A Series of Virtual Seminars Highlighting the Advances in the Field of Biocomputing


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Can we integrate biology with modern computers?

Learn about DNA Storage, Molecular Machines, Molecular logic gate, DNA-Nanotechnology, DNA Assembly, Chemical Computing, and more ... .

March 29, Ultrasensitive components enable adaptation in molecular feedback systems Professor Elsa Franco University of California, Los Angeles ( Friday at 2 PM PST/ 5 PM EST)

Talk Abstract

  • Biological organisms regulate many of their properties to fall in a prescribed range, for example temperature, osmotic pressure, and glucose levels. The capacity to preserve a desired condition is enabled by feedback loops that adjust gene expression or metabolism in response to changes or perturbations in the environment. Theory developed in automation engineering indicates that the best way to reject perturbations in a feedback system is to include components that integrate (maintain memory) of past effects of the disturbances, and are known as integrators. While models of biological networks such as osmoregulation and chemotaxis are known to include integral feedback, a different question is how to build molecular integral control systems from the bottom up. With mathematical modeling I will describe how ultrasensitive components within feedback loops can help maintain a desired gene expression level. I will also discuss a particular ultrasensitive reaction network that combines molecular sequestration and an activation/deactivation cycle, and could be used not only for maintaining a steady state but also for setting a tunable reference. I will finally provide an overview of ongoing projects in our group focused on the role of ultrasensitivity in the context of molecular computation and non-equilibrium kinetics.
  • About Professor Franco

  • Professor Elsa Franco is an associate professor in Mechanical & Aerospace Engineering and Bioengieering at UCLA; between 2011-2018 professor Franco worked at UC Riverside as an Assistant Professor. She received a Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2011, and she also holds a Ph.D. in Automation and a Laurea degree in Power Systems Engineering from the University of Trieste, Italy. The research interests of her group are in the areas of biological feedback and DNA/RNA nanotechnology, with focus on design, modeling, and synthesis of circuits and responsive materials using nucleic acids and proteins. Her work is currently funded by NSF, DOE, and the Broad Stem Cell Research Center at UCLA. She is an associate editor with IEEE Control Systems Letters, an elected member of the IEEE Control Systems Society Board of Governors and a council member of the Engineering Biology Research Center (EBRC).
    • This talk will be host at 2 PM PST / 5 PM EST via zoom

March 12, Crystals that think about how they’re growing Professor David Doty University of California, Davis ( Friday at 2 PM PST/ 5 PM EST)

Talk Abstract

  • Biology offers inspiring examples of molecules that can store and process information to construct and control the sophisticated nanoscale devices that regulate the machinery of life. Yet biology offers few effective design principles for manufacturing such molecules ourselves. Much of synthetic biology relies on "alien technology": evolved proteins that, had evolution not furnished them, we would not know how to design ourselves. DNA nanotechnology offers a different approach, enabling design of smart molecular systems from first principles. Theory that combines mathematical tiling and statistical-mechanical models of crystallization has shown that algorithmic behavior can be embedded within molecular self-assembly processes. Previous results had experimentally demonstrated algorithmic "tile" self-assembly with up to 22 tile types, creating algorithmically generated patterns such as Sierpinski triangles and binary counters. Despite that success, many information technologies exhibit a complexity threshold -- such as the minimum transistor count needed for a general-purpose computer -- beyond which there is a qualitative increase in the power of a reprogrammable system, and it has not been clear whether the biophysics of DNA self-assembly would allow that threshold to be exceeded. Here we report the design and experimental validation of a DNA tile set containing 355 single-stranded tiles, reprogrammable by tile selection to implement a wide variety of 6-bit algorithms, including copying, sorting, recognizing palindromes and multiples of 3, random walking, obtaining an unbiased choice from a biased random source, electing a leader, simulating Turing-universal cellular automata, generating deterministic and randomized patterns, and serving as a period 63 counter. The system is quite reliable: averaged across the 21 implemented circuits, the per-tile error rate is less than 1 in 3000.
  • References

  • Woods, D., Doty, D., Myhrvold, C., Hui, J., Zhou, F., Yin, P., & Winfree, E. (2019). Diverse and robust molecular algorithms using reprogrammable DNA self-assembly. Nature, 567(7748), 366-372.
  • About Professor Doty

  • David Doty is an assistant professor of Computer Science at the University of California, Davis. He is broadly interested in problems at the intersection of physics, chemistry, biology, and computation. This does not mean the traditional "computation in service of natural science" (e.g., bioinformatics, computational chemistry, or molecular dynamics simulation). Rather, certain molecular systems — such as a test tube of reacting chemicals, a genetic regulatory network, or a growing crystal — can be interpreted as doing computation themselves... natural science in service of computation. He seeks to understand the fundamental logical and physical limits to computation by such means.
    • This talk will be host at 2 PM PST / 5 PM EST via zoom

October 9th, 2020 The Blind Cartographer: 'Imaging' with a DNA swarm Dr, Nikhil Gopalkrishnan Wyss Institute ( Friday at 2 PM PST/ 5 PM EST)

Talk Abstract

  • Techniques that can both spatially map out molecular features and discriminate many targets would be highly valued for their utility in studying fundamental nanoscale processes. Despite decades of development, no current technique can achieve both nanoscale resolution and discriminate hundreds of targets. Here, we report the development of a novel bottom-up technology that: (a) labels a sample with DNA barcodes, (b) measures pairwise-distances between labeled sites and writes them into DNA molecules, (c) reads the pairwise-distances by sequencing and (d) robustly integrates this noisy information to reveal the geometry of the underlying sample. We demonstrate our technology on DNA origami, which are complex synthetic nanostructures. We both spatially localized and uniquely identified over a hundred densely packed elements, some spaced just 6 nm apart, with an average spatial localization accuracy (RMS deviation) of ~2 nm. The bottom-up, sequencing-enabled mechanism of this 'DNA nanoscope' is fundamentally different from top-down imaging, and hence offers unique advantages in precision, throughput and accessibility.
  • About Dr. Gopalkrishnan

  • Ph.D., Computer Science and Engineering, 2012; B.Tech, Computer Science and Engineering, 2006
    • This talk will be host at 2 PM PST / 5 PM EST via zoom

September 4th, 2020 Modeling and control for molecular self-assembly and the construction of genetic circuits Professor, Xun Tang Louisiana State University ( Friday at 2 PM PST/ 5 PM EST)

Talk Abstract

  • Control theory studies the design strategies to deliver desired performance of the process of interest. Applications of control theory has benefited a wide range of fields from molecular self-assembly to gene expression regulation. In this talk, I will focus on a colloidal self-assembly system and simple genetic circuits to discuss the application of mathematical modeling, including both data-driven and first principle-based approaches and control theory in tackling the challenge of molecular self-assembly and the design of genetic circuits. Specifically, I will demonstrate the use of a generalizable optimal control framework that involves dimensionality reduction, data-driven modeling, and dynamic programming, in the control of a high dimensional stochastic colloidal self-assembly process, and how this approach could be applied to control genetic molecular assembly process. I will also talk about how first principle-based modeling could help design and construct predictable genetic circuits, using a small-RNA and CRISPRi combined pulse generator as an example.
  • About Professor Tang

  • Dr. Xun Tang obtained his Ph.D. in Chemical Engineering from Georgia Tech in 2016, with his thesis work focused on optimal control for colloidal self-assembly. He then worked in the Franco’s lab (UCLA) as a postdoc working on control theory-based genetic circuits. After that he spent several months at the Salis lab at Penn State working on RNA degradation simulation, before joining Ford as a research engineer. Starting on August 15th, 2020, Dr. Xun Tang will join the Cain Cain Department of Chemical Engineering at Louisiana State University as a tenure track Assistant Professor, and his future research will focus on machine learning, optimal control, molecular self-assembly, and synthetic biology.
    • This talk will be host at 2 PM PST / 5 PM EST via zoom

August 28, 2020 Organic Ionic Memories for Neuromorphic Computing Dr. Armantas Melianas Stanford University ( Friday at 2 PM PST/ 5 PM EST)

Talk Abstract

  • Novel materials and device architectures are needed to address the inability of CMOS transistor scaling to meet the increasingly demanding computational density and energy efficiency requirements of artificial intelligence. To address these challenges, we have recently introduced organic ionic memories that are accurately programmable by ion fluxes into an organic semiconductor channel, similar to the synaptic cleft in the brain. This novel device concept is expected to unlock capabilities unattainable by conventional circuits, non-volatile memories and CMOS technology.
    In this talk, I will explain neuromorphic device requirements using illustrative examples and how we designed [1] our memories to meet these needs, such as write linearity, high speed, energy efficiency, bit precision, endurance, and size. Using a proof-of-concept 3x3 array, we have demonstrated parallel array programming [2], which enables significantly faster and more energy efficient artificial neural network (ANN) accelerators than previously possible using conventional memories. Furthermore, I will show the first steps in coupling organic ionic memories with live cells [3], paving the way towards combining artificial neuromorphic systems with biological neural networks.
  • About Dr. Melianas

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  • Armantas is a Wallenberg Foundation postdoctoral fellow at Stanford University, developing novel semiconductor memories for neuromorphic computing applications in the lab of Prof. Alberto Salleo. He is also interested in developing better biosensors and bioelectronics devices using soft materials such as organic semiconductors and holds a PhD degree in Applied Physics for research on thin-film solar cells from the lab of Prof. Olle Ingänas.
  • References

  • 1. Melianas, A. et al. Temperature-resilient solid-state organic artificial synapses for neuromorphic computing. Science Advances 6, eabb2958 (2020).
  • 2. Fuller, E. J. et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 570–574 (2019).
  • 3. Keene, S. T. et al. A biohybrid synapse with neurotransmitter-mediated plasticity. Nature Materials (2020). doi:10.1038/s41563-020-0703-y
    • This talk will be host at 2 PM PST / 5 PM EST via zoom

August 14, 2020 DNA Punch-Cards: Implementations and Coding-Theoretic Questions Professor Olgica Milenkovic University of Illinois at Urbana-Champaign ( Friday at 2 PM PST/ 5 PM EST)

Talk Abstract

  • Recent implementations of synthetic DNA-based data storage systems have demonstrated several promising applications of macromolecular recorders. However, the proposed systems suffer from high cost, read-write latency and error-rates that render them non-competitive with traditional silicon-based devices. One means to avoid synthesizing DNA is to use readily available, naturally occurring DNA. As the nucleotide sequences of native DNA are fixed, they cannot be edited to accommodate arbitrary user-defined content. Hence, instead of changing the sequence content, one may adopt an alternative recording strategy -- akin to card punching -- that modifies the topology of native DNA to encode desired information. We describe the first macromolecular storage paradigm in which data is written in the form of “nicks” at predetermined positions on the sugar-phosphate backbone of double–stranded native DNA. The platform accommodates parallel nicking on one and multiple genomic DNAfragments, and paired nicking and disassociation for creating “toehold” regions that enable single-bit random access and strand displacement. It also provides a large mass of inexpensive DNA that may be used for multiple, error-free readout cycles via current sequencing technologies. As a proof of concept, we used the multiple-turnover programmable artificial restriction enzymes to punch both text and image files into the PCR products of Escherichia coli genomic DNA fragments in vitro. The encoded data was reliably reconstructed through sequence alignment and read coverage analysis. The described storage implementation is accompanied by a number of new coding-theoretic questions and constructions connecting combinatorial designs and set discrepancy theory. This is a joint work with S Kasra Tabatabaei, Boya Wang, Nagendra Bala Murali Athreya, Behnam Enghiad, Alvaro Gonzalo Hernandez, Jean-Pierre Leburton, David Soloveichik and Huimin Zhao.
  • About Professor Milenkovic

  • Olgica Milenkovic is a professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign (UIUC), and Research Professor at the Coordinated Science Laboratory. She obtained her Masters Degree in Mathematics in 2001 and PhD in Electrical Engineering in 2002, both from the University of Michigan, Ann Arbor. Prof. Milenkovic heads a group focused on addressing unique interdisciplinary research challenges spanning the areas of algorithm design and computing, bioinformatics, coding theory, machine learning and signal processing. Her scholarly contributions have been recognized by multiple awards, including the NSF Faculty Early Career Development (CAREER) Award, the DARPA Young Faculty Award, the Dean’s Excellence in Research Award, and several best paper awards. In 2013, she was elected a UIUC Center for Advanced Study Associate and Willett Scholar while in 2015 she was elected Distinguished Lecturer of the Information Theory Society. In 2018 she became an IEEE Fellow. She has served as Associate Editor of the IEEE Transactions of Communications, the IEEE Transactions on Signal Processing, the IEEE Transactions on Information Theory and the IEEE Transactions on Molecular, Biological and Multi-Scale Communications. In 2009, she was the Guest Editor in Chief of a special issue of the IEEE Transactions on Information Theory on Molecular Biology and Neuroscience.
    • This talk will be host at 2 PM PST / 5 PM EST via zoom

August 7, 2020 Roundtable Discussion with Professor Steven Benner on Future of Biocomputing Professor Steven Benner Harvard University ( Friday at 2 PM PST/ 5 PM EST)

Talk Abstract

  • The concept of using biomolecules like DNA as information storage platforms comes primarily from people who know very little about DNA and other biomolecules as actual chemicals. Therefore, they do not know that DNA is not particularly stable, which contradicts the "stability" often cited in the literature as a reason for why DNA should be used for information storage. Those not familiar with DNA as a molecular substance seem to believe that "A pairs with T and G pairs with C", are absolute rules that can be easily used to write and retrieve information. They have not grasped the concept of diffusion as a slow factor in molecular interactions in solution, the possibility of off-target interactions, the concept of solubility, or other details that make chemistry an "unpredictable art". Accordingly, tens of millions of dollars in public and private money is being wasted to re-learn these fundamental features of how real molecular species behave. All this comes as a surprise to those whose experience in computation is based on immobilized semiconductor logic elements with well-defined connections that inter-communicate at the speed of light. This Roundtable will discuss biocomputing from the perspective of actual chemistry and discuss how public and private money should be redirected so that is not wasted relearning basic chemistry.
  • About Professor Benner

  • Steven Albert Benner has been a professor at Harvard University, ETH Zurich, and the University of Florida where he was the V.T. & Louise Jackson Distinguished Professor of Chemistry. In 2005, he founded The Westheimer Institute of Science and Technology (TWIST) and the Foundation For Applied Molecular Evolution. Benner has also founded the companies EraGen Biosciences and Firebird BioMolecular Sciences LLC. Benner and his colleagues were the first to synthesize a gene, beginning the field of synthetic biology. He was instrumental in establishing the field of paleogenetics. He is interested in the origin of life and the chemical conditions and processes needed to produce RNA.
    • This talk will be host at 2 PM PST / 5 PM EST via zoom

July 10, 2020 Artificial Life In a Chemical Computer Professor Lee Cronin University of Glasgow ( Friday at 12 PM PST/ 3 PM EST)

Talk Abstract

  • In my laboratory we are interested in creating the conditions that allow an artificial life form to emerge. But how do we know when our chemical system is really on the path to life? Will bottom-up (prebiotic) and top-down (programmed) be intrinsically different types of artificial life forms? I will describe three areas of work in my laboratory: 1) how to measure how alive an artificial life form is; 2) our attempts to emerge a bottom up life form; 3) a top down chemically embodied life form. To achieve the top-down life form we had to build a chemical computer that was able to be digitally programmed, error correcting, and ability to do computations using a chemical-logic-machine. We believe this represents the first example of chemical artificial life.
  • About Professor Cronin

  • Lee Cronin was born in the UK and was fascinated with science and technology from an early age getting his first computer and chemistry set when he was 8 years old. This is when he first started thinking about programming chemistry and looking for inorganic aliens. He went to the University of York where he completed both a degree and PhD in Chemistry and then on to do post docs in Edinburgh and Germany before becoming a lecturer at the Universities of Birmingham, and then Glasgow where he has been since 2002 working up the ranks to become the Regius Professor of Chemistry in 2013 aged 39. He has one of the largest multidisciplinary chemistry-based research teams in the world, having raised over $35 M in grants and current income of $15 M. He has given over 300 international talks and has authored over 350 peer reviewed papers with recent work published in Nature, Science, and PNAS. He and his team are trying to make artificial life forms, find alien life, explore the digitization of chemistry, understand how information can be encoded into chemicals and construct chemical computers.
    • This talk will be host at 12 PM PST / 3 PM EST via zoom

June 26, 2020 Extending DNA Data Storage with Content-Based Similarity Search Callie Bee University of Washington ( Friday at 2 PM PST)

Talk Abstract

  • DNA-based digital storage is emerging as a dense and durable alternative to traditional media. However, existing approaches have lacked the ability to perform complex queries on the stored data, such as content-based search. In this talk, I will describe our technique for executing similarity search on a DNA-based database of 1.6 million images, and present experimental results that show its performance is comparable to state-of-the-art electronic systems. By using machine learning techniques to build a predictor of DNA hybridization reactions, we are able to efficiently optimize an encoding from images to DNA sequences for similarity search. Using our optimized encoding, an encoded query is most likely to hybridize with targets that are encoded from images visually similar to the query, which facilitates similarity-based enrichment of the database. These results bring DNA computing to DNA digital storage, showing a possible path forward for the practicality of DNA digital storage systems.
  • About Callie

  • Callie is a fourth-year PhD candidate at the University of Washington, working with the Molecular Information Systems Lab. In the past, she has worked in a variety of fields, from comparative literature to computer architecture. Her current interests are in machine-driven DNA sequence design and its applications to molecular programming and synthetic biology.
    • This talk will be host at 2 PM PST via zoom

June 19, 2020 Bacterial Controller Aided Wound Healing: A Case Study in Dynamical Population Controller Design Dr. Leopold Green Caltech ( Friday at 2 PM PST)

Talk Abstract

  • Wound healing is a complicated biological process consisting of many types of cellular dynamics and functions regulated by chemical and molecular signals. Recent advances in synthetic biology have made it possible to predictably design and build closed-loop controllers that can function appropriately alongside biological species. In this work develop a simple dynamical population model mimicking the sequential relay-like dynamics of cellular populations involved in the wound healing process. Our model consists of four nodes and five signals whose parameters we can tune to simulate various chronic healing conditions. We also develop a set of regulator functions based on type-1 incoherent feed forward loops (IFFL) that can sense the change from acute healing to incomplete chronic wounds, improving the system in a timely manner. Both the wound healing and type-1 IFFL controller architectures are compatible with available synthetic biology experimental tools for potential applications.
  • About Dr. Green

  • Dr. Green is a post-doctoral researcher at Caltech working on engineering bacteria for potential “smart” bio-sensors and bio-actuators. The applications of this work include medicine, environmental sciences, textiles, and cosmetics, to name a few. Dr.Green promotes scientific exploration for underrepresented minorities, financial literacy, and mental & physical health.
    • This talk will be host at 2 PM PST via zoom

June 12, 2020 Anatomical Computation by Somatic Bioelectrical Network Professor Michael Levin Tufts / Wyss Institute ( Friday at 1 PM PST)

Talk Abstract

  • A remarkable fact about living bodies is that all cells communicate during embryogenesis and regeneration to enable them to work together toward the construction and repair of complex anatomical structures. My lab has uncovered a powerful new component of that communication: endogenous bioelectrical signaling among all cells (not just neurons) that enables the computations required to make decisions about large-scale growth and form. We have developed novel techniques to re-write the pattern memories that control gene expression and morphogenesis, with numerous applications for birth defects, regeneration of injured organs, cancer reprogramming, and synthetic bioengineering of novel living machines. In this talk, I will describe the emerging science at the intersection of developmental biophysics, basal cognition, and regenerative medicine, and its implications for understanding computation and engineering novel AIs. The development of new tools, together with conceptual advances that link computer science, cognitive science, and molecular genetics, are revealing exciting new vistas for many fields, from bioengineering and synthetic biology to robotics and machine learning.
  • About Professor Levin

  • Michael Levin, a professor in the Biology department at Tufts, holds the Vannevar Bush endowed Chair and serves as director of the Tufts Center for Regenerative and Developmental Biology. Recent honors include the Scientist of Vision award and the Distinguished Scholar Award. His group’s focus is on understanding the biophysical mechanisms that implement decision-making during complex pattern regulation, and harnessing endogenous bioelectric dynamics toward rational control of growth and form
    • This talk will be host at 1 PM PST via zoom

June 5, 2020 In Vitro Transcriptional Rgulatory Networks for Autonomous Control of Bioinspired Materials Dr. Samuel Schaffter JUH ( Friday at 2 PM PST)

Talk Abstract

  • Cells use genetic regulatory networks (GRNs) composed of interconnected genes that regulate one another to orchestrate complex behaviors such as differentiation, stress response, and self-healing. Many of the key dynamics identified in cellular GRNs have been recapitulated using synthetic chemistries in vitro, with emerging applications in autonomous control and dynamic regulation of materials. In this context, in vitro GRN analogs could imbue materials with the aforementioned capabilities of living systems. In vitro transcriptional circuits, composed of short synthetic genelets and a few inexpensive enzymes, have emerged as a simple, yet potentially powerful tool for assembling synthetic GRNs. However, only small genelet modules that exhibit a single function have been developed. Given cellular GRNs build complexity by integrating many functional modules together, identifying design rules that allow reliable integration of multiple genelet modules into larger networks is a critical step in developing sophisticated multifunctional GRN analogs. Here we report an updated genelet toolbox that enables the construction of large multifunctional regulatory networks. We develop the toolbox by identifying sources of undesired interactions between network components and designing strategies to mitigate these effects. Using these design principles, we assemble multi-module genelet networks that exhibit key functionalities inspired by cellular decision making and differentiation pathways. These results introduce a new class of mesoscale synthetic networks that can orchestrate increasingly complex regulatory processes by design.
  • About Dr. Schaffter

  • Sam is a bioengineering working in the field of DNA nanotechnology and computing. His research primarily focuses on developing in vitro chemical circuits based on RNA production and degradation that can emulate the dynamics of cellular genetic regulatory networks. His ultimate goal is to develop sophisticated circuits that can autonomously control nucleic acid materials in a manner similar to how the genome controls living systems
    • This talk will be host at 2 PM PST via zoom

May 29, 2020 DNA 'Velcro' to Control Microbial Adhesion, Professor Ariel Furst MIT ( Friday at 2 PM PST)

Talk Abstract

  • Microbial fuel cells offer a promising technology to remove contaminants from wastewater and generate electricity from those contaminants. However, microbe-modified electrodes suffer from issues regarding interfacing the cells with electrodes. Using the inherent properties of DNA, we have successfully overcome conventional limitations with microbial fuel cells.
  • About Professor Furst

  • Ariel L. Furst received a B.S. degree in Chemistry from the University of Chicago working with Prof. Stephen B. H. Kent on the chemical synthesis of proteins. She then completed her Ph.D. in the lab of Prof. Jacqueline K. Barton at the California Institute of Technology developing new cancer diagnostic strategies based on DNA charge transport. She was then an A. O. Beckman Postdoctoral Fellow in the lab of Prof. Matthew Francis at the University of California, Berkeley. She is now an assistant professor in the Chemical Engineering Department at MIT. She is passionate about STEM outreach and increasing participation of underrepresented groups in engineering.
    • This talk will be host at 2 PM PST via zoom

May 15, 2020 Integrated Scientific Modeling and Lab Automation, Professor Luca Cardelli Oxford University (Friday at 2 PM PST)

Talk Abstract

  • The cycle of observation, hypothesis formulation, experimentation, and falsification that has driven scientific and technical progress is lately becoming automated in all its separate components. However, integration between these automated components is lacking. Theories are not placed in the same formal context as the (coded) protocols that are supposed to test them: neither description knows about the other, although they both aim to describe the same process. We develop integrated descriptions from which we can extract both the model of a phenomenon (for possibly automated mathematical analysis), and the steps carried out to test it (for automated execution by lab equipment). This is essential if we want to carry out automated model synthesis, falsification, and inference, by taking into account uncertainties in both the model structure and in the equipment tolerances that may jointly affect the results of experiments.
  • About Professor Cardelli

  • Luca Cardelli has an M.Sc. in computer science from the University of Pisa, and a Ph.D. in computer science from the University of Edinburgh. He worked in the USA at Bell Labs, Murray Hill, from 1982 to 1985, and at Digital Equipment Corporation, Systems Research Center in Palo Alto, from 1985 to 1997, and at Microsoft Research, in Cambridge UK from 1997 to 2018 where he was head of the Programming Principles and Tools and Security groups until 2012. Since 2013 he is a Royal Society Research Professor at the University of Oxford. His main interests are in programming languages and concurrency, and more recently in programmable biology and nanotechnology. He is a Fellow of the Royal Society, a Fellow of the Association for Computing Machinery, an Elected Member of the Academia Europaea, and an Elected Member of the Association Internationale pour les Technologies Objets. His web page is at lucacardelli.name.
    • This talk will be host at 2 PM PST via zoom

May 8, 2020 Self-Assembly of Nanoscale Architectures with DNA, Dr. Grigory Tikhomirov Caltech (Friday at 2 PM PST)

Talk Abstract

  • Nature has evolved to self-assemble complex functional architectures in a sustainable bottom-up way. From bacteria to humans, biological systems arise from a common set of atomically precise nanoscale building blocks such as proteins that give rise to complex functions such as sensing, computation, and actuation. In contrast, most human-made devices are composed of building blocks with much less precision, and are assembled through a top-down process which is highly inflexible and unsustainable. Drawbacks aside, these devices are highly useful and can often surpass their biological counterparts (e.g., computers playing chess). This success is largely due to a systematic and modular engineering approach where simple but well-understood components such as transistors are put together in a programmable way. Is it possible to develop a new approach to building complex devices that combines the strengths of biomolecular self-assembly and systematic engineering? In this talk I will discuss recent work towards this goal using DNA as a nanoscale, programmable building block [1-5]. However, despite being the most programmable molecule for information processing, DNA lacks the basic physical attributes required for building high performance electronic devices. I will discuss ongoing work towards a new type of nanoscale building blocks in which DNA can be flexibly replaced with other materials such as metals and semiconductors. These nanoscale modules can be designed to self-assemble into a variety of plasmonic, photonic, and electronic architectures unattainable with any current nanofabrication techniques. This novel approach integrates the advantages of natural bottom-up assembly and engineered top-down programming and may lead to a host of new intelligent devices for technology and medicine.
  • References

  • 1. G. Tikhomirov, S. Hoogland, P. Lee, A. Fisher, E.H. Sargent, S.O. Kelley “DNA-Based Programming of Quantum Dot Valency, Self Assembly, and Luminescence” Nature Nanotechnology, 2011, 485-490
  • 2. G. Tikhomirov, P. Petersen, L. Qian “Fractal assembly of micrometre-scale DNA origami arrays with arbitrary patterns” Nature, 2017, 67-71
  • 3. G. Tikhomirov, P. Petersen, L. Qian “Programmable disorder in random DNA tilings” Nature Nanotechnology, 2017, 251-259
  • 4. P. Petersen, G. Tikhomirov, L. Qian. “Information-based autonomous reconfiguration in systems of interacting DNA nanostructures” Nature Communications, 2018, 5362
  • 5. G. Tikhomirov, P. Petersen, L. Qian “Triangular DNA origami tilings” JACS, 2018, 17361
  • About Greg

  • Greg is a postdoc in bioengineering at Caltech. He has a longstanding dream to build systems approaching the complexity of life, motivated by the realization that incomprehensible natural complexity arises from comprehensible fundamental laws. Greg is interested both in understanding the principles required to build such systems as well as in building practical devices using these principles.
    • This talk will be host at 2 PM PST via zoom

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