The Next Experiment – Détails, épisodes et analyse

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The Next Experiment

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Fréquence : 1 épisode/50j. Total Éps: 14

It's vital that we make progress in biology. Yet today, understanding complex living systems is hard. But does it have to be? We're Markus and Phil, a biologist and a statistician. We created this podcast for anyone who believes that there might be a better way. Together, we'll discuss the best experiments to cut through biological complexity. Join us to explore the shape of the next experiment.

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Données mises à jour le 01/09/2026

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A Billion Oil Tankers and a Slice of Cheese (with Claes Gustafsson)

Saison 2 · Épisode 7

lundi 27 juillet 2026 • Durée 57:53

“We compress DNA from oil-tanker scale to 96 wells - and still capture everything that matters.”

🎙️ The latest episode of The Next Experiment is out!

Markus Gershater and Phil Kay sit down with Claes Gustafsson to talk about the power of design of experiments (DOE) in biology, how to get the most out of your data, and why smart experimentation beats brute force every time.

Claes shares how DOE lets you compress enormous experimental spaces, optimize proteins, and even improve enzymes used in millions of cheeses worldwide. He dives into codon optimization, iterative rounds of testing, and why a good assay is always king.

Some standout lines:

🔹 “If your assay sucks, it doesn’t matter how much DOE you put on it - it will still suck.”

🔹 “Biologists love collecting data, but more data isn’t better. The question is: how do you get the most bang for your data buck?”

🔹 “You don’t need to understand every mechanism. You just need to know which experiments will reliably get the outcome you care about.”

If you’re in biotech, protein engineering, or any field where structured experimentation matters, Claes’ insights are gold.

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Only the Robust Survive (with Rob Howes)

Saison 2 · Épisode 6

lundi 15 juin 2026 • Durée 52:56

“Most assays don’t fail because they’re wrong… they fail because they’re fragile.”

This time, Markus Gershater and Phil Kay sat down with Rob Howes (Head of Biology & DMPK at Charles River) to unpack what REALLY happens when you scale biology - and why most teams underestimate how quickly things fall apart.

Rob’s been working with multi-dimensional experimentation since the early 2000s (back when most of us were still painfully tweaking one variable at a time). And the contrast is pretty stark:

Some standout lines:

🔹 “We used to optimise for the highest signal. Now we optimise for something that won’t break.”

🔹 “If your data isn’t high quality, your AI model won’t be either. Garbage in, just faster garbage out.”

🔹 “Why run every experiment, if you can predict most of them?”

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JMP Discovery Summit LIVE (with Linden Schrecker)

Saison 2 · Épisode 5

lundi 18 mai 2026 • Durée 24:37

“Some people don’t want experiments to be repeatable, they want them to feel like magic in their hands.”

🎙️The latest episode of “The Next Experiment” is here!

This time, Markus and Phil sat down with Dr. Linden Schrecker, co-founder of SOLVE Chemistry, to explore the future of experimentation, automation, and why better data changes everything.

From failed reactions caused by cold Oxford labs to disproving decades of accepted chemistry with a single well-designed experiment, Linden shares a fascinating perspective on how scientists should think about experimentation in the age of AI.

The conversation dives into reproducibility, metadata, machine learning, and the cultural incentives that often prevent researchers from collecting truly meaningful data.

Some standout lines:

🔹 “You can collect 50 experiments in the same time as one, if you design the system properly.”

🔹 “We’re not incentivising scientists to collect meaningful amounts of data. We reward novelty, not repeatability.”

🔹 “We should be collecting data for a future we may never personally benefit from.”

If you’re working in chemistry, biotech, automation, AI, or process development, this episode is packed with insights on how smarter experimentation can unlock entirely new ways of understanding complex systems.

The Quest for the Global Maxima (with Adam Winnifrith)

Saison 2 · Épisode 4

lundi 23 mars 2026 • Durée 01:01:24

“Drug-resistant bacteria played the ultimate uno reverse card on us.”

🎙️The latest episode of “The Next Experiment” is here!

This time, Markus and Phil sat down with Adam Winnifrith - who shared his unique perspective on experimentation, scaling, and why understanding your system is always step one.

Adam dives deep into the realities of doing science at scale, the pitfalls of jumping straight to automation, and how small, smart experiments can save months of wasted effort.

Some standout lines:

🔹 “You can automate all you want, but if you don’t know what’s actually happening in your system, you’re just speeding up failure.”

🔹 “DOE isn’t just a tool; it’s a mindset shift. It forces you to question assumptions before spending a month chasing the wrong lead.”

🔹 “The real magic happens when you combine curiosity with disciplined experimentation.”

If you’re in biotech, research, or any field where experimentation is key, Adam’s insights are not to be missed.

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Automation Saves Your Bacon (with Alex Rimmer)

Saison 2 · Épisode 3

lundi 16 février 2026 • Durée 49:30

"I wouldn’t describe DOE as easy — I’d describe it as extremely worthwhile.”

🎙️The latest episode of “The Next Experiment” is here!

This time, Markus and Phil sat down with Alex Rimmer, who brings a clear and practical perspective on DOE in biology – and some truly memorable lines about why experimentation so often fails to scale.

Alex talks about discovering DOE out of necessity, not theory. When nothing else worked, DOE became the only way to make progress.

A few great quotes:

🔹 “Biology is basically the study of interactions that we largely don’t study interactions for.”

🔹 “I had a need to make a media and absolutely no idea how to get that to work via traditional non-DOE methods.”

🔹 “Automation on its own won’t be enough; it’s what you apply with that automation that really matters.”

If you care about scalable experimentation, media development, or the practical reality of doing modern biology, this episode is full of insight.

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Choose Your Fighter: Bayesian vs DOE (with Owen Jonathan)

Saison 2 · Épisode 2

lundi 12 janvier 2026 • Durée 42:23

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.

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DOE, The Right Thing (with Keara Sutherland)

Saison 2 · Épisode 1

lundi 15 décembre 2025 • Durée 50:22

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

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Bonus Episode: Statisticians vs Biologists

Saison 1 · Épisode 6

mercredi 11 décembre 2024 • Durée 09:36

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

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Starry-Eyed and Sobering Views on AI in Biology

Saison 1 · Épisode 5

mercredi 4 décembre 2024 • Durée 18:45

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

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What the Past Teaches Us About Future Experiments

Saison 1 · Épisode 4

mercredi 27 novembre 2024 • Durée 25:47

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

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The Next Experiment – Détails, épisodes et analyse

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