Episode Summary: 

Millions of people die every year from chronic diseases. Traditional drug discovery has failed in identifying solutions to many of these persistent health challenges. Functional genomics is offering a way forward by identifying gene networks and enabling the development of drugs with very specific targets. But, rather than just relying on gene targets within humans, Linda and her company, Fauna Bio, are casting a wider net across the animal kingdom. Extreme adaptation is common across many mammals, giving us an incredible pool of potential targets to go after. Whereas a single heart attack can kill a person, certain animals not only survive 25 heart attacks a year but also go on to thrive, living 2x longer than other mammals their size. By identifying and understanding the gene networks underlying these extreme adaptations, Fauna can identify novel targets across 415 different species, map them to human genes, and develop drugs that exploit our natural protective physiological mechanisms.

About the Guest 

• Linda is the Co-Founder and CTO at Fauna Bio, a biotechnology company leveraging the science of hibernation to improve healthcare for humans. She earned an MPhil in Computational Biology from the University of Cambridge and got her Ph.D. in Genetics and Genomics from Harvard University. She previously held positions at the Broad Institute and Stanford University studying comparative mammalian genomics and human disease genetics.

Key Takeaways 

• Many mammals have evolved complex adaptations that enable them to survive in extreme environments or withstand physiological events that humans cannot.
• At Fauna Bio, Linda Goodman and her team are working to better understand the biological networks that underlie these adaptations, in hopes of developing therapeutics inspired by the adaptations of the animal kingdom.

Impact 

• Drawing on a completely new source of knowledge about the defense mechanisms of living organisms, Fauna Bio goes beyond the limitations of traditional drug development and looks for better, more effective drugs based on natural defense mechanisms.

Company: Fauna Bio

Episode Summary

The expression of genes in our genome to produce proteins and non-coding RNAs, the building blocks of life, is critical to enable life and human biology. So, the ability to predict how much of a gene is expressed based on that gene’s regulatory DNA, or promoter sequence, would help us both understand gene expression, regulation, and evolution, and would also help us design new, synthetic genes for better cell therapies, gene therapies, and other genomic medicines in bioengineering.

However, the process by which gene transcription is regulated is incredibly complex; thus, prediction transcriptional regulation has been an open problem in the field for over half a century. In his work, Eeshit used neural networks to predict the levels of gene expression based on promoter sequences. Then, he reverse engineered the model to design specific sequences that can elicit desired expression levels. Eeshit’s work developing a sequence-to-expression oracle also provided a framework to model and test theories of gene evolution.

About the Guest

• Eeshit earned his double major in CS & Engineering and Biological Sciences & Engineering from the Indian Institute of Technology in Kanpur. 
• During his PhD at MIT, working on Dr. Aviv Regev’s team, he published 4 papers in Nature-family journals, including 2 on the cover and 1 on the cover as first and corresponding author. Eeshit’s work is in Cell, Nature Biotechnology, Nature Medicine, Nature Communications, and beyond.

Key Takeaways

• cis-regulatory elements like promoters interact with transcription factors in the cell to regulate gene expression.
• Variation in cis-regulatory elements drives phenotypic variation and influences organismal fitness.
• Modeling the relationship between promoter sequences and their function – in this case, the expression levels they induce – is important to better understand regulatory evolution and also enable the engineering of regulatory sequences with specific functions with applications across therapeutics and cell-based biomanufacturing.
• By cloning 50 million sequences into a yellow fluorescent protein (YFP) expression vector in S. cerevisiae and measuring the YFP levels they induced, Eeshit generated a rich dataset to map yeast promoter sequence to expression levels.
• Next, Eeshit trained neural network models, including convolutional neural networks and Transformers, to predict expression from sequence with high accuracy.
• Eeshit then “reverse-engineered” these convolutional models to create genetic algorithms that designed sequences which could induce desired expression levels.
• Finally, Eeshit’s sequence-to-expression oracle allowed for the computational evaluation of regulatory evolution across different evolutionary scenarios, including genetic drift, stabilizing selection, and directional selection.

Impact

• Eeshit’s work developing a sequence-to-expression oracle provided a framework to model and test theories of gene evolution.
• This framework can help us both understand gene expression, regulation, and evolution, and design new, synthetic genes for better cell therapies, gene therapies, and other genomic medicines in bioengineering.

Paper: The evolution, evolvability and engineering of gene regulatory DNA 

Episode Summary:  

In this very special episode of Translation, Seth is joined by Ash Trotman-Grant to demystify spinning out from academia. Much of this knowledge has so far only been available to select groups of academics and PhD founders are at a disadvantage – some potentially breakthrough technologies  never saw the light of day and didn’t get a chance to have a real impact. We want to bring the power of the tech transfer process back to entrepreneurial scientists.

Enter the Spinout Playbook – your complete guide to spinning out of academia. In this episode, we chat about the Playbook’s content and share useful tips for entrepreneurial academics eager to spin out their research into an impactful company. Ash shares his experience from spinning out Notch Therapeutics and, together with Seth, they offer brilliant insights into navigating the (up until now) stormy waters of the spinout process.

About the Guests

• Seth is a Founding Partner at Fifty Years, a venture capital firm backing founders using technology to solve the world’s biggest problems.
• Ash is a Synthetic Biologist at Fifty Years and Founder of Notch Therapeutics, a stem cell spin out company from the University of Toronto.
• Ash & the Fifty Years team have created the Spinout Playbook, a living document that will help academic founders spin out their companies from universities and negotiate with Tech Transfer Offices – TTOs.

Key Takeaways

• A spinout is a company that has been developed from a university's research.
• The process of establishing the spinout as a new company involves multiple hurdles, like licensing patents from the tech transfer office, splitting equity among academic and full-time founders, and deciding when to leave academia.
• Universities take months to sign agreements and make startups unfundable by taking too much equity.
• The final licensing agreement may include counter-productive clauses that prevent the company from succeeding.
• University tech transfer offices (TTOs) refuse to negotiate directly with grad students and postdocs.
• For Ash, the creation of Notch Therapeutics was his first real step into the entrepreneurship world and the first encounter with the process of spinning out a company.
• The Spinout Playbook, the newest Fifty Years initiative, will serve as a comprehensive guide for founders and scientists wishing to spin out a company.

Impact

• The Spinout Playbook will help future founders and scientists better navigate the challenges of the process.
• Previously only available to a coterie of academics, the know-how of tech transfer will allow great science to see the light of day more easily.
• A transparent process can give scientists the tools and information they need to build world-changing companies, which is hard enough by itself.

The Spinout Playbook