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DNA Computers for Work and Play
INFORMATION SCIENCE COMPUTERS DNA FOR WORK AND PL AY TIC-TAC-TOE-PLAYING COMPUTER consisting of DNA strands in solution demonstrates the potential of molecular logic gates. © 2008 SCIENTIFIC AMERICAN, INC. Logic gates made of DNA could one day operate in your bloodstream, collectively making medical decisions and taking action. For now, they play a mean game of in vitro tic-tac-toe By Joanne Macdonald, Darko Stefanovic and Milan N. Stojanovic rom a modern chemist’s perspective, the mentary school in Belgrade, Serbia, we hap- structure of DNA in our genes is rather pened to be having dinner, and, encouraged by Fmundane. The molecule has a well-known some wine, we considered several topics, includ- importance for life, but chemists often see only ing bioinformatics and various existing ways of a uniform double helix with almost no function- using DNA to perform computations. We decid- al behavior on its own. It may come as a surprise, ed to develop a new method to employ molecules then, to learn that this molecule is the basis of a to compute and make decisions on their own. truly rich and strange research area that bridges We planned to borrow an approach from synthetic chemistry, enzymology, structural electrical engineering and create a set of molec- nanotechnology and computer science. ular modules, or primitives, that would perform Using this new science, we have constructed elementary computing operations. In electrical molecular versions of logic gates that can oper- engineering the computing primitives are called ate in water solution. Our goal in building these logic gates, with intuitive names such as AND, DNA-based computing modules is to develop OR and NOT. -
Biological Computation
Portland State University PDXScholar Computer Science Faculty Publications and Presentations Computer Science 9-21-2010 Biological Computation Melanie Mitchell Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/compsci_fac Part of the Biology Commons, and the Computer Engineering Commons Let us know how access to this document benefits ou.y Citation Details Mitchell, Melanie, "Biological Computation" (2010). Computer Science Faculty Publications and Presentations. 2. https://pdxscholar.library.pdx.edu/compsci_fac/2 This Working Paper is brought to you for free and open access. It has been accepted for inclusion in Computer Science Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Biological Computation Melanie Mitchell SFI WORKING PAPER: 2010-09-021 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited visit to or collaboration at SFI, or funded by an SFI grant. ©NOTICE: This working paper is included by permission of the contributing author(s) as a means to ensure timely distribution of the scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the author(s). It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. -
Scaling up Genetic Circuit Design for Cellular Computing
Edinburgh Research Explorer Scaling up genetic circuit design for cellular computing Citation for published version: Xiang, Y, Dalchau, N & Wang, B 2018, 'Scaling up genetic circuit design for cellular computing: advances and prospects', Natural Computing, vol. 17, no. 4, pp. 833-853. https://doi.org/10.1007/s11047-018-9715-9 Digital Object Identifier (DOI): 10.1007/s11047-018-9715-9 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Natural Computing Publisher Rights Statement: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creative commons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and -
Natural Computing Conclusion
Introduction Natural Computing Conclusion Natural Computing Dr Giuditta Franco Department of Computer Science, University of Verona, Italy [email protected] For the logistics of the course, please see the syllabus Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion Natural Computing Natural (complex) systems work as (biological) information elaboration systems, by sophisticated mechanisms of goal-oriented control, coordination, organization. Computational analysis (mat modeling) of life is important as observing birds for designing flying objects. "Informatics studies information and computation in natural and artificial systems” (School of Informatics, Univ. of Edinburgh) Computational processes observed in and inspired by nature. Ex. self-assembly, AIS, DNA computing Ex. Internet of things, logics, programming, artificial automata are not natural computing. Dr Giuditta Franco Slides Natural Computing 2017 Introduction Natural Computing Natural Computing Conclusion Bioinformatics versus Infobiotics Applying statistics, performing tools, high technology, large databases, to analyze bio-logical/medical data, solve problems, formalize/frame natural phenomena. Ex. search algorithms to recover genomic/proteomic data, to process them, catalogue and make them publically accessible, to infer models to simulate their dynamics. Identifying informational mechanisms underlying living systems, how they assembled and work, how biological information may be defined, processed, decoded. New -
Alternative Computational Models: a Comparison of Biomolecular and Quantum Computation
Alternative Computational Models: A Comparison of Biomolecular and Quantum Computation John H. Reif Department of Computer Science Duke University ∗ Abstract Molecular Computation (MC) is massively parallel computation where data is stored and processed within objects of molecular size. Biomolecular Computation (BMC) is MC using biotechnology techniques, e.g. recombinant DNA operations. In contrast, Quantum Computation (QC) is a type of computation where unitary and measurement operations are executed on linear superpositions of basis states. Both BMC and QC may be executed at the micromolecular scale by a variety of methodologies and technologies. This paper surveys various methods for doing BMC and QC and discusses the considerable theoretical and practical advances in BMC and QC made in the last few years. We compare bounds on key resource such as time, volume (number of molecules times molecular density), energy and error rates achievable, taking particular note of the scalability of these methods with the size of the problem solved. In addition to NP search problems and database search problems, we enumerate a wide variety of further potential practical applications of BMC and QC. We observe that certain problems (e.g., NP search problems), if solved with polynomial time bounds, requires exponentially large volume for BMC, so BMC does not scale well to solve very large NP search problems. However, we describe a number of applications (e.g., search within large data bases and simu- lation of parallel machines) where the volume grows polynomial. Also, we note that the observation operation of QC, which is a fundamental operation of QC used for obtaining classical output, may potentially suffer from exponentially large volume requirements. -
Solving Travelling Salesman Problem in a Simulation of Genetic Algorithms with Dna
International Journal "Information Theories & Applications" Vol.15 / 2008 357 SOLVING TRAVELLING SALESMAN PROBLEM IN A SIMULATION OF GENETIC ALGORITHMS WITH DNA Angel Goñi Moreno Abstract: In this paper it is explained how to solve a fully connected N-City travelling salesman problem (TSP) using a genetic algorithm. A crossover operator to use in the simulation of a genetic algorithm (GA) with DNA is presented. The aim of the paper is to follow the path of creating a new computational model based on DNA molecules and genetic operations. This paper solves the problem of exponentially size algorithms in DNA computing by using biological methods and techniques. After individual encoding and fitness evaluation, a protocol of the next step in a GA, crossover, is needed. This paper also shows how to make the GA faster via different populations of possible solutions. Keywords: DNA Computing, Evolutionary Computing, Genetic Algorithms. ACM Classification Keywords: I.6. Simulation and Modeling, B.7.1 Advanced Technologies, J.3 Biology and Genetics Introduction In a short period of time DNA based computations have shown lots of advantages compared with electronic computers. DNA computers can solve combinatorial problems that an electronic computer cannot like the well known class of NP complete problems. That is due to the fact that DNA computers are massively parallel [Adleman, 1994]. However, the biggest disadvantage is that until now molecular computation has been used with exact and “brute force” algorithms. It is necessary for DNA computation to expand its algorithmic techniques to incorporate aproximative and probabilistic algorithms and heuristics so the resolution of large instances of NP complete problems will be possible. -
The Bio Revolution: Innovations Transforming and Our Societies, Economies, Lives
The Bio Revolution: Innovations transforming economies, societies, and our lives economies, societies, our and transforming Innovations Revolution: Bio The The Bio Revolution Innovations transforming economies, societies, and our lives May 2020 McKinsey Global Institute Since its founding in 1990, the McKinsey Global Institute (MGI) has sought to develop a deeper understanding of the evolving global economy. As the business and economics research arm of McKinsey & Company, MGI aims to help leaders in the commercial, public, and social sectors understand trends and forces shaping the global economy. MGI research combines the disciplines of economics and management, employing the analytical tools of economics with the insights of business leaders. Our “micro-to-macro” methodology examines microeconomic industry trends to better understand the broad macroeconomic forces affecting business strategy and public policy. MGI’s in-depth reports have covered more than 20 countries and 30 industries. Current research focuses on six themes: productivity and growth, natural resources, labor markets, the evolution of global financial markets, the economic impact of technology and innovation, and urbanization. Recent reports have assessed the digital economy, the impact of AI and automation on employment, physical climate risk, income inequal ity, the productivity puzzle, the economic benefits of tackling gender inequality, a new era of global competition, Chinese innovation, and digital and financial globalization. MGI is led by three McKinsey & Company senior partners: co-chairs James Manyika and Sven Smit, and director Jonathan Woetzel. Michael Chui, Susan Lund, Anu Madgavkar, Jan Mischke, Sree Ramaswamy, Jaana Remes, Jeongmin Seong, and Tilman Tacke are MGI partners, and Mekala Krishnan is an MGI senior fellow. -
Molecular and Neural Computation
CSE590b: Molecular and neural computation Georg Seelig 1 Administrative Georg Seelig Grading: Office: CSE 228 30% class participation: ask questions. It’s more [email protected] fun if it’s interactive. TA: Kevin Oishi 30% Homework: Due at the end of class one week after they are handed out. Late policy: -10% each day for the first 3 days, then not accepted 40% Class project: A small design project using one (or several) of the design and simulation tools that will be introduced in class [email protected] Books: There is no single book that covers the material in this class. Any book on molecular biology and on neural computation might be helpful to dig deeper. https://www.coursera.org/course/compneuro 2 Computation can be embedded in many substrates Computaon controls physical Alternave physical substrates substrates (output of computaon is can be used to make computers the physical substrate) The molecular programming project The history of computing has taught us two things: first, that the principles of computing can be embodied in a wide variety of physical substrates from gears and springs to transistors, and second that the mastery of a new physical substrate for computing has the potential to transform technology. Another revolution is just beginning, one that will result in new types of programmable systems based on molecules. Like the previous revolutions, this “molecular programming revolution” will take much of its theory from computer science, but will require reformulation of familiar concepts such as programming languages and compilers, data structures and algorithms, resources and complexity, concurrency and stochasticity, correctness and robustness. -
The Many Facets of Natural Computing
The Many Facets of Natural Computing Lila Kari0 Grzegorz Rozenberg Department of Computer Science Leiden Inst. of Advanced Computer Science University of Western Ontario Leiden University, Niels Bohrweg 1 London, ON, N6A 5B7, Canada 2333 CA Leiden, The Netherlands [email protected] Department of Computer Science University of Colorado at Boulder Boulder, CO 80309, USA [email protected] “Biology and computer science - life and computation - are various natural phenomena. Thus, rather than being over- related. I am confident that at their interface great discoveries whelmed by particulars, it is our hope that the reader will await those who seek them.” (L.Adleman, [3]) see this article as simply a window onto the profound rela- tionship that exists between nature and computation. There is information processing in nature, and the natu- 1. FOREWORD ral sciences are already adapting by incorporating tools and Natural computing is the field of research that investi- concepts from computer science at a rapid pace. Conversely, gates models and computational techniques inspired by na- a closer look at nature from the point of view of information ture and, dually, attempts to understand the world around processing can and will change what we mean by computa- us in terms of information processing. It is a highly in- tion. Our invitation to you, fellow computer scientists, is to terdisciplinary field that connects the natural sciences with take part in the uncovering of this wondrous connection.1 computing science, both at the level of information tech- nology and at the level of fundamental research, [98]. As a matter of fact, natural computing areas and topics come in many flavours, including pure theoretical research, algo- 2. -
The Nanobank Database Is Available at for Free Use for Research Purposes
Forthcoming: Annals of Economics and Statistics (Annales d’Economie et Statistique), Issue 115/116, in press 2014 NBER WORKING PAPER SERIES COMMUNITYWIDE DATABASE DESIGNS FOR TRACKING INNOVATION IMPACT: COMETS, STARS AND NANOBANK Lynne G. Zucker Michael R. Darby Jason Fong Working Paper No. 17404 http://www.nber.org/papers/w17404 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2011 Revised March 2014 The construction of Nanobank was supported under major grants from the National Science Foundation (SES- 0304727 and SES-0531146) and the University of California’s Industry-University Cooperative Research Program (PP9902, P00-04, P01-02, and P03-01). Additional support was received from the California NanoSystems Institute, Sun Microsystems, Inc., UCLA’s International Institute, and from the UCLA Anderson School’s Center for International Business Education and Research (CIBER) and the Harold Price Center for Entrepreneurial Studies. The COMETS database (also known as the Science and Technology Agents of Revolution or STARS database) is being constructed for public research use under major grants from the Ewing Marion Kauffman Foundation (2008- 0028 and 2008-0031) and the Science of Science and Innovation Policy (SciSIP) Program at the National Science Foundation (grants SES-0830983 and SES-1158907) with support from other agencies. Our colleague Jonathan Furner of the UCLA Department of Information Studies played a leading role in developing the methodology for selecting records for Nanobank. We are indebted to our scientific and policy advisors Roy Doumani, James R. Heath, Evelyn Hu, Carlo Montemagno, Roger Noll, and Fraser Stoddart, and to our research team, especially Amarita Natt, Hsing-Hau Chen, Robert Liu, Hongyan Ma, Emre Uyar, and Stephanie Hwang Der. -
The Biological Microprocessor, Or How to Build a Computer with Biological Parts
Volume No: 7, Issue: 8, April 2013, e201304003, http://dx.doi.org/10.5936/csbj.201304003 CSBJ The biological microprocessor, or how to build a computer with biological parts Gerd HG Moe-Behrens a,* Abstract: Systemics, a revolutionary paradigm shift in scientific thinking, with applications in systems biology, and synthetic biology, have led to the idea of using silicon computers and their engineering principles as a blueprint for the engineering of a similar machine made from biological parts. Here we describe these building blocks and how they can be assembled to a general purpose computer system, a biological microprocessor. Such a system consists of biological parts building an input / output device, an arithmetic logic unit, a control unit, memory, and wires (busses) to interconnect these components. A biocomputer can be used to monitor and control a biological system. Introduction Nature and computers are words that used to mean unrelated Other important contributions to systemics are by the Nobel-prize things. However, this view changed, starting in the 1940s, when a winning work of Ilya Prigogine on self-organization and his systems revolutionary scientific paradigm, systemics based on platonic theory concepts in thermodynamics [10]. Furthermore: Mitchell idealistic philosophy, gained popularity [1] [2] [3]. Feigenbaums work on Chaos theory [11]. Contemporary application The roots of philosophical idealism based systemics goes back to finds systems theory in bioscience in fields such as systems biology, Plato. A centerpiece of Plato’s (428/7 to 348/7 BC) work is his and its practical application synthetic biology [12]. The term systems theory of forms, also called theory of ideas [2]. -
Moving from Observation to Predictive Design Corey J. Wilson
Biomolecular Assemblies: Moving from Observation to Predictive Design Corey J. Wilson,† Andreas S. Bommarius,† Julie A. Champion,† Yury O. Chernoff,‡,≡ David G. Lynn,‖ Anant K. Paravastu,† Chen Liang,‖ Ming-Chien Hsieh,‖ Jennifer M. Heemstra‖,* †School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States ‡School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States ≡Laboratory of Amyloid Biology & Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia ‖Department of Chemistry, Emory University, Atlanta, Georgia 30322, United States *corresponding author, [email protected] ABSTRACT: Biomolecular assembly is a key driving force in nearly all life processes, providing structure, information storage, and communication within cells and at the whole organism level. These assembly processes rely on precise interactions between functional groups on nucleic acids, proteins, carbohydrates, and small molecules, and can be fine tuned to span a range of time, length, and complexity scales. Recognizing the power of these motifs, researchers have sought to emulate and engineer biomolecular assemblies in the laboratory, with goals ranging from modulating cellular function to the creation of new polymeric materials. In most cases, engineering efforts are inspired or informed by understanding the structure and properties of naturally occurring assemblies, which has in turn fueled the development of predictive