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Machine Learning Inspired Synthetic Biology: Neuromorphic Computing in Mammalian Cells by Andrew Moorman

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Machine Learning Inspired Synthetic Biology: Neuromorphic Computing in Mammalian Cells by Andrew Moorman Machine Learning Inspired Synthetic Biology: Neuromorphic Computing in Mammalian Cells by Andrew Moorman B.Arch. Cornell University, 2017 Submitted to the MIT Department of Architecture and the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degrees of Master of Science in Architecture Studies and Master of Science in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology February 2020 © 2020 Massachusetts Institute of Technology. All rights reserved Signature of Author .............................................................................................................................................. MIT Department of Architecture MIT Department of Electrical Engineering and Computer Science January 17, 2020 Certified by ........................................................................................................................................................... Ron Weiss Professor of Biological Engineering and Electrical Engineering and Computer Science Thesis Supervisor Certified by ........................................................................................................................................................... Skylar Tibbits Associate Professor of Architecture Thesis Supervisor Accepted by .......................................................................................................................................................... Leslie K. Norford Professor of Building Technology Chair, Department Committee on Graduate Students Accepted by .......................................................................................................................................................... Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science Chair, Department Committee on Graduate Students 2 Machine Learning Inspired Synthetic Biology: Neuromorphic Computing in Mammalian Cells by Andrew Moorman Submitted to the Department of Architecture and Department of Electrical Engineering and Computer Science on February 17, 2020, in partial fulllment of the requirements for the degrees of Master of Science in Electrical Engineering and Computer Science and Master of Science in Architecture Studies Abstract Synthetic biologists seek to collect, rene, and repackage nature so that it’s easier to design new and reliable biological systems, typically at the cellular or multicellular level. These redesigned systems are often referred to as “biological circuits,” for their ability to perform operations on biomolecular signals, rather than electrical signals, and for their aim to behave as predictably and modularly as would integrated circuits in a computer. In natural and synthetic biological systems, the abstraction of these circuits’ behaviors to dig- ital computation is often appropriate, especially in decision-making settings wherein the output is selected to coordinate a discrete set of outcomes, e.g. developmental networks or disease-state classication circuits. However, there are challenges in engineering entire genetic systems that mimic digital logic. Biological molecules do not generally exist at only two possible concentra- tions but vary over an analog range of concentrations, and are ordinarily uncompartmentalized in the cell. As a result, scaling biological circuits which rely on digital logic schemes can prove dicult in practice. Neuromorphic devices represent a promising computing paradigm which aims to reproduce desirable, high-level characteristics inspired by how the brain processes information - features like tunable signal processing and resource ecient scaling. They are a versatile substrate for compu- tation, and, in engineered biological systems, marry the practical benets of digital and analog signal processing. As the decision-making intelligence of engineered-cell therapies, neuromor- phic gene circuits could replace digital logic schemes with a modular and reprogrammable ana- log template, allowing for more sophisticated computation using fewer resources. This template could then be adapted either externally or autonomously in long-term single cell medicine. Here, I describe the implementation of in-vivo neuromorphic circuits in human cell culture models as a proof-of-concept for their application to personalized medicine. While biology has long served as inspiration for the articial intelligence community, this work will help launch a new, interactive relationship between the two elds, in which nature of- 3 fers more to AI than a helpful metaphor. Synthetic biology provides a rigorous framework to actively probe how learning systems work in living things, closing the loop between traditional machine learning and naturally intelligent systems. This thesis oers a starting point from which to pursue cell therapeutic strategies and multi-step genetic dierentiation programs, while expos- ing the inherent learning capabilities of biology (e.g., self-repair, operation in noisy environments, etc.). Simultaneously, the results included lay groundwork to analyze the role of machine learn- ing in medicine, where its dicult interpretability contradicts the need to guarantee stable, safe, and ecacious therapies. This thesis should not only spur future research in the use of these approaches for personalized medicine, but also broaden the landscape of academics who nd interest in and relevance to its concerns. Thesis Supervisor: Ron Weiss Title: Professor of Biological Engineering and Electrical Engineering and Computer Science Thesis Supervisor: Skylar Tibbits Title: Associate Professor of Architecture 4 Acknowledgments I would rst like to thank my thesis advisors Prof. Ron Weiss and Prof. Skylar Tibbits, whose doors and minds were always open despite the broad and somewhat audacious interdisciplinary scope of this topic. Only because of their patience, trust, and condence was I able to discover a new world of interest in biology. I would then like to acknowledge my colleagues in the MIT Weiss Lab. I especially thank Christian Cuba-Samaniego and Wenlong Xu for their contributions to the project SBIML, as well as to my growth as a synthetic biologist. I owe much of my progress and excitement in the eld to their inspiration and mentorship, and my sanity to their counseling during challenging times. I am grateful to the United States Department of Defense Advanced Research Projects Agency (DARPA)as well as MIT Department of Architecture for providing funding during my graduate education at MIT. I also acknowledge with gratitude the Department of Biological Engineering Administrators Olga Parkin and Darlene Ray for their persistence and help. I thank my sister, Aubry White, for her friendship and for adding four members to my family and support network, including Eli, Callen, and Isabel White, during my tenure at MIT. I must also thank my girlfriend, Jessica Jiang, whose unrelenting patience and companionship has been the foundation of my success. Finally, I must express my very profound gratitude to my parents Bob and Jody Moorman for providing me with unconditional support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you. Author Andrew Moorman 5 6 Contents 1 Introduction 15 1.1 What is Design in Synthetic Biology? . 15 1.2 Cells as Computers . 17 1.3 Non-Classical Computation in Dynamical Systems . 19 1.3.1 The Computational Behavior of Dynamical Systems . 19 1.3.2 Molecular Computation: A Simple Example . 21 1.4 Cell Therapies as Computations . 24 1.5 Neuromorphic Computing in Cell Therapies . 26 2 Molecular Neural Network Computing in Mammalian Cells 29 2.1 A Brief Review of Articial Neural Networks . 29 2.2 On Combinatorial Transcriptional Regulation . 30 2.2.1 A Brief Review of Transcriptional Regulation . 30 2.2.2 A Transcriptional Articial Neuron . 32 2.3 On Bio-Molecular Sequestration . 36 2.3.1 A Brief Review of Molecular Sequestration . 36 2.3.2 A Sequestration-Based Articial Neuron . 37 2.3.3 Sequestration-Based Molecular ANNs to Solve the XOR Problem . 40 2.4 Conclusions . 41 2.5 Methods . 43 2.6 Supplementary Proofs . 44 2.6.1 Sequestration-Based Biomolecular Perceptron Model . 44 2.6.2 Sequestration-Based Biomolecular Neural Network . 47 7 3 Molecular Learning and Adaptation in Mammalian Cells 51 3.1 A Genetic Circuit for Learning in Mammalian Cells . 53 3.1.1 Circuit Design . 53 3.1.2 Discussion . 54 3.1.3 Description of Model . 56 3.2 A Stability Analysis for Population-Scale Learning . 60 3.2.1 Preliminaries . 60 3.2.2 Stability Analysis of a Perceptron Ensemble . 64 4 Conclusion 67 A Tables 71 B Figures 73 8 List of Figures B-1 Personalized medicine increases in therapeutic precision from group level to single- cell ‘living drugs.’ . 74 B-2 Neuromorphic computing ts within the ‘Sense-Compute-Eect’ pipeline for engineered-cell therapies. 75 B-3 A general schematic for the operation of neuromorphic gene circuits in mam- malian cells. 76 B-4 Circuit design schematic for a three-input articial neuron computation using transcriptional regulation. 77 B-5 A general functional schematic for the computation performed by a hybrid pro- moter. 78 B-6 Steady state uorescence measurements for a three-input hybrid promoter cir- cuit with the corresponding log-scale reference computation. 79 B-7 Reaction diagrams for molecular binding and sequestration. 80 B-8 Pictorial depiction of molecular
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