What is this all about? Does the computational community need a podcast? Along with the first interview, I'm sharing the idea behind PSI, and explaining why the time is ripe for a new marketplace of ideas to drive the changes in how we do science. Everyone is invited, from ambitious undergraduates to seasoned veterans!
What is this all about? Does the computational community need a podcast? Along with the first interview, I'm sharing the idea behind PSI, and explaining why the time is ripe for a new marketplace of ideas to drive the changes in how we do science. Everyone is invited, from ambitious undergraduates to seasoned veterans!
Hello my name is milosz wieczór. I'm a postdoctoral fellow at the Institute for Research in Biomedicine in Barcelona, and I welcome you to Phase Space Invaders, a podcast where we discuss the future of computational and biophysics biology and chemistry. This is a short introductory solo episode to explain the idea behind the podcast. In the upcoming episodes, I'll be holding interviews with principal investigators all around the world to ask them their opinion On the future of the field, we'll discuss where the AI revolution will take us. What are the most important scientific practices we engage in? How scientific publishing will change in the future, and all things you might find relevant about the evolution of the field so I want to dwell for a moment on the reason why I feel a podcast like this is actually needed. I've been in the field of computational biophysics and molecular simulations for about a decade now, and in many conferences, workshops, and meetings I've attended, I understood it's not the routine reporting of scientific results that is the most enriching, but rather the part where people share their opinions, the common knowledge, the upcoming breakthroughs where one can listen to all these brilliant minds come together in real time and you know, discuss things that never appear in publication. At the same time, when just starting your adventure in science, there's all this time from the moment you're given your first project as a student to the moment where you have the results to go to a conference and present and actually hear out those conversations and maybe even participate in them. And during all that time, you're essentially cut off from all those voices, from all those very important conversations that are going on in the field, even though these are the formative years that might decide whether you will become a scientist or not. And I believe as a field we're not doing a good enough job so far in making sure that people get the support and hear all the relevant voices early in the career. So I know this from experience, but also from the time I spent advising on online forums where a lot of people say that they're just one person in the lab working on a computational project. They don't really have anyone to talk to who's an expert on that. Maybe they don't have the financial means to come to a conference to meet other people, or they're just starting their career and choosing wisely where they're investing their time. So in those cases, I hope you'll find something interesting and exciting in our conversations. I don't want to only focus on early career scientists. If you're a researcher with lots of experience in, for example, computational biophysics. I hope you'll also be inspired by the conversations where we'll discuss future directions, best practices, emerging fields, and so on. Maybe you'll join the conversation yourself and contribute your experience and ideas. S. Finally, we all very often find ourselves looking for some lighthearted scientific content. So sometimes we don't have the focus to go through figures, scientific articles, or textbooks. Maybe we're on our commute or having lunch and we want to learn something or find inspiration, but without necessarily going through intellectual heavy lifting that's why I think that these conversations about more high level aspects of research might be a fun way for you to spend your time, and I hope you'll stay with us for the upcoming episodes I want to finish off with a personal list of research topics that I find relevant to my near future. I'm myself working on the development of energy models or force fields for nucleic acids. Using both standard and machine learning methods. And while challenging this field is so exciting because RNA has so much intrinsic plasticity that it basically defies the standard concepts of protein folding that we know. So that's one reason why we're not having an RNA version of alpha fold two, for example. And in the meantime. RNA based therapeutic tools are literally booming now. Coming up are splicing modifiers, small molecules that stabilize certain RNA folds, personalized, RNA vaccines, or RNA therapeutics that interfere with RNA processing and various stages All those things need to be modeled to be understood, to be then improved. So this is one place where our computational tools can actually make a big impact in medicine or biomedicine of the future. At the same time, I see more and more often how our old methods are challenged by the sheer size of the macromolecular assemblies that are being solved experimentally. These days, it's not uncommon to see systems that are in the Megadalton regime, so systems with hundreds of thousands or millions of atoms and if you want to get any real insight into the science of these assemblies, it is not just that we want to model structures, which we kind of already can use increasing AI tools such as alpha fold, but we also want to simulate them to see the dynamics. And to see the dynamics of such assemblies, we need to have the accuracy of physics-based force fields, but we need the speed of mesoscale or coarse grain models. How to combine these two sources of insight is I think going to be the big question for the years to come. The other question we will explore here, how can we do computational science in a better way, is something I want to approach by listening to the voices of the community. But there are several broad categories of concern here. One is purely technical, think sharing data, reporting honest statistics, or documenting software. Secondly, we have the social aspect of being a scientist, and that involves how we manage networking, share, and distribute credit for work, or find ways to be scientifically critical while remaining kind to each other. Finally, there are also questions of personal qualities from what it means to be a mentor to how to manage a lifelong learning schedule. I'm sure our guests will bring up many questions I myself would have never thought of. So that's it for the brief introduction. Thank you for listening. This episode will be released back to back with the first interview, so please feel invited to go check it out and get excited about the future of computational biology together with me at Phase Space Invaders.