Merging machine learning and bioelectronics for closed-loop control of biological systems and homeostasis

Billman, 2020, Homeostasis: The Underappreciated and Far Too Often Ignored Central Organizing Principle of Physiology, Front. Physiol., 11, 200, 10.3389/fphys.2020.00200 Cannon, 1929, Organization for physiological homeostasis, Physiol. Rev., 9, 399, 10.1152/physrev.1929.9.3.399 Röder, P.V., Wu, B., Liu, Y., and Han, W. Pancreatic Regulation of Glucose Homeostasis. Pomatto, 2017, The role of declining adaptive homeostasis in ageing, J. Physiol., 595, 7275, 10.1113/JP275072 Bhave, 2021, Distributed sensor and actuator networks for closed-loop bioelectronic medicine, Mater. Today, 46, 125, 10.1016/j.mattod.2020.12.020 Chen, 2022, A wireless millimetric magnetoelectric implant for the endovascular stimulation of peripheral nerves, Nat. Biomed. Eng., 6, 706, 10.1038/s41551-022-00873-7 Goh, 2023, Machine learning for bioelectronics on wearable and implantable devices: challenges and potential, Tissue Eng. Part A, 29, 20, 10.1089/ten.tea.2022.0119 Raczkowska, 2022, Closed-Loop Visceral Bioelectronics Therapies, 1 Shin, 2022, NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC, IEEE J. Solid-State Circuits, 57, 3243, 10.1109/JSSC.2022.3204508 Wang, 2022, Wearable bioelectronics for chronic wound management, Adv. Funct. Mater., 32, 2111022, 10.1002/adfm.202111022 Pansodtee, 2021, The multi-channel potentiostat: Development and evaluation of a scalable mini-potentiostat array for investigating electrochemical reaction mechanisms, PLoS One, 16, e0257167, 10.1371/journal.pone.0257167 Sefah, 2009, Nucleic acid aptamers for biosensors and bio-analytical applications, Analyst, 134, 1765, 10.1039/b905609m Liu, 2017, Connecting biology to electronics: Molecular communication via redox modality, Adv. Healthc. Mater., 6, 1700789, 10.1002/adhm.201700789 Wong, 2010, Dynamic actuation using nano-bio interfaces, Mater. Today, 13, 14, 10.1016/S1369-7021(10)70105-X Selberg, 2020, Expanding biological control to bioelectronics with machine learning, Apl. Mater., 8, 120904, 10.1063/5.0027226 Strakosas, 2017, Taking Electrons out of Bioelectronics: From Bioprotonic Transistors to Ion Channels, Adv. Sci., 4, 1600527, 10.1002/advs.201600527 Obaid, 2020, Massively parallel microwire arrays integrated with CMOS chips for neural recording, Sci. Adv., 6, eaay2789, 10.1126/sciadv.aay2789 Ronchi, 2019, Single-Cell Electrical Stimulation Using CMOS-Based High-Density Microelectrode Arrays, Front. Neurosci., 13, 208, 10.3389/fnins.2019.00208 Song, 2019, Flexible electronic/optoelectronic microsystems with scalable designs for chronic biointegration, Proc. Natl. Acad. Sci. USA, 116, 15398, 10.1073/pnas.1907697116 Chen, 2020, Flexible inorganic bioelectronics, npj Flex. Electron., 4, 2, 10.1038/s41528-020-0065-1 Spyropoulos, 2019, Internal ion-gated organic electrochemical transistor: A building block for integrated bioelectronics, Sci. Adv., 5, eaau7378, 10.1126/sciadv.aau7378 Inal, 2018, Conjugated Polymers in Bioelectronics, Acc. Chem. Res., 51, 1368, 10.1021/acs.accounts.7b00624 Zhang, 2016, Nano-Bioelectronics, Chem. Rev., 116, 215, 10.1021/acs.chemrev.5b00608 Fang, 2017, Capacitively Coupled Arrays of Multiplexed Flexible Silicon Transistors for Long-Term Cardiac Electrophysiology, Nat. Biomed. Eng., 1, 0038, 10.1038/s41551-017-0038 Stavrinidou, 2015, Electronic plants, Sci. Adv., 1, e1501136, 10.1126/sciadv.1501136 Hemmatian, 2019, Electronic control of H+ current in a bioprotonic device with carbon nanotube porins, PLoS One, 14, e0212197, 10.1371/journal.pone.0212197 Pappa, 2020, Optical and Electronic Ion Channel Monitoring from Native Human Membranes, ACS Nano, 14, 12538, 10.1021/acsnano.0c01330 Jakešová, 2019, Optoelectronic control of single cells using organic photocapacitors, Sci. Adv., 5, eaav5265, 10.1126/sciadv.aav5265 Habib, 2019, Electro-plasmonic nanoantenna: A nonfluorescent optical probe for ultrasensitive label-free detection of electrophysiological signals, Sci. Adv., 5, eaav9786, 10.1126/sciadv.aav9786 Singer, 2020, Magnetoelectric Materials for Miniature, Wireless Neural Stimulation at Therapeutic Frequencies, Neuron, 107, 631, 10.1016/j.neuron.2020.05.019 Caruso, 2017, In Vivo Magnetic Recording of Neuronal Activity, Neuron, 95, 1283, 10.1016/j.neuron.2017.08.012 Strakosas, 2019, A non-enzymatic glucose sensor enabled by bioelectronic pH control, Sci. Rep., 9, 10844, 10.1038/s41598-019-46302-9 Bhokisham, 2020, A redox-based electrogenetic CRISPR system to connect with and control biological information networks, Nat. Commun., 11, 2427, 10.1038/s41467-020-16249-x Yu, 2020, Flexible Electrochemical Bioelectronics: The Rise of In Situ Bioanalysis, Adv. Mater., 32, e1902083, 10.1002/adma.201902083 Jia, 2022, A multi-ion electrophoretic pump for simultaneous on-chip delivery of H+, Na+, and Cl−, Apl. Mater., 10, 041112, 10.1063/5.0084570 Seitanidou, 2019, Modulating Inflammation in Monocytes Using Capillary Fiber Organic Electronic Ion Pumps, Adv. Healthc. Mater., 8, 1900813, 10.1002/adhm.201900813 Seitanidou, 2019, Overcoming transport limitations in miniaturized electrophoretic delivery devices, Lab Chip, 19, 1427, 10.1039/C9LC00038K Poxson, 2019, Capillary-Fiber Based Electrophoretic Delivery Device, ACS Appl. Mater. Interfaces, 11, 14200, 10.1021/acsami.8b22680 Williamson, 2015, Controlling Epileptiform Activity with Organic Electronic Ion Pumps, Adv. Mater., 27, 3138, 10.1002/adma.201500482 Long, 2021, Wearable and implantable electroceuticals for therapeutic electrostimulations, Adv. Sci., 8, 2004023, 10.1002/advs.202004023 Sunwoo, 2021, Wearable and implantable soft bioelectronics: device designs and material strategies, Annu. Rev. Chem. Biomol. Eng., 12, 359, 10.1146/annurev-chembioeng-101420-024336 Strakosas, 2019, A Bioelectronic Platform Modulates pH in Biologically Relevant Conditions, Adv. Sci., 6, 1800935, 10.1002/advs.201800935 Selberg, 2018, The potential for convergence between synthetic biology and bioelectronics, Cell Syst., 7, 231, 10.1016/j.cels.2018.08.007 Selberg, 2020, Machine Learning-Driven Bioelectronics for Closed-Loop Control of Cells, Adv. Intell. Syst., 2, 2070122, 10.1002/aisy.202070122 El-Samad, 2021, Biological feedback control—Respect the loops, Cell Syst., 12, 477, 10.1016/j.cels.2021.05.004 Iglesias, 2010 Del Vecchio, 2016, Control theory meets synthetic biology, J. R. Soc. Interface, 13, 20160380, 10.1098/rsif.2016.0380 Purnick, 2009, The second wave of synthetic biology: from modules to systems, Nat Rev Mol Cell Bio, 10, 410, 10.1038/nrm2698 Benner, 2005, Synthetic biology, Nat. Rev. Genet., 6, 533, 10.1038/nrg1637 Boo, 2019, Host-aware synthetic biology, Curr. Opin. Syst. Biol., 14, 66, 10.1016/j.coisb.2019.03.001 Afroz, 2013, Understanding and exploiting feedback in synthetic biology, Chem. Eng. Sci., 103, 79, 10.1016/j.ces.2013.02.017 Menolascina, 2014, In-vivo real-time control of protein expression from endogenous and synthetic gene networks, PLoS Comput. Biol., 10, e1003625, 10.1371/journal.pcbi.1003625 Aoki, 2019, A universal biomolecular integral feedback controller for robust perfect adaptation, Nature, 570, 533, 10.1038/s41586-019-1321-1 Del Vecchio, 2015 Lugagne, 2017, Balancing a genetic toggle switch by real-time feedback control and periodic forcing, Nat. Commun., 8, 1671, 10.1038/s41467-017-01498-0 Comerci, 2022, Localized electrical stimulation triggers cell-type-specific proliferation in biofilms, Cell Syst., 13, 488, 10.1016/j.cels.2022.04.001 Shakiba, 2021, Context-aware synthetic biology by controller design: Engineering the mammalian cell, Cell Syst., 12, 561, 10.1016/j.cels.2021.05.011 Ghorbani, 2014, 4839 Krawczyk, 2020, Electrogenetic cellular insulin release for real-time glycemic control in type 1 diabetic mice, Science, 368, 993, 10.1126/science.aau7187 Din, 2020, Interfacing gene circuits with microelectronics through engineered population dynamics, Sci. Adv., 6, eaaz8344, 10.1126/sciadv.aaz8344 Boutet, 2021, Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning, Nat. Commun., 12, 3043, 10.1038/s41467-021-23311-9 Jung, 2023 Patel, 2019, Precision electronic medicine in the brain, Nat. Biotechnol., 37, 1007, 10.1038/s41587-019-0234-8 Rouhani, 2023, Suppression of seizure in childhood absence epilepsy using robust control of deep brain stimulation: a simulation study, Sci. Rep., 13, 461, 10.1038/s41598-023-27527-1 Parastarfeizabadi, 2017, Advances in closed-loop deep brain stimulation devices, J. NeuroEng. Rehabil., 14, 79, 10.1186/s12984-017-0295-1 Angermueller, 2016, Deep learning for computational biology, Mol. Syst. Biol., 12, 878, 10.15252/msb.20156651 Rahimi, 2021, Toward smart carbon capture with machine learning, Cell Rep. Phys. Sci., 2, 100396, 10.1016/j.xcrp.2021.100396 Shailaja, 2018, 910 Chen, 2019, How to develop machine learning models for healthcare, Nat. Mater., 18, 410, 10.1038/s41563-019-0345-0 Sargent, 2022 Marquez, 2019, 120 Jiang, 2021, Using machine learning technologies in pressure injury management: systematic review, JMIR Med. Inform., 9, e25704, 10.2196/25704 Schackart, 2021, Machine learning enhances the performance of bioreceptor-free biosensors, Sensors, 21, 5519, 10.3390/s21165519 Zlobina, 2022, The role of machine learning in advancing precision medicine with feedback control, Cell Rep. Phys. Sci., 3, 101149, 10.1016/j.xcrp.2022.101149 Girdhar, 2022, Classification of White blood cell using Convolution Neural Network, Biomed. Signal Process Control, 71, 103156, 10.1016/j.bspc.2021.103156 Nassif, 2022, Breast cancer detection using artificial intelligence techniques: A systematic literature review, Artif. Intell. Med., 127, 102276, 10.1016/j.artmed.2022.102276 Carrión, 2022, Automatic wound detection and size estimation using deep learning algorithms, PLoS Comput. Biol., 18, e1009852, 10.1371/journal.pcbi.1009852 Novikov, 2018, Fully convolutional architectures for multiclass segmentation in chest radiographs, IEEE Trans. Med. Imaging, 37, 1865, 10.1109/TMI.2018.2806086 Lugagne, 2020, DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning, PLoS Comput. Biol., 16, e1007673, 10.1371/journal.pcbi.1007673 Van Valen, 2016, Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments, PLoS Comput. Biol., 12, e1005177, 10.1371/journal.pcbi.1005177 Joorabloo, 2022, Using artificial neural network for design and development of PVA/chitosan/starch/heparinized nZnO hydrogels for enhanced wound healing, J. Ind. Eng. Chem., 108, 88, 10.1016/j.jiec.2021.12.027 Camacho, 2018, Next-generation machine learning for biological networks, Cell, 173, 1581, 10.1016/j.cell.2018.05.015 Jafari, 2021, Feedback control of bioelectronic devices using machine learning, IEEE Control Syst. Lett., 5, 1133, 10.1109/LCSYS.2020.3015597 Greener, 2022, A guide to machine learning for biologists, Nat. Rev. Mol. Cell Biol., 23, 40, 10.1038/s41580-021-00407-0 Chicco, 2017, Ten quick tips for machine learning in computational biology, BioData Min., 10, 35, 10.1186/s13040-017-0155-3 Lee, 2022, Ten quick tips for deep learning in biology, PLoS Comput. Biol., 18, e1009803, 10.1371/journal.pcbi.1009803 Volk, 2020, Biosystems design by machine learning, ACS Synth. Biol., 9, 1514, 10.1021/acssynbio.0c00129 Haykin, 2009 Goodfellow, 2016 Jafari, 2019, 163 Famm, 2013, A jump-start for electroceuticals, Nature, 496, 159, 10.1038/496159a Birmingham, 2014, Bioelectronic medicines: a research roadmap, Nat. Rev. Drug Discov., 13, 399, 10.1038/nrd4351 Güemes Gonzalez, 2020, Closed-loop bioelectronic medicine for diabetes management, Bioelectron. Med., 6, 11, 10.1186/s42234-020-00046-4 Mickle, 2019, A wireless closed-loop system for optogenetic peripheral neuromodulation, Nature, 565, 361, 10.1038/s41586-018-0823-6 ávan Doremaele, 2019, Towards organic neuromorphic devices for adaptive sensing and novel computing paradigms in bioelectronics, J. Mater. Chem. C Mater., 7, 12754, 10.1039/C9TC03247A Kim, S., Baek, S., Sluyter, R., Konstantinov, K., Kim, J.H., Kim, S., and Kim, Y.H. Wearable and implantable bioelectronics as eco-friendly and patient-friendly integrated nanoarchitectonics for next-generation smart healthcare technology. EcoMat, e12356. Shirzaei Sani, 2023, A stretchable wireless wearable bioelectronic system for multiplexed monitoring and combination treatment of infected chronic wounds, Sci. Adv., 9, eadf7388, 10.1126/sciadv.adf7388 Yao, 2020, Flexible bioelectronics for physiological signals sensing and disease treatment, J. Materiomics, 6, 397, 10.1016/j.jmat.2019.12.005 Zhou, 2022, Smart bioelectronics and biomedical devices, Biodes. Manuf., 5, 1, 10.1007/s42242-021-00179-8 Quiroz, 2019, The evolution of control algorithms in artificial pancreas: A historical perspective, Annu. Rev. Control, 48, 222, 10.1016/j.arcontrol.2019.07.004 Ganzer, 2019, Opportunities and challenges for developing closed-loop bioelectronic medicines, Neural Regen. Res., 14, 46, 10.4103/1673-5374.243697 Kovatchev, 2019, Diabetes technology: monitoring, analytics, and optimal control, Cold Spring Harb. Perspect. Med., 9, a034389, 10.1101/cshperspect.a034389 Elowitz, 2002, Stochastic gene expression in a single cell, Science, 297, 1183, 10.1126/science.1070919 Kwok, 2010, Five Hard Truths for Synthetic Biology, Nature, 463, 288, 10.1038/463288a Ang, 2005, PID control system analysis, design, and technology, IEEE Trans. Control Syst. Technol., 13, 559, 10.1109/TCST.2005.847331 Li, 2006, PID control system analysis and design, IEEE Control Systems Magazine, 26, 32, 10.1109/MCS.2006.1580152 O'Dwyer, 2009 Rocchitta, 2016, Enzyme Biosensors for Biomedical Applications: Strategies for Safeguarding Analytical Performances in Biological Fluids, Sensors, 16, 780, 10.3390/s16060780 Liu, 2019, Low fouling strategies for electrochemical biosensors targeting disease biomarkers, Anal. Methods, 11, 702, 10.1039/C8AY02674B Polikov, 2005, Response of brain tissue to chronically implanted neural electrodes, J. Neurosci. Methods, 148, 1, 10.1016/j.jneumeth.2005.08.015 Smolen, 2000, Mathematical modeling of gene networks, Neuron, 26, 567, 10.1016/S0896-6273(00)81194-0 Zheng, 2021, Exploiting machine learning for bestowing intelligence to microfluidics, Biosens. Bioelectron., 194, 113666, 10.1016/j.bios.2021.113666 Kan, 2017, Machine learning applications in cell image analysis, Immunol. Cell Biol., 95, 525, 10.1038/icb.2017.16 Goecks, 2020, How Machine Learning Will Transform Biomedicine, Cell, 181, 92, 10.1016/j.cell.2020.03.022 Maltarollo, 2013, Applications of artificial neural networks in chemical problems, Artif. Neural Networks-Architect. Appl., 203 Park, 2015, Deep learning for regulatory genomics, Nat. Biotechnol., 33, 825, 10.1038/nbt.3313 Narendra, 1990, Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Netw., 1, 4, 10.1109/72.80202 Lavretsky, 2013, Robust adaptive control, 317 Hagan, 1999, 1642 Spooner, 2004 Khargonekar, 2018, Advancing systems and control research in the era of ML and AI, Annu. Rev. Control, 45, 1, 10.1016/j.arcontrol.2018.04.001 Hosseini Jafari, 2021, A feedback control architecture for bioelectronic devices with applications to wound healing, J. R. Soc. Interface, 18, 20210497, 10.1098/rsif.2021.0497 Castiglione, 2014, Modeling biology spanning different scales: an open challenge, BioMed Res. Int., 2014, 902545, 10.1155/2014/902545 Lehninger, 2008 Bean, 2007, The action potential in mammalian central neurons, Nat. Rev. Neurosci., 8, 451, 10.1038/nrn2148 Pérez-Ortín, 2007, Genomics and gene transcription kinetics in yeast, Trends Genet., 23, 250, 10.1016/j.tig.2007.03.006 Costa, 2011, Continuous live imaging of adult neural stem cell division and lineage progression in vitro, Development, 138, 1057, 10.1242/dev.061663 Seiler, 2014, Time-lapse microscopy and classification of 2D human mesenchymal stem cells based on cell shape picks up myogenic from osteogenic and adipogenic differentiation, J. Tissue Eng. Regen. Med., 8, 737, 10.1002/term.1575 Schofield, 2020, Bioelectrical understanding and engineering of cell biology, J. R. Soc. Interface, 17, 20200013, 10.1098/rsif.2020.0013 Anisuzzaman, 2022, Image-based artificial intelligence in wound assessment: A systematic review, Adv. Wound Care, 11, 687, 10.1089/wound.2021.0091 Mostafalu, 2018, Smart bandage for monitoring and treatment of chronic wounds, Small, 14, 1703509, 10.1002/smll.201703509 Veredas, 2015, Wound image evaluation with machine learning, Neurocomputing, 164, 112, 10.1016/j.neucom.2014.12.091 Carrión, 2022, 446 Lamnabhi-Lagarrigue, 2017, Systems & Control for the future of humanity, research agenda: Current and future roles, impact and grand challenges, Annu. Rev. Control, 43, 1, 10.1016/j.arcontrol.2017.04.001 2020, Building a bioelectronic medicine movement 2019: insights from leaders in industry, academia, and research, Bioelectron. Med., 6, 1, 10.1186/s42234-020-0037-8