Translating Proteomics

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‘Translating Proteomics’ explores the science of proteomics and its growing impact on biological research, biomarker discovery, drug development, food and energy security, and a range of other timely topics. The goal of these conversations is to expose you to important issues in proteomics, deepen your love of science, and prompt you to question assumptions about what may be possible.

Your hosts are Drs. Parag Mallick and Andreas Huhmer of Nautilus Biotechnology. Parag is an Associate Professor at Stanford University whose lab performs systems biology studies that drive precision medicine approaches for cancer diagnosis and treatment. Andreas is a veteran scientist whose industry work has supported thousands of proteomics researchers by helping to bring the latest mass spec technologies into their labs.

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All episodes of the Translating Proteomics podcast

The idea to measure the proteome to get a clear understanding of healthy and diseased tissues at the molecular level has been around for many years but has not come to fruition in a broadly accessible and applicable way. In this episode we discuss:

  • Why now is the time to make this goal a reality
  • Why past efforts to broadly leverage proteomics did not work out
  • What we’ve learned from the past
  • What’s changed in proteomics and science in general that makes a proteomics breakthrough possible

Learn more about proteomics

Sure, proteomics may revolutionize precision medicine and biomarker discovery, but did you know it can help make better cheese? Listen to this episode of  “Translating Proteomics” featuring Nautilus Co-Founder and Chief Scientist, Parag Mallick, and Nautilus Senior Director of Scientific Affairs and Alliance Management, Andreas Huhmer to learn the many ways we can put the proteome to work as the proteomics revolution begins to bear fruit.

Learn more about applications of proteomics

It’s no surprise that biological systems change dramatically over space and time, but we often ignore these dynamics when comparing biological samples. In the latest episode of Translating Proteomics, Parag and Andreas discuss why it’s essential to take space and time into account and envision ways we can design experiments that explicitly incorporate spacial and temporal considerations.

Chapters:

00:00 – Biological systems as dynamic, adaptive systems

04:45 – How current experimental designs rarely take space and time into account

11:54 – The tools necessary to sufficiently measure biology in space and time

Some key takeaways from the conversation:

  • Different biological processes occur at very different time scales
  • Complex, multiomic interactions can only be understood over time and space
  • We need to properly collect, annotate, and share omics-level data in order to understand the rules that govern complex biology

Protein biomarkers are proteins measured as indicators of biological processes. People often hope biomarkers will take the form of elevated or decreased amounts of single proteins, but few single protein measurements provide specific and sensitive indications of biological processes. In this episode of Translating Proteomics, Parag and Andreas discuss why it is difficult to find new biomarkers and describe how new techniques can enable the development of multi protein, multi-time point, and even multiomic biomarkers that have more potential than any single protein measurement.

Learn more about biomarkers.

Chapters:

00:00 – What are biomarkers and why are they hard to find?

06:40 – What makes a good biomarker?

13:35 – How we can move beyond single-protein/single measurement biomarkers

Some key points of discussion:

  • Biomarkers are difficult to find because of the methods we use to find them and because there is a ton of variability in natural biological systems
  • Most proteins are biomarkers
  • We need more proteome-scale data over space and time to find new biomarkers

From high school biology on up, we’re taught the central dogma of biology – that biological information flows from DNA to RNA to proteins. This representation of the central dogma is, however, very much a simplification of its original formulation by Francis Crick and over-applying it can lead us down spurious paths and faulty conclusions. In this episode of Translating Proteomics, Parag and Andreas dive into the real meaning of the central dogma and discuss how modern biology research, including proteomics, shows we must drastically alter the ways we use and interpret the central dogma.

Chapters:

00:00 – What the central dogma actually says

08:06 – Why it’s important to develop models of biology that account for regulation

11:58 – How new tools will help us make better models of biology

Some key points of discussion:

  • The central dogma is a description of where proteins come from
  • Regulation is not encapsulated in the central dogma
  • We need new models of biology and perhaps even a general theory of biology

AI might be the biggest buzz word of the decade, but the buzz is warranted in terms of its practical potential in biological research. In this episode of Translating Proteomics, Parag and Andreas discuss some of the early wins for AI in biology, practical ways AI can be applied to biology research in the near term, challenges in that application, and how proteomics researchers in particular can use AI to advance their work.

Chapters:

  • 00:00 – Why now is the time to apply AI to biomedicine
  • 05:28 – Difficulties and potential solutions when applying AI to biology
  • 14:20 – How will AI impact the study of proteins
  • 19:34 – Risks of AI in biomedicine

Some key points of discussion:

  • AI has tremendous potential in biomedicine
  • AI can help us recognize patterns in biological data, but we need more data to maximize usefulness
  • We can better leverage AI in biomedicine if biological data and data sharing are standardized

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