ayush noori

Season 5, Episode 2: Novel Translational Therapeutics with Linda Goodman

Episode Contributors: Ayush Noori, Ashton Trotman-Grant, Linda Goodman

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, is 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.

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

  • 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.

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

Season 5, Episode 1: Building the DNA Oracle with Eeshit Vaishnav

Episode Contributors: Ayush Noori, Ashton Trotman-Grant, Eeshit Vaishnav

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

Season 4, Episode 5: DNA Origami with Anastasia Ershova

First Author: Anastasia Ershova

Episode Summary: DNA is an ideal molecule for storing information in our genomes because it’s stable, programmable, and well understood. The same qualities make DNA a great building block or construction material for nanoscale biomolecular structures that have nothing to do with our genome, like molecular scaffolds created by folding DNA into 2D and 3D shapes. This technology is known as DNA origami.

However, the practical applications of DNA origami are limited by spontaneous growth and poor reaction yields. Anastasia developed a method that uses crisscross DNA polymerization of single-stranded DNA slats or DNA origami tiles to assemble DNA structures in a seed-dependent manner. This work may be useful to produce ultrasensitive, next-generation diagnostics or in programmable biofabrication at the multi-micron scale.

About the Author

  • Anastasia is a PhD candidate at Harvard University, currently working on DNA nanotechnology in William Shih's lab at the Wyss Institute and Dana-Farber Cancer Institute.

  • She received her bachelor’s degree in Natural Sciences from Cambridge University.

  • During her PhD at Harvard, she co-founded the Molecular Programming Interest Group, an international community of students in the molecular programming, DNA computing and related fields.

Impact

  • DNA Origami will provide us with a plethora of new information on biology and physics.

  • By manipulating that data on the nanoscale, we can get answers to a lot of questions in the future.

  • Quick diagnostics can enable people all over the world to quickly get diagnosis-related answers and seek targeted treatment.

Papers: Robust nucleation control via crisscross polymerization of highly coordinated DNA slats

Multi-micron crisscross structures from combinatorially assembled DNA-origami slats

Season 4, Episode 4: Illuminating Biological Context with Josie Kishi

First Author: Jocelyn Kishi

Episode Summary: Technologies like next-generation sequencing allow us to understand which RNA transcripts and proteins are expressed in biological tissues. However, it’s often equally important to understand how cells or molecules are positioned relative to one another! Whether it be a cell changing its shape, an organelle ramping up a metabolic process, or a DNA molecule traveling across the nucleus, understanding spatial context is critical. Current approaches for spatial sequencing are limited by cost, complicated equipment, sample damage, or low resolution. Recognizing this challenge, Josie and team developed Light-seq, a cheap and accessible method to combine sequencing and imaging in intact biological samples. Not only is the method inexpensive, but Light-seq can also achieve unprecedented spatial resolution by using light to add genetic barcodes to any RNA, allowing scientists to determine exactly where sequencing should occur with extreme precision. By helping researchers to understand spatial context, Light-seq-driven insights may illuminate cancer, neurodegeneration, and autoimmunity.

About the Author

  • Following her lifelong passion for computer programming, Josie studied Computer Science at Caltech and worked as a software engineering intern at Google. At Caltech, a biomolecular computation course introduced her to the field of biomolecular programming.

  • Josie quickly got excited about the intersection of computers and biology and its potential to bring about positive change in the world. She pursued this interest in her graduate studies in the Wyss Institute for Biologically Inspired Engineering at Harvard, where – as first a postdoctoral fellow, and then the Technology Development Fellow – she developed platform technologies for DNA-based imaging and sequencing assays.

Key Takeaways

  • Next-generation sequencing is a powerful technology to read the transcriptomic state of biological tissues by surveying the RNA transcripts present.

  • However, it’s important to understand not only what is being expressed but where this expression occurs! The spatial arrangement, structure, and interactions between molecules are critical to define the functions of biological systems.

  • By linking imaging with -omics profiling, the field of spatial biology seeks to understand molecules like RNAs in their 2D and 3D contexts.

  • Unfortunately, currently available spatial transcriptomics methods are limited in their ability to select individual cells with complex morphologies, require expensive instrumentation or complex microfluidics setups to the tune of several $100K, and often damage the samples.

  • Further, rare cells are often missed due to lower sequencing throughput, even though they may be critical for biological activity.

  • Recognizing this challenge, Josie and her collaborators developed Light-seq, a new, cheap, and accessible approach for single-cell spatial indexing and sequencing of intact biological samples.

  • Using light-controlled nucleotide crosslinking chemistry, Light-seq can correlate multi-dimensional and high-resolution cellular phenotypes – like morphology, protein markers, spatial organization) – to transcriptomic profiles across diverse sample types.

  • In particular, using the biological equivalent of photolithography, Light-seq can add genetic barcodes to any RNA by shining light on it, allowing scientists to control exactly where sequencing should occur with extreme precision – up to the subcellular level.

  • Light-seq can operate directly on the sample: the method does not require cellular dissociation, microfluidic separation/sorting, or custom capture substrates or pre-patterned slides.

  • Samples used for Light-seq remain intact for downstream analysis post-sequencing.

  • Josie evaluated Light-seq on mouse retinal sections to barcode three different cell layers and study the rare dopaminergic amacrine cells (DACs).

Impact

  • Josie created a cheap, accessible, and powerful tool for scientists to perform spatial sequencing at unprecedented resolution without requiring expensive or complicated setups.

  • By enabling new advances in spatial biology, Light-seq has the potential to help biologists discover biomarkers for disease, measure on and off target effects of therapeutic candidates, and illuminate poorly understood biological mechanisms where understanding spatial context makes all the difference.

Paper: Light-Seq: Light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing

Season 4, Episode 3: Peering Inside the Immune Response for Novel Antibodies with Nima Emami

Episode Contributors: Seth Bannon, Michael Chavez, Nima Emami

Episode Summary: Antibodies are one of the greatest tools we have in our therapeutic arsenal and have transformed the way we treat cancer and autoimmunity. But we still largely develop these drugs using guess and check methods, massively slowing down the process. However, our own B cells are constantly making new antibodies against the pathogens and diseases we routinely suffer from, creating a gold mine of drugs floating around inside all of us. We just need to find them! Recognizing this challenge, Nima and his team at Avail Bio have leveraged their deep experience in computation and systems immunology to build a platform that massively screens the antibody repertoire of patients who have successfully cleared a disease. With it, they find ready-to-deploy antibody drugs that could treat everything from cancer to autoimmunity and even reprogram our own immune system!

About the Guest

  • Nima Emami is the CEO & co-founder of Avail Bio. He received a PhD in Bioinformatics from the UCSF Cancer Center, and studied Bioengineering, Electrical Engineering and Computer Science at UC Berkeley.

Key Takeaways

  • The immune system contains a massive diversity of antibodies that hold clues on how to fight disease. Avail has developed a platform to discover and develop these antibodies for cancer and autoimmune disease.

  • Companies that spin out of universities can pair with accelerators early on to both raise funding and make progress with a small amount of capital. The most challenging part of pulling IP out of a university is speed. Public universities that generate many spinouts are often overwhelmed with the amount of inventions disclosed concurrently, which lengthens the time required for tech transfer.

  • Avail’s platform combines synbio, machine learning and genomics to both discover and validate targets, and ultimately translate those targets into drugs. Failure of clinical stage programs in cancer trials can be traced back to the failure of mouse models to faithfully recapitulate the cancer biology or the immunobiology that we see in humans.

  • The future that Avail hopes to create is one where drugs developed using their platform will reach patients, thereby changing the drug discovery paradigm to be more data-driven.

Impact

  • The platform that Avail is building peers inside the human immune response to find and develop novel antibodies to cure cancer and autoimmune disease.

Company: Avail Bio

Season 4, Episode 2: Powering the Biocomputing Revolution with LatchBio

Episode Contributors: Michael Chavez, Ashton Trotman-Grant, Ayush Noori, Alfredo Andere, Kyle Giffin, Kenny Workman

Episode Summary: Imagine if every graphics design company built its own version of Photoshop in-house. That’s exactly what’s happening today in biology research. Ten-fold increases in data every two years are forcing every biology team to build out their own, in-house bioinformatics stack to store, clean, pipe, and manage the massive volumes of data generated by their experiments. All that work has to happen even before teams can analyze the results! Recognizing this obstacle to high-throughput biology research, Alfredo, Kenny and Kyle built LatchBio to bring the modern computing stack to biotech. By uniting wet lab experiments with dry lab processing, storage, and analyses, LatchBio is democratizing access to top-notch bioinformatics and empowering biologists to derive relevant insights from their data that can move our world forward. Tune in to learn more about their journey from Berkeley dropouts to entrepreneurs building no-code tools to power the biocomputing revolution.

About the Team

  • Alfredo Andere, CEO, was born in Mexico City and raised in Guadalajara, Mexico. He majored in Computer Science and Electrical Engineering and minored in Math at UC Berkeley before dropping out to co-found LatchBio.

  • Kyle Giffin, COO, attended UC Berkeley to study Cognitive Neuroscience and Data Science before dropping out to found LatchBio.

  • Kenny Workman, CTO, started engaging in molecular biology research when he was 15, first at local community colleges as a lab hand and then at MIT and UC Berkeley over successive summers. Prior to co-founding LatchBio, he worked at Asimov and Serotiny as a Software and Machine Learning Engineer.

Key Takeaways

  • After hundreds of interviews with biotech leaders to discover pain points around managing data, the founders developed the LatchAI platform.

  • Common biology analyses require piping gigabytes/terabytes of data, meaning data storage and retrieval require programming expertise.

  • Although scientists may be experts in biological theory and wet lab experimentation, programming expertise is scarce. Biologists must rely on limited computational analysts to process and visualize their data; thus, access to bioinformaticians is a bottleneck in the scientific discovery process.

  • On the flip side, bioinformaticians are often hampered by repetitive analysis tasks, preventing them from innovating new computational methods.

  • Recognizing this disconnect between biologists and bioinformaticians, Alfredo, Kenny, and Kyle launched LatchBio: an end-to-end biocomputing platform to allow both wet lab and dry lab scientists to get back to what they’re trained to do - science!

  • The team recently launched their SDK - a Python native developer toolkit - to bridge the divide between the computationally literate bioinformaticians and the no-code savvy biologists.

  • The goal of LatchBio is to become the universal cloud computing platform for academic research and industry biotech.

Impact

  • The no-code platform that LatchBio is building is bringing the modern computing stack to biotech, streamlining data analysis so scientists can focus on solving the world’s biggest problems with biology.

Company: LatchBio