Advertise on podcast: The Next Experiment

This time, Markus and Phil sat down with Owen Jonathan, who brings a refreshingly open and grounded perspective on learning – and teaching – DOE in real biological systems. What makes Owen’s story so compelling is how honestly he talks about coming into DOE with an open mind, realising what he hadn’t been taught, and then discovering how powerful the methodology becomes when biology is too complex for intuition alone. A few standout quotes: 🔹 “I didn’t realize until later that I’d never actually been taught DOE.” 🔹 "Progress isn’t about doing more — it’s about asking the right questions first." 🔹 “Biology needs abstraction or it becomes impossible to model.” If you’re interested in practical experimentation, ML-enabled workflows, or how real scientists bring DOE into messy biology, this one’s packed with insight.

If efficiency is the name of the game in process development, then Design of Experiments (DOE) is the playbook you didn’t know you needed. But how do you actually apply it in the messy, variable world of viral vector production? That’s exactly what we dig into in this episode. Keara Sutherland takes us through her journey from simple drug pairing experiments to complex, high-dimensional DOE campaigns that optimize biological processes in ways that feel almost magical – but are grounded in solid strategy. Conversation highlights: 05:24 - Keara’s first exposure to DOE and transitioning from drug synergy studies to complex process optimization 12:17 - The addictive efficiency of DOE: maximizing data points while minimizing time 17:26 - DOE as a tool for biological flexibility: managing multiple outputs and future-proofing processes 31:31 - Real-world challenges: balancing experimental ideal with practical constraints, including automation and scalability

Making multidimensional experiments a reality doesn’t just sit with one discipline.  Most commonly, it involves biologists and statisticians. So, what’s the key to open communication, and working together to do radically better biology? That’s what we explore, in this season’s bonus episode, filmed at Synthace’s Beer, Bytes and Biology annual event.  Conversation highlights 00:00 - Introduction 00:49 - Condescending attitudes causing statisticians and biologists to butt heads  02:49 - The key to successful relationships between the 2 disciplines 04:09 - Markus’ experiences showing how he and his team bridged the communication gap  08:39 - Embodying the “George Box” attitude for successful collaboration 

If data is the new oil in an AI centric world, then amassing ever-larger multidimensional datasets can only be a good thing.  But how can we use these datasets to gain deeper insight into biology? This is the question we try to answer in season 1’s final episode. Between us, we bring views of the AI optimist and skeptic—with starry-eyed visions and sobering realities—to the table before reaching a conclusion. Conversation highlights 00:00 - Introduction 01:18 - A vision for multidimensional datasets fueling better experiments 03:37 - AI and myth-busting: “It’s not magic”   09:15 - Risks of combining datasets for making “pizza” and “ice cream” 10:11 - Ideals and potential low-hanging fruit for AI in biology 13:09 - Thinking about AI as collective memory 18:16 - Combining human and artificial intelligence is key

To explore what the future of multidimensional experiments might look like, we decided to look back. In this episode, we explored how different multi-dimensional (aka Design of Experiments, or DOE) methods have come about to date.  Then, we pondered how these different methods, together with tech and software innovations, have brought both opportunities to scale multidimensional experimentation, and exciting questions on the best way to go about it. Conversation highlights 00:00 - Introduction 01:04 - The question mark around automation: what to do with 1000+ runs   10:15 - History of multidimensional (Design of Experiments / DOE) methods to date: 10:58 - Era 1: Factorial designs came about, and benefited agriculture 15:12 - Era 2: Response surface methods emerged in process industries 18:17 - Era 3: Software packages helped design the “optimal” experiment   21:29 - “DOE 4.0”: Bayesian optimization, and the pros and cons for biology 

In this episode, we delve into Markus’ experiences with doing multidimensional biological experiments manually—from the exhilarating progress he made, to the definitive results he produced. Plus, we touch on how automation can scale multidimensional experimentation, and when is the right time to bring it into the mix. Conversation highlights 00:00 - Introduction 01:24 - Markus’ early manual, multidimensional experiments, and their definitive results 06:07 - The overwhelming number of combinations you explore in biology vs chemistry 11:39 - What happens when you don’t use multidimensional methods 20:37 - When you should automate multidimensional experiments 23:02 - The exciting, uncharted territory that automation brings

It’s easy to say that the tried-and-tested way of doing biology isn’t helping us progress. It’s quite another to embrace new approaches.  That’s what we’re covering in this second episode. We talk about communicating the power of multidimensional experimentation for biology, the insights they unlock—and how often, it takes some time to entertain new-and-improved ways of working. Conversation highlights 00:00 Introduction 01:36 Phil’s tale of Dr Stevie vs Dr Charlie, or traditional vs multidimensional methods 10:40 Multidimensional? Design of Experiments? DOE? It’s all one in the same  12:19 How Markus got to a 7-fold increase in 3 weeks using multidimensional methods 15:55 The magic of multidimensional experiments lies in the statistics 16:49 Markus’ envelope-pushing multidimensional experiment with 27 factors 20:00 Markus admits that at first, he dismissed multidimensional experiments

In our first episode of The Next Experiment, we start by unpacking that all-important question:  Why is biology so hard?  In order to answer it, we get into the fundamentals. The nature of nature.  We talk about how biology’s interconnectedness makes experimentation in biology so uncertain; why the standard method of varying one parameter at a time isn’t cutting it; and how switching to a multidimensional approach is more important for biology than any other scientific discipline. Conversation highlights 00:00 Introduction 00:45 Why biology is different from other scientific disciplines 03:40 What emergence means, and how it relates to biological systems 06:03 Chemistry is a “solved problem”, and biological systems are “black boxes” 12:18 How multidimensional methods helped Markus make definitive progress 16:50 The counter-intuitive science lessons Phil remembers from primary school

We’re Markus and Phil, a biologist and statistician. We decided to come together and discuss the best experiments to cut through biological complexity. If you have this sense that there might be a better way of making progress in biology, subscribe and join us. Stay tuned for our very first episode, launching on November 6th.