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What do ant colonies have to do with molecular programming? In this podcast, we spoke with Namita Sarraf, a graduate student at Caltech in Lulu Qian’s group. We discuss her research, which revolves around the production of multifunctional and modular DNA robots. Namita takes inspiration from ant colony dynamics to design robots, which alone may exhibit simple behaviour, but show emergent complexity when put together. By having these robots pattern the surface, ant pheromones can be emulated. One task which these “DNA ants” are being made to perform is maze-solving. Because traditional methods are not ideal for DNA robots, Namita is developing bespoke maze-solving algorithms. As she points out however, maze-solving by itself is not inherently useful, and for this reason these DNA robots are being built for modularity and composability. By combining maze-solving with cargo sorting Namita can generate more complex behaviours with real world applications. We then move on to talk about how Namita moved into molecular programming from her original field of tissue engineering. We discuss graduate student life, impostor syndrome, and the generation of negative results and their use in publishing. Namita is also one of the founders of the open collaborative textbook project “The Art of Molecular Programming”, a grassroots project aimed at collecting experts in the field to build a comprehensive textbook which will serve as a starting point for new and existing researchers. We discuss how the idea came about, inspired by the spirit of the Synthetic Biology community. The Art of Molecular Programming aims to be a project which collects all of the useful pieces of lore which exist scattered throughout the molecular programming literature and put them in one useful repository, taking away the pain that new graduate students endure in their first years while they struggle to build up a coherent picture of the field by reading countless ad-hoc papers. --- Find more information at the episode page here: https://podcast.molpi.gs/media/sarraf-n-5f564ce4c08e6e5c/

Kate Adamala is a biochemist building synthetic cells. Her research aims at understanding chemical principles of biology, using artificial cells to create new tools for bioengineering, drug development, and basic research. The interests of her lab span questions from the origin and earliest evolution of life, using synthetic biology to colonize space, to the future of biotechnology and medicine. She received a MSc in chemistry from the University of Warsaw, Poland, studying synthetic organic chemistry. In grad school, she worked with professor Pier Luigi Luisi from University Roma Tre and Jack Szostak from Harvard University. She studied RNA biophysics, small peptide catalysis and liposome dynamics, in an effort to build a chemical system capable of Darwinian evolution. Kate’s postdoctoral work in Ed Boyden’s Synthetic Neurobiology group at MIT focused on developing novel methods for multiplex control and readout of mammalian cells. Her full first name spells Katarzyna; she goes by Kate for the benefit of friends speaking less consonant-enriched languages. First we discuss Kate’s synthetic cells and whether or not they are living. These are phospholipid liposomes which encapsulate a full central dogma (transcription, translation). Synthetic cells are more complex than biochemical experiments, but at the moment, Kate does not consider her synthetic cells living. These cells are not self replicating, currently requiring a graduate-assisted replication. We then have an extended discussion about the ribosome, why it’s the biggest hurdle to achieving true self replication, and why it kind of sucks as a catalyst! Next, we move on to how synthetic cells can be used to aid in the research of brain computer interfaces (BCI). Kate’s vision is that, because synthetic cells can be so robustly controlled, they represent a form of “programmable goo” which would interface much more robustly with our brains than traditional silicon. She envisions the role of synthetic cells as being used as a less injurious interface for BCIs, which currently cause significant scarring to the brain. Finally, we talk about one of the most interesting topics covered on the molpigs podcast: space exploration! Kate discusses how synthetic cells, being so programmable, might be ideal devices for Martian terraforming. By engineering poly-extremophiles (extremophiles which are robust to many extreme conditions, organisms which do not exist on Earth) specific to the environment of Mars, it may be possible to design a metabolism capable to transforming Martian soil into something fertile. Additionally, synthetic cells might be used as on-board biochemical printers on long space missions. Their programmable metabolism may enable us to produce any biomolecule, such as medicines on demand. --- Find more information at the episode page here: https://podcast.molpi.gs/media/adamala-k-c1a694437dacfe8e/

In the third episode of our ‘Lab Pigs’ series, which highlights the research and journeys of early career researchers in our field, we talked with Erik Poppleton. Erik researches the use of computational modeling in informing the design of molecular machines. As part of this, he also develops general-use analysis tools for oxDNA, and conversion tools to integrate the various design and simulation tools in the nucleic acid nanotechnology ecosystem. We talked about his research, his experience writing academic software, and the relationship between geology and molecular programming. Core Simulation Tools - Main oxDNA documentation: https://dna.physics.ox.ac.uk/index.php/Main_Page - Current stable release (being retired soon): https://sourceforge.net/projects/oxdna/files/ - Bleeding edge release (has Python bindings!): https://github.com/lorenzo-rovigatti/oxDNA - The model is also available as part of LAMMPS, documentation can be found here: https://lammps.sandia.gov/doc/Packages_details.html#pkg-user-cgdna Useful tutorials - A textbook chapter covering how to relax and simulate origamis: https://arxiv.org/pdf/2004.05052.pdf - A textbook chapter covering the details of molecular simulation: https://www.public.asu.edu/~psulc/myimages/chapter.pdf - Example input files: https://github.com/sulcgroup/oxdna_analysis_tools/tree/master/example_input_files Useful tools - TacoxDNA, converters from design software to oxDNA: http://tacoxdna.sissa.it/ - oxView, a visualizer and editor for oxDNA: https://sulcgroup.github.io/oxdna-viewer/ - oxView documentation: https://github.com/sulcgroup/oxdna-viewer - oxdna_analysis_tools, a library of python scripts for basic simulation analysis: https://github.com/sulcgroup/oxdna_analysis_tools - oxdna.org, a public webserver for running simulations: oxdna.org - ox-serve, run interactive simulations in your web browser using a Google Colab GPU: https://colab.research.google.com/drive/1nFC9zy-wEwwl8vlJZAbQZZofavP4PXvL#scrollTo=C_8TB2t5gxDg Of course, if you find these tools useful, please remember to cite us! The citations for each tool can be found in its documentation (oxdna.org paper coming soon!)

In this episode we talked with Yuan-Jyue Chen, of Microsoft Research and the University of Washington, on some of his research into DNA Data Storage. Yuan focussed on two topics: random access of data, and the accompanying issues with stochasticity and errors, and an application of DNA storage for efficiently searching a large database of images by similarity. Please note: The views expressed by Yuan in this podcast do not necessarily represent the views of Microsoft.

In the second episode of our 'Lab Pigs' series, which highlights the research and journeys of early career researchers in our field, we talked with Josie Kishi. Josie was instrumental in developing the Primer Exchange Reaction (PER) synthesis method and the related imaging method, SABER. As well as talking about these, we found out what excites her about molecular programming, how she got into the field, and where she things it's going to go.

We had a long and interesting chat with Tom Ouldridge of Imperial College London on Maxwell’s demon, Szilard’s engine, what people get wrong about thermodynamics and information theory, how this all relates to biology, and how his lab is using these ideas to develop exciting new approaches to molecular programming. Check it out! Abstract: Maxwell’s demon and Szilard’s engine—thought experiments from the 19th and early 20th centuries about the interplay of thermodynamics and information-processing—have long captured the imagination of theoretical physicists. Many still disagree about the interpretation of these ideas, the implications for the second law of thermodynamics, and the consequences for thermodynamics of computation. We have designed a theoretical Szilard engine from biomolecules; by explicitly rendering each step of the engine as a biochemical process, we are able to demystify the whole story. Doing so is helpful not only in resolving old thought experiments, but because the crucial idea—that the generation of correlation between non-interacting degrees of freedom is thermodynamically costly—is of fundamental significance to natural and synthetic molecular information-processing systems.

Join us for the first of our 'Lab Pigs' series, in which we talk with early career researchers on their research and journey within our field. In this episode, we chatted with Dominic Scalise. We talked a lot with Dominic about his work towards building a stored program chemical computer: what this is, the challenges in doing so, the approaches he's taking, and more. We also talked a bit about his experiences in academia, and Dominic made a special announcement for molpigs members at the end about the creation of a Molecular Programming textbook—stay tuned on our newsletter for more information!

Following on from Brenda's fantastic tutorial, we chatted with her to get answers to many questions, find out more about her lab's work, and get her thoughts on the future direction of this approach!

Join us for the first of our 'Meet the Molecular Programmer' series, in which we talk with seasoned academics in our field about their journey and life experiences. In this episode, we chatted with Prof. Rebecca Schulman. We briefly talked with Rebecca about her work from her early PhD journey to more recent work coming from her lab. We heard about her stories, the challenges, fun and exciting memories she had and lessons she learned during graduate school, and her wisdom about running her own lab. Rebecca provided valuable advice to students surviving and thriving graduate school and suggestions regarding working in a lab. We also had wonderful discussion about the interdisciplinary property of our field. Hope you enjoy the conversation! We certainly had a lot of fun!

For our first event, Brenda Rubenstein has presented a tutorial on her lab's approach to storage and computation, making use of the chemical properties of a variety of types of small molecules. This was a real tour-de-force, and is worth a watch. Be sure to listen to our subsequent Q&A session in a couple episodes time! Abstract: As transistors near the size of molecules, computer engineers are increasingly finding themselves asking a once idle question: how can we store information in and compute using chemistry? While molecular storage and computation have traditionally leveraged the sequence diversity of polymers such as DNA, our team has recently demonstrated that vast amounts of information can also be stored in unordered mixtures of small molecules. In this tutorial, I will begin by explaining this new, more general molecular storage paradigm and how polymers fit into it. I will then describe how our team has married combinatorial chemical synthesis with high resolution spectrometry to experimentally realize this paradigm and store GBs of information in small molecules and metabolites. Lastly, I will end with a discussion of how these storage principles can be combined with machine learning techniques to realize fully molecular neural networks for pattern recognition and image processing. The new paradigm discussed in this tutorial will lend itself to new means of increasing molecular storage capacity and interpreting the many small molecule chemistries that underlie "computing" within the body.