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Hwang GM, Simonian AL. Special Issue-Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience? BIOSENSORS 2024; 14:68. [PMID: 38391987 PMCID: PMC10886788 DOI: 10.3390/bios14020068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 01/25/2024] [Indexed: 02/24/2024]
Abstract
In partnership with the Air Force Office of Scientific Research (AFOSR), the National Science Foundation's (NSF) Emerging Frontiers and Multidisciplinary Activities (EFMA) office of the Directorate for Engineering (ENG) launched an Emerging Frontiers in Research and Innovation (EFRI) topic for the fiscal years FY22 and FY23 entitled "Brain-inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence" (BRAID) [...].
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Affiliation(s)
- Grace M. Hwang
- Johns Hopkins University Applied Physics Laboratory, 111000 Johns Hopkins Road, Laurel, MD 20723, USA
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McGuinness KN, Fehon N, Feehan R, Miller M, Mutter AC, Rybak LA, Nam J, AbuSalim JE, Atkinson JT, Heidari H, Losada N, Kim JD, Koder RL, Lu Y, Silberg JJ, Slusky JSG, Falkowski PG, Nanda V. The energetics and evolution of oxidoreductases in deep time. Proteins 2024; 92:52-59. [PMID: 37596815 DOI: 10.1002/prot.26563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/06/2023] [Indexed: 08/20/2023]
Abstract
The core metabolic reactions of life drive electrons through a class of redox protein enzymes, the oxidoreductases. The energetics of electron flow is determined by the redox potentials of organic and inorganic cofactors as tuned by the protein environment. Understanding how protein structure affects oxidation-reduction energetics is crucial for studying metabolism, creating bioelectronic systems, and tracing the history of biological energy utilization on Earth. We constructed ProtReDox (https://protein-redox-potential.web.app), a manually curated database of experimentally determined redox potentials. With over 500 measurements, we can begin to identify how proteins modulate oxidation-reduction energetics across the tree of life. By mapping redox potentials onto networks of oxidoreductase fold evolution, we can infer the evolution of electron transfer energetics over deep time. ProtReDox is designed to include user-contributed submissions with the intention of making it a valuable resource for researchers in this field.
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Affiliation(s)
- Kenneth N McGuinness
- Department of Natural Sciences, Caldwell University, Caldwell, New Jersey, USA
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | - Nolan Fehon
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey, USA
| | - Ryan Feehan
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Michelle Miller
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey, USA
| | - Andrew C Mutter
- Department of Physics, The City College of New York, New York, New York, USA
| | - Laryssa A Rybak
- Department of Physics, The City College of New York, New York, New York, USA
| | - Justin Nam
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | - Jenna E AbuSalim
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | - Joshua T Atkinson
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas, USA
| | - Hirbod Heidari
- Department of Chemistry, University of Texas at Austin, Austin, Texas, USA
| | - Natalie Losada
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | - J Dongun Kim
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey, USA
| | - Ronald L Koder
- Department of Physics, The City College of New York, New York, New York, USA
| | - Yi Lu
- Department of Chemistry, University of Texas at Austin, Austin, Texas, USA
| | - Jonathan J Silberg
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas, USA
| | - Joanna S G Slusky
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, USA
| | - Paul G Falkowski
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey, USA
- Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, New Jersey, USA
| | - Vikas Nanda
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA
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Beahm DR, Deng Y, DeAngelo TM, Sarpeshkar R. Drug Cocktail Formulation via Circuit Design. IEEE TRANSACTIONS ON MOLECULAR, BIOLOGICAL, AND MULTI-SCALE COMMUNICATIONS 2023; 9:28-48. [PMID: 37397625 PMCID: PMC10312325 DOI: 10.1109/tmbmc.2023.3246928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Drug cocktail therapies are a powerful tool against diseases that exhibit such dynamics. We show that just six key states, which are represented in a feedback circuit, enable drug-cocktail formulation: 1) healthy cell number; 2) infected cell number; 3) extracellular pathogen number; 4) intracellular pathogenic molecule number; 5) innate immune system strength; and 6) adaptive immune system strength. To enable drug cocktail formulation, the model represents the effects of the drugs in the circuit. For example, a nonlinear feedback circuit model fits measured clinical data, represents cytokine storm and adaptive autoimmune behavior, and accounts for age, sex, and variant effects for SARS-CoV-2 with few free parameters. The latter circuit model provided three quantitative insights on the optimal timing and dosage of drug components in a cocktail: 1) antipathogenic drugs should be given early in the infection, but immunosuppressant timing involves a tradeoff between controlling pathogen load and mitigating inflammation; 2) both within and across-class combinations of drugs have synergistic effects; 3) if they are administered sufficiently early in the infection, anti-pathogenic drugs are more effective at mitigating autoimmune behavior than immunosuppressant drugs.
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Affiliation(s)
| | - Yijie Deng
- Thayer School or Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Thomas M DeAngelo
- Thayer School or Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Rahul Sarpeshkar
- Departments of Engineering, Physics, Microbiology & Immunobiology, and Molecular & Systems Biology, Dartmouth College, Hanover, NH 03755 USA
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Zhao H, Sarpeshkar R, Mandal S. 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 2023; 70:3-16. [PMID: 39157673 PMCID: PMC11329235 DOI: 10.1109/tcsi.2022.3199561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
Abstract
This paper describes an adaptive noise generator circuit suitable for on-chip simulations of stochastic chemical kinetics. The circuit uses amplified BJT white noise and adaptive low-pass filtering to emulate the power spectrum and autocorrelation of random telegraph signals (RTS) with Poisson-distributed level transitions. A current-mode implementation in the AMS 0.35 μm BiCMOS process shows excellent agreement with theoretical results from the Gillespie stochastic simulation algorithm over a 60 dB range in mean current levels (modeling molecule count numbers). The circuit has an estimated layout area of 0.032 mm2 and typically consumes 400 μA, which are 73% and 50% less, respectively, than prior implementations. Moreover, it does not require any off-chip capacitors. Experimental results from a discrete board-level implementation of the circuit are in good agreement with theoretical predictions.
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Affiliation(s)
- Haixiang Zhao
- Dept. of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Rahul Sarpeshkar
- Engineering departments of Engineering, Physics, Microbiology&Immunology, and Molecular and Systems Biology, Dartmouth College, Hanover, NH 03755, USA
| | - Soumyajit Mandal
- Instrumentation Division, Brookhaven NationalLaboratory, Upton, NY 11973, USA
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Deng Y, Beahm DR, Ran X, Riley TG, Sarpeshkar R. Rapid modeling of experimental molecular kinetics with simple electronic circuits instead of with complex differential equations. Front Bioeng Biotechnol 2022; 10:947508. [PMID: 36246369 PMCID: PMC9554301 DOI: 10.3389/fbioe.2022.947508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Kinetic modeling has relied on using a tedious number of mathematical equations to describe molecular kinetics in interacting reactions. The long list of differential equations with associated abstract variables and parameters inevitably hinders readers’ easy understanding of the models. However, the mathematical equations describing the kinetics of biochemical reactions can be exactly mapped to the dynamics of voltages and currents in simple electronic circuits wherein voltages represent molecular concentrations and currents represent molecular fluxes. For example, we theoretically derive and experimentally verify accurate circuit models for Michaelis-Menten kinetics. Then, we show that such circuit models can be scaled via simple wiring among circuit motifs to represent more and arbitrarily complex reactions. Hence, we can directly map reaction networks to equivalent circuit schematics in a rapid, quantitatively accurate, and intuitive fashion without needing mathematical equations. We verify experimentally that these circuit models are quantitatively accurate. Examples include 1) different mechanisms of competitive, noncompetitive, uncompetitive, and mixed enzyme inhibition, important for understanding pharmacokinetics; 2) product-feedback inhibition, common in biochemistry; 3) reversible reactions; 4) multi-substrate enzymatic reactions, both important in many metabolic pathways; and 5) translation and transcription dynamics in a cell-free system, which brings insight into the functioning of all gene-protein networks. We envision that circuit modeling and simulation could become a powerful scientific communication language and tool for quantitative studies of kinetics in biology and related fields.
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Affiliation(s)
- Yijie Deng
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
| | | | - Xinping Ran
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
| | - Tanner G. Riley
- School of Undergraduate Arts and Sciences, Dartmouth College, Hanover, NH, United States
| | - Rahul Sarpeshkar
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
- Departments of Engineering, Microbiology and Immunology, Physics, and Molecular and Systems Biology, Dartmouth College, Hanover, NH, United States
- *Correspondence: Rahul Sarpeshkar,
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Choi S. Electrogenic Bacteria Promise New Opportunities for Powering, Sensing, and Synthesizing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2107902. [PMID: 35119203 DOI: 10.1002/smll.202107902] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/06/2022] [Indexed: 06/14/2023]
Abstract
Considerable research efforts into the promises of electrogenic bacteria and the commercial opportunities they present are attempting to identify potential feasible applications. Metabolic electrons from the bacteria enable electricity generation sufficient to power portable or small-scale applications, while the quantifiable electric signal in a miniaturized device platform can be sensitive enough to monitor and respond to changes in environmental conditions. Nanomaterials produced by the electrogenic bacteria can offer an innovative bottom-up biosynthetic approach to synergize bacterial electron transfer and create an effective coupling at the cell-electrode interface. Furthermore, electrogenic bacteria can revolutionize the field of bioelectronics by effectively interfacing electronics with microbes through extracellular electron transfer. Here, these new directions for the electrogenic bacteria and their recent integration with micro- and nanosystems are comprehensively discussed with specific attention toward distinct applications in the field of powering, sensing, and synthesizing. Furthermore, challenges of individual applications and strategies toward potential solutions are provided to offer valuable guidelines for practical implementation. Finally, the perspective and view on how the use of electrogenic bacteria can hold immeasurable promise for the development of future electronics and their applications are presented.
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Affiliation(s)
- Seokheun Choi
- Bioelectronics & Microsystems Laboratory, Department of Electrical & Computer Engineering, State University of New York at Binghamton, Binghamton, NY, 13902, USA
- Center for Research in Advanced Sensing Technologies & Environmental Sustainability, State University of New York at Binghamton, Binghamton, NY, 13902, USA
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Shallom D, Naiger D, Weiss S, Tuller T. Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware. ACS Synth Biol 2021; 10:3489-3506. [PMID: 34813269 PMCID: PMC8689694 DOI: 10.1021/acssynbio.1c00415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In recent years, intracellular biophysical simulations have been used with increasing frequency not only for answering basic scientific questions but also in the field of synthetic biology. However, since these models include networks of interaction between millions of components, they are extremely time-consuming and cannot run easily on parallel computers. In this study, we demonstrate for the first time a novel approach addressing this challenge by using a dedicated hardware designed specifically to simulate such processes. As a proof of concept, we specifically focus on mRNA translation, which is the process consuming most of the energy in the cell. We design a hardware that simulates translation in Escherichia coli and Saccharomyces cerevisiae for thousands of mRNAs and ribosomes, which is in orders of magnitude faster than a similar software solution. With the sharp increase in the amount of genomic data available today and the complexity of the corresponding models inferred from them, we believe that the strategy suggested here will become common and can be used among others for simulating entire cells with all gene expression steps.
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Affiliation(s)
- David Shallom
- School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Danny Naiger
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Shlomo Weiss
- School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
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Beahm DR, Deng Y, Riley TG, Sarpeshkar R. Cytomorphic Electronic Systems: A review and perspective. IEEE NANOTECHNOLOGY MAGAZINE 2021; 15:41-53. [DOI: 10.1109/mnano.2021.3113192] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Deng Y, Beahm DR, Ionov S, Sarpeshkar R. Measuring and modeling energy and power consumption in living microbial cells with a synthetic ATP reporter. BMC Biol 2021; 19:101. [PMID: 34001118 PMCID: PMC8130387 DOI: 10.1186/s12915-021-01023-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 04/10/2021] [Indexed: 11/29/2022] Open
Abstract
Background Adenosine triphosphate (ATP) is the main energy carrier in living organisms, critical for metabolism and essential physiological processes. In humans, abnormal regulation of energy levels (ATP concentration) and power consumption (ATP consumption flux) in cells is associated with numerous diseases from cancer, to viral infection and immune dysfunction, while in microbes it influences their responses to drugs and other stresses. The measurement and modeling of ATP dynamics in cells is therefore a critical component in understanding fundamental physiology and its role in pathology. Despite the importance of ATP, our current understanding of energy dynamics and homeostasis in living cells has been limited by the lack of easy-to-use ATP sensors and the lack of models that enable accurate estimates of energy and power consumption related to these ATP dynamics. Here we describe a dynamic model and an ATP reporter that tracks ATP in E. coli over different growth phases. Results The reporter is made by fusing an ATP-sensing rrnB P1 promoter with a fast-folding and fast-degrading GFP. Good correlations between reporter GFP and cellular ATP were obtained in E. coli growing in both minimal and rich media and in various strains. The ATP reporter can reliably monitor bacterial ATP dynamics in response to nutrient availability. Fitting the dynamics of experimental data corresponding to cell growth, glucose, acetate, dissolved oxygen, and ATP yielded a mathematical and circuit model. This model can accurately predict cellular energy and power consumption under various conditions. We found that cellular power consumption varies significantly from approximately 0.8 and 0.2 million ATP/s for a tested strain during lag and stationary phases to 6.4 million ATP/s during exponential phase, indicating ~ 8–30-fold changes of metabolic rates among different growth phases. Bacteria turn over their cellular ATP pool a few times per second during the exponential phase and slow this rate by ~ 2–5-fold in lag and stationary phases. Conclusion Our rrnB P1-GFP reporter and kinetic circuit model provide a fast and simple way to monitor and predict energy and power consumption dynamics in bacterial cells, which can impact fundamental scientific studies and applied medical treatments in the future.
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Affiliation(s)
- Yijie Deng
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | | | - Steven Ionov
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Rahul Sarpeshkar
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA. .,Departments of Engineering, Microbiology & Immunology, Physics, and Molecular and Systems Biology, Dartmouth College, Hanover, NH, 03755, USA.
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