Ginkgo Bioworks, Inc.’s Post

Stats at the basis of Science. Statistics are crucial in science as they provide the tools to analyze complex data, draw meaningful conclusions, and validate experimental results. In science, and particularly in biology, where variability is inherent in living systems, statistical methods help distinguish between random variations and significant patterns. Stats enable researchers to design experiments, interpret outcomes with confidence, and make informed decisions. Without statistics, biological research would lack precision and reliability, but more important, it would move forward slower.

From 2018 to 2021, my research group collaborated with the group of Michelle Simmons to understand spin resonance of multiple P donor qubits across various STM lithographic devices with the goal of laying out a unified and consistent framework for understanding spin couplings and their Stark effects in silicon. Recently, we published two of those works, thanks to the intense experiment and theory collaboration among the various members of each group back then. Kudos to Michael Jones and Ludwik Kranz for persevering and driving these papers home. Thanks to the theory team members, Serajum Monir, Edyta Osika, Yuling Hsueh, and Angus Worrall for doing many heroic calculations, and to the many members of the experimental team including Pascal Macha, Felix Krauth, Sam Gorman, Joris Keizer, Michelle Simmons. Paper 1: Atomic Engineering of Molecular Qubits for High-Speed, High-Fidelity Single Qubit Gates https://lnkd.in/guJKhbkm Paper 2: Exploiting Atomic Control to Show When Atoms Become Molecules https://lnkd.in/gS4-YWqQ

Paper allert! Exciting developments in #particle technology! As part of the German Science Foundation's #DFG priority program SPP 2045, we're making significant steps in understanding multidimensional particle properties. Our research is dedicated to describe each particle with multiple attributes, such as size and shape. The attached paper offers practical insights into the behavior of two-dimensionally distributed particle systems during separation processes. We begin with a robust description of the two- or multi-dimensional particle property distribution, laying the foundation for a comprehensive understanding. Exploring two- and multidimensional property distributions provides a detailed perspective on particle processes, paving the way for more precise modeling and advancing #digitalization. See: https://lnkd.in/e_GDn2NT

What can computers tell us about the nature of biology? Cellular automaton is a grid-like structure composed of cells in a grid that follow a given set of rules, typically the ON/OFF function (white or black). The initial condition is set on the first row and flows downwards, each row down is another iteration (n+1) of the system based on the rules of the previous row. Rule 30 has aperiodic and chaotic (chaos theory) dynamics. Demonstrating that simple rules can yield very complex results.

📢NEW PAPER ACCEPTED in Angewandte Chemie ! 📝 Parallel and High Throughput Reaction Monitoring with Computer Vision Abstract: We report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well-plate high throughput settings, are described. Analysis of parallel dye-quenching hydroxylations, DMAP-catalysed esterification, solid-liquid sedimentation dynamics, metal catalyst degradation, and biologically-relevant sugar-mediated nitro reduction reactions have each provided insight into the scope and limitations of camera-enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi-reaction time-resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space. Read the paper: https://lnkd.in/eTDsHaJQ __ #Kineticolor #computervision #highthroughput #reactionmonitoring

Method for observing single molecules in their original states---many implications https://lnkd.in/gBDyTwvQ

👇 Check out this short video by Mark Howard talking about the Arxspan by SciY Notebook, a cloud-based Electronic Laboratory Notebook (ELN) and workflow solution perfect for chemistry and biology data. Learn more: https://lnkd.in/dSF3u6-W #SciY #ELN #ArxspanbySciY

DNA storage holds immense potential to revolutionize the way we store and preserve vast amounts of data. With its remarkable density, durability, and longevity, DNA offers a promising alternative to traditional storage mediums. Beyond its technical advantages, DNA storage presents opportunities for enhanced security, energy efficiency, and environmental sustainability. Moreover, its compatibility with biological systems opens the door to novel applications, including data storage within living organisms and integration with biotechnological processes. Check out our recent paper published in the IEEE Globecom 2023 conference, where we propose and analyze a concatenated coding scheme for DNA storage. https://lnkd.in/eT4UXDxb

Dr. Marcus Noack and Dr. Mark Risser pioneer the Noack-Risser method, advancing Gaussian processes. (https://lnkd.in/epbKKK8A) Learn more: https://lnkd.in/eHMVvBmQ @BerkeleyLab #research #GaussianProcesses

Have you read the study by Franziska Emmerling, Fabrizia Grepioni and colleagues in RSC Mechanochemistry issue 1? Learn all about Deriving kinetic insights from mechanochemically synthesized compounds using multivariate analysis (MCR-ALS) of powder X-ray diffraction data You can read their work for free here: https://lnkd.in/dQjHEmCU