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In Episode 30, Zan Luthey-Schulten tells us the story of her most ambitious project over the last fifteen years or so: creating whole-cell simulations. In a reminder that true science knows no boundaries, she ties together a whole range of scientific disciplines - hardware optimization, stochastic calculus, reaction rates, advanced Hamiltonians, synthetic biology, cell imaging data, to eventually approach bioengineering and medicine. Zan also shares her stories and thoughts about how to pick collaborators, how to work together, and what we computational scientists can learn both from each other and from the broader field of biology or biophysics.

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Jerome starts our conversation by reviewing the history of the ABF method and its advantages compared to the main competitors, and connects it to the development of COLVARS, historically very parallel to how the development of the Plumed tool stemmed from the needs of the metadynamics community. We discuss the benefits of graphical interfaces in biomolecular workflows, and touch upon the question of connecting multiple software environments and communities. We then move on to discuss membrane systems and the challenges they pose, both historically and today, and end up on the alchemical side, talking about the latest approaches to alchemical free energy calculations from several exciting angles. Eventually, we agree that regardless of software developments, it's learning and helping others learn to understand molecular systems that's the most rewarding part of the job of a biophysicist.

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In Episode 28, Yuji Sugita shares the story of how he developed temperature replica exchange in the lab of Yuko Okamoto, connecting to his early experience from working with Nobuhiro Go, the father of Go models. We then talk about the process of building up workflows for simulating massive atomistic systems, a multi-year collaboration with Michael Feig, and ponder the question of when one should go about writing their own scientific software rather than reusing existing software packages. Talking about molecular crowding naturally brings us to current and future directions, which for Yuji include simulating increasingly multi-component condensates and exploring multi-resolution schemes in GENESIS. Towards the end, he highlights the need for young researchers to engage with the international community through long-distance collaborations, regardless of where one ends up living and working.

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In episode 27, Kresten starts by explaining his path from a wet lab biochemist to a computational biophysicist, a story full of open-ended explorations and helpful mentors. He gives us some background on how both the legacy and latest models developed, highlighting how in each case the driving force were experimental results that either weren't quite matching simulations, or were plenty enough to allow for top-down training. We walk through some of the functions and applications of intrinsically disordered regions, or IDRs in short, and their relevance for medical research. Then towards the end, Kresten shares some tips and observations from his work in grant evaluation, insisting that internal peer review remains the best source of feedback, but in the end it's one's scientific intuition that has to guide us.

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In episode 26, we talk about the origins of Pratyush's passion for statistical mechanics, deeply rooted in his background in material science, and think about how we can promote a profound understanding of statmech theory among people working in computational biophysics. From there, we explore ways of re-introducing physical rigor into modern data-driven approaches, which is the main concern that Pratyush says drives his research agenda. He ends up sharing a ton of interesting points on working with the industry, the value of education and knowledge sharing, or the philosophy of complex and cognitive systems, and ends up with a call for more time for silent thinking, where - he says - most of his original ideas came from.

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In episode 25, Ivet and me start with a general overview of the elastic network theory and its applications to biology, as well as its strengths and limitations. Ivet then tells us about the specific takeaways from the different lines of her research, talking about evolutionary dynamics signatures, mode excitations in allosteric effects, as well as her recent research on the relevance of hinge regions for drug discovery. We talk about the relevance of the proteins-as-graphs picture for machine learning, and end up with a few general reflections about the complementary roles of persistence and alertness in scientific careers.