy, J. A. N. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).
Li, X. & Daniel, R. Synthetic nonlinear computation for genetic circuit design. Curr. Opin. Biotechnol. 76, 102727 (2022).
Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).
Stricker, J. et al. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519 (2008).
Tamsir, A., Tabor, J. J. & Voigt, C. A. Robust multicellular computing using genetically encoded NOR gates and chemical ‘wiresg’. Nature 469, 212–215 (2011).
Daniel, R., Rubens, J. R., Sarpeshkar, R. & Lu, T. K. Synthetic analog computation in living cells. Nature 497, 619–623 (2013).
Andrews, L. B., Nielsen, A. A. K. & Voigt, C. A. Cellular checkpoint control using programmable sequential logic. Science 361, 1340–1344 (2018).
Li, X. et al. Synthetic neural-like computing in microbial consortia for pattern recognition. Nat. Commun. 12, 3139 (2021).
Samuel, R. & Daniel, R. Intelligent computation in cancer gene therapy. Front. Genet. 15, 1252246 (2024).
Rizik, L., Danial, L., Habib, M., Weiss, R. & Daniel, R. Synthetic neuromorphic computing in living cells. Nat. Commun. 13, 5602 (2022).
Ferrell, J. E. Signaling Motifs and Weber’s law. Mol. Cell 36, 724–727 (2009).
Mimee, M. et al. An ingestible bacterial-electronic system to monitor gastrointestinal health. Science 360, 915–918 (2018).
Teymourian, H., Barfidokht, A. & Wang, J. Electrochemical glucose sensors in diabetes management: an updated review (2010–2020). Chem. Soc. Rev. 49, 7671–7709 (2020).
Sit, J. J. & Sarpeshkar, R. A micropower logarithmic A/D with offset and temperature compensation. IEEE J. Solid State Circuits 39, 308–319 (2004).
Mahattanakul, J. Logarithmic data converter suitable for hearing aid applications. Electron Lett. 41, 394–396 (2005).
Alon, U. An Introduction to Systems Biology Design Principles of Biological Circuits (CRC Press, 2006).
Daniel, R., Woo, S. S., Turicchia, L. & Sarpeshkar, R. Analog transistor models of bacterial genetic circuits. In 2011 IEEE Biomedical Circuits and Systems Conference - BioCAS, 333–336 (2011).
Teo, J. J. Y. & Sarpeshkar, R. The merging of biological and electronic circuits. iScience 23, 101688 (2020).
Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).
Danial, L. et al. Two-terminal floating-gate transistors with a low-power memristive operation mode for analogue neuromorphic computing. Nat. Electron. 2, 596–605 (2019).
Gilbert, B. Translinear circuits: a proposed classification. Electron. Lett. 11, 14–16 (1975).
Gilbert, B. Translinear circuits: an historical overview. Analog Integr. Circuits Signal Process 9, 95–118 (1996).
Andreou, A. G. & Boahen, K. A. Translinear circuits in subthreshold MOS. Analog Integr. Circuits Signal Process 9, 141–166 (1996).
Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 1–23 (2011).
Tank, D. W. & Hopfield, J. J. Simple “neural” optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans. Circuits Syst. 33, 533–541 (1986).
Danial, L., Wainstein, N., Kraus, S. & Kvatinsky, S. Breaking through the speed-power-accuracy tradeoff in ADCs using a memristive neuromorphic architecture. IEEE Trans. Emerg. Top. Comput Intell. 2, 396–409 (2018).
Danielli, L., Li, X., Tuler, T. & Daniel, R. Quantifying the distribution of protein oligomerization degree reflects cellular information capacity. Sci. Rep. 10, 1–10 (2020).
Sarpeshkar, R. Analog versus digital: extrapolating from electronics to neurobiology. Neural Comput. 10, 1601–1638 (1998).
Mandal, S. Collective analog bioelectronic computation. https://dspace.mit.edu/handle/1721.1/52801 (2009).
Walden, R. H. Analog-to-digital converter survey and analysis. IEEE J. Sel. Areas Commun. 17, 539–550 (1999).
Rhew, H. G. et al. A fully self-contained logarithmic closed-loop deep brain stimulation SoC with wireless telemetry and wireless power management. IEEE J. Solid State Circuits 49, 2213–2227 (2014).
Lee, J. et al. A 2.5 mW 80 dB DR 36 dB SNDR 22 MS/s logarithmic pipeline ADC. IEEE J. Solid State Circuits 44, 2755–2765 (2009).
Lee, J., Rhew, H. G., Kipke, D. R. & Flynn, M. P. A 64 channel programmable closed-loop neurostimulator with 8 channel neural amplifier and logarithmic ADC. IEEE J. Solid State Circuits 45, 1935–1945 (2010).
Judy, M., Sodagar, A. M., Lotfi, R. & Sawan, M. Nonlinear signal-specific ADC for efficient neural recording in brain-machine interfaces. IEEE Trans. Biomed. Circuits Syst. 8, 371–381 (2014).
Thanachayanont, A. A 1-V, 330-nW, 6-Bit current-mode logarithmic cyclic ADC for ISFET-based pH digital readout system. Circuits Syst. Signal Process 34, 1405–1414 (2015).
Morton, E. J., Ng, C. Y. & Menezes, T. Low noise integrating preamplifier with integral ADC. IEEE Electron Device Lett. 40, 352–355 (2019).
Wolffenbuttel, R. F. Nonlinear AD converters for integrated silicon optical sensors. In VLSI and Computer Peripherals/ COMPEURO. https://doi.org/10.1109/cmpeur.1989.93420 (1989).
Francesconi, F. & Maloberti, F. Low power logarithmic A/D converter. Proc. IEEE Int. Symp. Circuits Syst. 1, 473–476 (1996).
Hernandez, L. & Paton, S. Continuous-time noise-shaping modulator for logarithmic A/D conversion. Proc. IEEE Int. Symp. Circuits Syst. 2, 955–956 (1999).
Guilherme, J., Vital, J. & Franca, J. A CMOS logarithmic pipeline A/D converter with a dynamic range of 80 dB. Proc. IEEE Int. Conf. Electron. Circuits, Syst. 1, 193–196 (2002).
Danial, L., Sharma, K., Dwivedi, S. & Kvatinsky, S. Logarithmic neural network data converters using memristors for biomedical applications. IEEE BioCAS 2019—Biomedical Circuits and Systems Conference, Proceedings. https://doi.org/10.1109/BIOCAS.2019.8919068 (2019).
Chen, S. F., Juang, Y. J., Huang, S. Y. & King, Y. C. Logarithmic CMOS image sensor through multi-resolution analog-to-digital conversion. IEEE- International Symposium on VLSI Technology, Systems, and Applications, Proceedings, Vol. 2003 (2003).
Sundarasaradula, Y., Constandinou, T. G. & Thanachayanont, A. A 6-bit, two-step, successive approximation logarithmic ADC for biomedical applications. IEEE Int. Conf. Electron., Circuits Syst., ICECS 25–28 (2016).
Yang, H. Y. & Sarpeshkar, R. A bio-inspired ultra-energy-efficient analog-to-digital converter for biomedical applications. IEEE Trans. Circuit Syst. I: Reg. Papers 53, 2349–2356 (2006).
Shokri, R., Koolivand, Y., Shoaei, O., Caviglia, D. D. & Aiello, O. A reconfigurable, nonlinear, low-power, VCO-based ADC for neural recording applications. Sensors 24, 6161 (2024).
Sirimasakul, S. & Thanachayanont, A. A Logarithmic level-crossing ADC with fixed comparison window. IEEE 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022 https://doi.org/10.1109/ECTI-CON54298.2022.9795458 (2022).
Sengupta, S. & Johnston, M. L. A widely reconfigurable piecewise-linear ADC for information-aware quantization. IEEE Trans. Circuits Syst. II Express Briefs 68, 1073–1077 (2021).
Acknowledgements
The authors would like to thank, Ofir Glick, Hadar Sade, Itay Weinstein, Harel Segal, Shlomo Koifman and Simcha Edery from the Technion-Israel Institute of Technology for supporting this research. This work was funded by the Israel Innovation Authority (Grant No. 75699).
Author information
Authors and Affiliations
Contributions
I.O., L.D., and R.D. designed the study. I.O. and V.G. performed electronic simulations and collected data. M.H. performed the biological experiments and collected data. L.D., Y.S., and J.S. reviewed the design and the results. All authors analyzed the data, discussed the results, and wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Communications Engineering thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: [Wenjie Wang, Rosamund Daw]. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Oren, I., Gupta, V., Habib, M. et al. Harnessing synthetic biology for energy-efficient bioinspired electronics: applications for logarithmic data converters. Commun Eng (2026). https://doi.org/10.1038/s44172-026-00589-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s44172-026-00589-5
