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Multicellular artificial neural network-type architectures demonstrate computational problem solving

Abstract

Here, we report a modular multicellular system created by mixing and matching discrete engineered bacterial cells. This system can be designed to solve multiple computational decision problems. The modular system is based on a set of engineered bacteria that are modeled as an ‘artificial neurosynapse’ that, in a coculture, formed a single-layer artificial neural network-type architecture that can perform computational tasks. As a demonstration, we constructed devices that function as a full subtractor and a full adder. The system is also capable of solving problems such as determining if a number between 0 and 9 is a prime number and if a letter between A and L is a vowel. Finally, we built a system that determines the maximum number of pieces of a pie that can be made for a given number of straight cuts. This work may have importance in biocomputer technology development and multicellular synthetic biology.

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Fig. 1: Mathematical and genetic network design.
Fig. 2: Optimization of bactoneuron parameters.
Fig. 3: Simulations and experimental validation of optimized bactoneurons.
Fig. 4: Fluorescence microscopy for full subtractor and full adder.
Fig. 5: Bacterial ANNs answer yes/no computational problems.
Fig. 6: Bacterial ANNs answer multiple mathematical problems together and determine the maximum number of pieces obtained from a pie for a given number of straight cuts.

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Data availability

All relevant data supporting the findings are available within the paper, Extended Data figures and the Supplementary Data. Source data are provided with this paper.

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Acknowledgements

This work was financially supported by grant RSI4002, Department of Atomic Energy, Government of India, awarded to Saha Institute of Nuclear Physics, and S.B. is a part of it.

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Author notes

  1. These authors contributed equally: Deepro Bonnerjee, Saswata Chakraborty, Biyas Mukherjee.

Authors and Affiliations

  1. Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Block AF, Sector-I, Bidhannagar, Kolkata, India

    Deepro Bonnerjee, Saswata Chakraborty, Biyas Mukherjee, Ritwika Basu, Abhishek Paul & Sangram Bagh

  2. Homi Bhabha National Institute, Training School Complex, Anushaktinagar, Mumbai, India

    Deepro Bonnerjee, Saswata Chakraborty, Biyas Mukherjee, Ritwika Basu, Abhishek Paul & Sangram Bagh

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Contributions

D.B., S.C., B.M., R.B. and A.P. performed all the experiments. D.B., S.C., B.M., R.B. and S.B. analyzed the data. D.B., S.C. and S.B. designed the experiments and wrote the manuscript. S.B. designed and conceived the study.

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Sangram Bagh.

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Extended data

Extended Data Fig. 1 Genetic circuit designs.

Generic maps of the synthetic genetic networks that encode corresponding specific functions to each bactoneuron type.

Extended Data Fig. 2 Details of characterization and Dose Responses experiments of Bactoneurons that went through no iteration and were selected as final.

The bactoneurons AS1, AS2, AS3, AS4, AS8 and AS9 were separately subjected to all possible combinations of input conditions (saturated concentration ‘1’ or absent ‘0’) yielding corresponding parameter values of Fold Change (F.C.), Σ Leakage (ΣL) and Signal variation (S.V.) (shown in figure). ‘Signal variation’ (S.V.) is a comparative parameter drawn by scaling the fluorescence signal (@ ‘ON’ state) of all bactoneurons in an iteration set (with the same output channel) by considering the strongest signal of all bactoneurons as ‘1’. For Dose response, the concentration of one parameter was varied across various concentration points while the other two inducers were kept constant at ‘0’ for repressor or saturated concentration for activator (specified atop each dose response curve). The fluorescence readings obtained from the dose response experiments were fitted with the Log-Sigmoid function (see Methods) to derive the parameters Bias (BAS#) and Weights (Winducer) and the respective Inducer Saturation points (Sat.) were identified. Each data point represent mean and s.d. from four independent colonies.

Source data

Extended Data Fig. 3 Details of characterization and Dose Responses experiments across iterations of Bactoneuron 5.

AS5A, AS5B, AS5C, AS5D, AS5E, AS5F and AS5G; yielding corresponding parameter values (shown in figure) of Fold Change (F.C.), Σ Leakage (ΣL), Signal variation (S.V.), Bias (BAS#), Weights (Winducer) and Inducer Saturation points (Sat.). The fluorescence readings obtained from the dose response experiments were fitted with the Log-Sigmoid function (see Methods) to derive the Weight and Bias parameters. The induction states of the two constant inducers are specified atop the fitted curves. The final selected Construct is shown boxed. Each data point represent mean and s.d. from four independent colonies.

Source data

Extended Data Fig. 4 Details of characterization and Dose Responses experiments across iterations of Bactoneuron 6.

AS6A, AS6B, AS6C, AS6D, AS6E and AS6F; yielding corresponding parameter values (shown in figure) of Fold Change (F.C.), Σ Leakage (ΣL), Signal variation (S.V.), Bias (BAS#), Weights (WInducer) and Inducer Saturation points (Sat.). The fluorescence readings obtained from the dose response experiments were fitted with the Log-Sigmoid function (see Methods) to derive the Weight and Bias parameters. The induction states of the two constant inducers are specified atop the fitted curves. The final selected Construct is shown boxed. Each data point represent mean and s.d. from four independent colonies.

Source data

Extended Data Fig. 5 Details of characterization and Dose Responses experiments across iterations of Bactoneuron 7.

AS7A, AS7B, AS7C and AS7D; yielding corresponding parameter values (shown in figure) of Fold Change (F.C.), Σ Leakage (ΣL), Signal variation (S.V.), Bias (BAS#), Weights (WInducer) and Inducer Saturation points (Sat.). The fluorescence readings obtained from the dose response experiments were fitted with the Log-Sigmoid function (see Methods) to derive the Weight and Bias parameters. The induction states of the two constant inducers are specified atop the fitted curves. The final selected Construct is shown boxed. Each data point represent mean and s.d. from four independent colonies.

Source data

Extended Data Fig. 6 Detailed genetic circuit designs and plasmid maps of Bactoneurons that went through no iteration and were selected as final.

(a) BNeu AS1, (b) BNeu AS2, (c) BNeu AS3, (d) BNeu AS4, (e) BNeu AS8, (f) BNeu AS9. Names of plasmids constructed and incorporated in the circuits are also shown within the plasmid schematic. MCS represents empty network brick cloning site. ‘F’ and ‘B’ denotes forward and reverse direction of cassette respectively. The E2-crimson output of AS1 is altered with EGFP to give AS1* and EGFP output of AS2 is altered with E2-Crimson to give AS2*.

Extended Data Fig. 7 Detailed genetic circuit designs and plasmid maps of Bactoneuron 5 across iterations.

(a) BNeu AS5A, (b) BNeu AS5B, (c) BNeu AS5C, (d) BNeu AS5D, (e) BNeu AS5E, (f) BNeu AS5F and (g) BNeu AS5G. Names of plasmids constructed and incorporated in the circuits are also shown within the plasmid schematic. MCS represents empty network brick cloning site. ‘F’ and ‘B’ denotes forward and reverse direction of cassette respectively.

Extended Data Fig. 8 Detailed genetic circuit designs and plasmid maps of Bactoneuron 6 and Bactoneuron 7 across iterations.

(a) BNeu AS6A, (b) BNeu AS6B, (c) BNeu AS6C, (d) BNeu AS6D, (e) BNeu AS6E, (f) BNeu AS6F, (g) BNeu AS7A, (h) BNeu AS7B, (i) BNeu AS7C and (j) BNeu AS7D. Names of plasmids constructed and incorporated in the circuits are also shown within the plasmid schematic. MCS represents empty network brick cloning site. ‘F’ and ‘B’ denotes forward and reverse direction of cassette respectively.

Extended Data Fig. 9 Detailed genetic circuit designs and plasmid maps of Bactoneurons.

(a) BNeu AS10, (b) BNeu AS11 and AS11*, (c) BNeu AS12, (d) BNeu AS13, (e) BNeu AS14 and AS14*. Names of plasmids constructed and incorporated in the circuits are also shown within the plasmid schematic. MCS represents empty network brick cloning site. ‘F’ and ‘B’ denotes forward and reverse direction of cassette respectively. The EGFP output of AS11 is altered with mTagBFP2 to give AS11* and EGFP output of AS14 is altered with mKO2 to give AS14*.

Extended Data Fig. 10 Experimental Characterizations of Bactoneurons.

Fold change characterization, Dose Responses experiments with fitting values, simulation, and re-validation of Bactoneurons AS10, AS11, AS12, AS13, and AS14. Each data point represent mean and s.d. from four independent colonies.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Tables 1–5.

Reporting Summary

Source data

Source Data Fig. 2

Statistical source data for threshold parameters obtained from Extended Data Figs. 2–5.

Source Data Fig. 3

Statistical source data for simulation and validation of bactoneurons.

Source Data Extended Data Fig. 2

Statistical source data for characterization and dose–response of bactoneurons.

Source Data Extended Data Fig. 3

Statistical source data for characterization and dose–response of bactoneurons.

Source Data Extended Data Fig. 4

Statistical source data for characterization and dose–response of bactoneurons.

Source Data Extended Data Fig. 5

Statistical source data for characterization and dose–response of bactoneurons.

Source Data Extended Data Fig. 10

Statistical source data for characterization, dose–response, simulation and experimental validation of bactoneurons.

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Bonnerjee, D., Chakraborty, S., Mukherjee, B. et al. Multicellular artificial neural network-type architectures demonstrate computational problem solving.
Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01711-4

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  • Received: 23 June 2023

  • Accepted: 26 July 2024

  • Published: 16 September 2024

  • DOI: https://doi.org/10.1038/s41589-024-01711-4

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