Using living organisms as logic gates or even general computing devices. Viewed as a computational science this might be considered an especially quaint sub-field of Turing-Machine-hunting. OTOH, the ability to bake real computation into the structures of life has some obvious applications and surely non-obvious ones.
As distinct from doing biology-like computation using computers - that’s a biomimetic algorithm.
Projects like Microsoft’s Station B and Biological computation unit are angling for some market share in this field. There are many others.
References
Abramson, Charles I., and Michael Levin. 2021. “Behaviorist Approaches to Investigating Memory and Learning: A Primer for Synthetic Biology and Bioengineering.” Communicative & Integrative Biology 14 (1): 230–47.
Adamatzky, Andrew, and Theresa Schubert. 2014. “Slime Mold Microfluidic Logical Gates.” Materials Today 17 (2): 86–91.
Baer, R M, and H M Martinez. 1974. “Automata and Biology.” Annual Review of Biophysics and Bioengineering 3 (1): 255–91.
Beniaguev, David, Idan Segev, and Michael London. 2021. “Single Cortical Neurons as Deep Artificial Neural Networks.” Neuron 109 (17): 2727–2739.e3.
Bongard, Joshua, and Michael Levin. 2021. “Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior.” Frontiers in Ecology and Evolution 9.
Bray. 1995. “Protein Molecules as Computational Elements in Living Cells.” Nature 376: 307–12.
Brette, Romain. 2012. “Computing with Neural Synchrony.” PLoS Comput Biol 8 (6): e1002561.
Chen, Zibo, James M. Linton, Ronghui Zhu, and Michael B. Elowitz. 2022. “A Synthetic Protein-Level Neural Network in Mammalian Cells.” bioRxiv.
Fields, Chris, and Michael Levin. 2020. “How Do Living Systems Create Meaning?” Philosophies 5 (4): 36.
———. 2021. “Metabolic limits on classical information processing by biological cells.” Bio Systems 209 (November): 104513.
Gopalkrishnan, Manoj. 2015. “A Scheme for Molecular Computation of Maximum Likelihood Estimators for Log-Linear Models.” arXiv:1506.03172 [Cs, Math, q-Bio, Stat], June.
Klein, Brennan, Erik Hoel, Anshuman Swain, Ross Griebenow, and Michael Levin. 2021. “Evolution and Emergence: Higher Order Information Structure in Protein Interactomes Across the Tree of Life.” Integrative Biology 13 (12): 283–94.
Levin, Michael. 2019. “The Computational Boundary of a ‘Self’: Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition.” Frontiers in Psychology 10.
Levin, Michael, Fred Keijzer, Pamela Lyon, and Detlev Arendt. 2021. “Uncovering Cognitive Similarities and Differences, Conservation and Innovation.” Philosophical Transactions of the Royal Society B: Biological Sciences 376 (1821): 20200458.
Levin, Rafael Yuste, Michael. n.d. “New Clues about the Origins of Biological Intelligence.” Scientific American.
Liang, Yuchen, Chaitanya K. Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J. Zaki, and Dmitry Krotov. 2021. “Can a Fruit Fly Learn Word Embeddings?” arXiv:2101.06887 [Cs, q-Bio, Stat], January.
Lyon, Pamela, Fred Keijzer, Detlev Arendt, and Michael Levin. 2021. “Reframing Cognition: Getting down to Biological Basics.” Philosophical Transactions of the Royal Society B: Biological Sciences 376 (1820): 20190750.
Manicka, Santosh, and Michael Levin. 2022. “Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation.” Entropy 24 (1): 107.
McGee, Ryan Seamus, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, and Carl T. Bergstrom. 2022. “The Cost of Information Acquisition by Natural Selection.” bioRxiv.
Orellana, Josue, Jordan Rodu, and Robert E. Kass. 2017. “Population Vectors Can Provide Near Optimal Integration of Information.” Neural Computation 29 (8): 2021–29.
Randazzo, Ettore, Alexander Mordvintsev, Eyvind Niklasson, Michael Levin, and Sam Greydanus. 2020. “Self-Classifying MNIST Digits.” Distill 5 (8): e00027.002.
Semenov, Sergey N., Lewis J. Kraft, Alar Ainla, Mengxia Zhao, Mostafa Baghbanzadeh, Victoria E. Campbell, Kyungtae Kang, Jerome M. Fox, and George M. Whitesides. 2016. “Autocatalytic, Bistable, Oscillatory Networks of Biologically Relevant Organic Reactions.” Nature 537 (7622): 656–60.
Straszak, Damian, and Nisheeth K. Vishnoi. 2016. “IRLS and Slime Mold: Equivalence and Convergence.” arXiv:1601.02712 [Cs, Math, Stat], January.
Vanchurin, Vitaly, Yuri I. Wolf, Mikhail Katsnelson, and Eugene V. Koonin. 2021. “Towards a Theory of Evolution as Multilevel Learning.” Cold Spring Harbor Laboratory.
Watson, Richard A., Michael Levin, and Christopher L. Buckley. 2022. “Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality.” Frontiers in Ecology and Evolution 10.
Watson, Richard A., Rob Mills, C. L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, et al. 2016. “Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.” Evolutionary Biology 43 (4): 553–81.
Wilkinson, Darren J. 2009. “Stochastic Modelling for Quantitative Description of Heterogeneous Biological Systems.” Nature Reviews Genetics 10 (2): 122–33.
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