1
|
Agiza A, Marriott S, Rosenstein JK, Kim E, Reda S. pH-Controlled enzymatic computing for digital circuits and neural networks. Phys Chem Chem Phys 2024; 26:20898-20907. [PMID: 39045608 DOI: 10.1039/d4cp02039a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Unconventional computing paradigms explore new methods for processing information beyond the capabilities of traditional electronic architectures. In this work, we present our approach to digital computation through enzymatic reactions in chemically buffered environments. A key aspect of this approach is its reliance on pH-sensitive enzymatic reactions, with the direction of the reaction controlled by maintaining pH levels within a specific range. When the pH crosses a defined threshold, the reaction moves forward and vice versa, akin to the switching action of electronic switches in digital circuits. The binary signals (0 and 1) are encoded as different concentrations of strong acids or bases, offering a bio-inspired method for computation. The final readout is done using UV-vis spectroscopy after applying detection reactions to indicate whether the output is 1 (indicated by the presence of the enzymatic reaction's product) or 0 (indicated by the absence of the enzymatic reaction's product). We build and evaluate a set of digital circuits in the lab using our proposed methodology to model the circuits using chemical reactions. In addition, we demonstrate the implementation of a neural network classifier using our framework.
Collapse
Affiliation(s)
- Ahmed Agiza
- Computer Science Department, Brown University, Providence, RI, USA.
| | | | | | - Eunsuk Kim
- Department of Chemistry, Brown University, Providence, RI, USA
| | - Sherief Reda
- School of Engineering, Brown University, Providence, RI, USA
| |
Collapse
|
2
|
Krasecki V, Sharma A, Cavell AC, Forman C, Guo SY, Jensen ET, Smith MA, Czerwinski R, Friederich P, Hickman RJ, Gianneschi N, Aspuru-Guzik A, Cronin L, Goldsmith RH. The Role of Experimental Noise in a Hybrid Classical-Molecular Computer to Solve Combinatorial Optimization Problems. ACS CENTRAL SCIENCE 2023; 9:1453-1465. [PMID: 37521801 PMCID: PMC10375572 DOI: 10.1021/acscentsci.3c00515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Indexed: 08/01/2023]
Abstract
Chemical and molecular-based computers may be promising alternatives to modern silicon-based computers. In particular, hybrid systems, where tasks are split between a chemical medium and traditional silicon components, may provide access and demonstration of chemical advantages such as scalability, low power dissipation, and genuine randomness. This work describes the development of a hybrid classical-molecular computer (HCMC) featuring an electrochemical reaction on top of an array of discrete electrodes with a fluorescent readout. The chemical medium, optical readout, and electrode interface combined with a classical computer generate a feedback loop to solve several canonical optimization problems in computer science such as number partitioning and prime factorization. Importantly, the HCMC makes constructive use of experimental noise in the optical readout, a milestone for molecular systems, to solve these optimization problems, as opposed to in silico random number generation. Specifically, we show calculations stranded in local minima can consistently converge on a global minimum in the presence of experimental noise. Scalability of the hybrid computer is demonstrated by expanding the number of variables from 4 to 7, increasing the number of possible solutions by 1 order of magnitude. This work provides a stepping stone to fully molecular approaches to solving complex computational problems using chemistry.
Collapse
Affiliation(s)
- Veronica
K. Krasecki
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Abhishek Sharma
- Department
of Chemistry, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Andrew C. Cavell
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Christopher Forman
- Department
of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Si Yue Guo
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Evan Thomas Jensen
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Mackinsey A. Smith
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Rachel Czerwinski
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Pascal Friederich
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Riley J. Hickman
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Nathan Gianneschi
- Department
of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Leroy Cronin
- Department
of Chemistry, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Randall H. Goldsmith
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| |
Collapse
|
3
|
Agiza AA, Oakley K, Rosenstein JK, Rubenstein BM, Kim E, Riedel M, Reda S. Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling. Nat Commun 2023; 14:496. [PMID: 36717558 PMCID: PMC9887006 DOI: 10.1038/s41467-023-36206-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 01/18/2023] [Indexed: 02/01/2023] Open
Abstract
Acid-base reactions are ubiquitous, easy to prepare, and execute without sophisticated equipment. Acids and bases are also inherently complementary and naturally map to a universal representation of "0" and "1." Here, we propose how to leverage acids, bases, and their reactions to encode binary information and perform information processing based upon the majority and negation operations. These operations form a functionally complete set that we use to implement more complex computations such as digital circuits and neural networks. We present the building blocks needed to build complete digital circuits using acids and bases for dual-rail encoding data values as complementary pairs, including a set of primitive logic functions that are widely applicable to molecular computation. We demonstrate how to implement neural network classifiers and some classes of digital circuits with acid-base reactions orchestrated by a robotic fluid handling device. We validate the neural network experimentally on a number of images with different formats, resulting in a perfect match to the in-silico classifier. Additionally, the simulation of our acid-base classifier matches the results of the in-silico classifier with approximately 99% similarity.
Collapse
Affiliation(s)
- Ahmed A. Agiza
- grid.40263.330000 0004 1936 9094Brown University, Providence, RI USA
| | - Kady Oakley
- grid.40263.330000 0004 1936 9094Brown University, Providence, RI USA
| | | | | | - Eunsuk Kim
- grid.40263.330000 0004 1936 9094Brown University, Providence, RI USA
| | - Marc Riedel
- grid.17635.360000000419368657University of Minnesota, Minneapolis, MN USA
| | - Sherief Reda
- grid.40263.330000 0004 1936 9094Brown University, Providence, RI USA
| |
Collapse
|
4
|
Kriukov DV, Koyuncu AH, Wong ASY. History Dependence in a Chemical Reaction Network Enables Dynamic Switching. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2107523. [PMID: 35257479 DOI: 10.1002/smll.202107523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/02/2022] [Indexed: 06/14/2023]
Abstract
This work describes an enzymatic autocatalytic network capable of dynamic switching under out-of-equilibrium conditions. The network, wherein a molecular fuel (trypsinogen) and an inhibitor (soybean trypsin inhibitor) compete for a catalyst (trypsin), is kept from reaching equilibria using a continuous flow stirred tank reactor. A so-called 'linear inhibition sweep' is developed (i.e., a molecular analogue of linear sweep voltammetry) to intentionally perturb the competition between autocatalysis and inhibition, and used to demonstrate that a simple molecular system, comprising only three components, is already capable of a variety of essential neuromorphic behaviors (hysteresis, synchronization, resonance, and adaptation). This research provides the first steps in the development of a strategy that uses the principles in systems chemistry to transform chemical reaction networks into platforms capable of neural network computing.
Collapse
Affiliation(s)
- Dmitrii V Kriukov
- Department of Molecules and Materials, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NH, The Netherlands
| | - A Hazal Koyuncu
- Department of Molecules and Materials, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NH, The Netherlands
| | - Albert S Y Wong
- Department of Molecules and Materials, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NH, The Netherlands
- MESA+ Institute for Nanotechnology, University of Twente, Drienerlolaan 5, Enschede, 7522 NH, The Netherlands
- BRAINS (Center for Brain-inspired Nano Systems), University of Twente, Drienerlolaan 5, Enschede, 7522 NH, The Netherlands
| |
Collapse
|
5
|
Sarkar K, Bonnerjee D, Srivastava R, Bagh S. A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing. Chem Sci 2021; 12:15821-15832. [PMID: 35024106 PMCID: PMC8672730 DOI: 10.1039/d1sc01505b] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/08/2021] [Indexed: 11/21/2022] Open
Abstract
Here, we adapted the basic concept of artificial neural networks (ANNs) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created cellular devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.
Collapse
Affiliation(s)
- Kathakali Sarkar
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
| | - Deepro Bonnerjee
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
| | - Rajkamal Srivastava
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
| | - Sangram Bagh
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
| |
Collapse
|