Gennady Gorin, Ph.D.

I am a Senior Scientist, Computational Biology at Fauna Bio, a biotech startup leveraging non-model organism biology to improve human health. I received my Ph.D. from the Pachter Laboratory at Caltech, establishing foundational theory, tools, and methods for genomics and biophysics.

As an academic and industry researcher, I have combined bioinformatics and physics to improve single-cell analyses and innovate active pipelines for therapeutic discovery. I am excited to use the depth of my interdisciplinary background to develop revolutionary methods for single-cell biology, putting my team at the forefront of the industry and enabling research that changes lives for the better.

For requests to referee scientific articles, please contact me using my personal email, listed below.

selected publications

2023

Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data

Maria Carilli*, Gennady Gorin*, Yongin Choi, Tara Chari, and Lior Pachter

bioRxiv, May 2023

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We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strategy for treating multimodal datasets generated by high-throughput, single-cell genomic assays.
Studying stochastic systems biology of the cell with single-cell genomics data

Gennady Gorin, John J. Vastola, and Lior Pachter

Cell Systems, Oct 2023

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Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.

2022

RNA velocity unraveled

Gennady Gorin, Meichen Fang, Tara Chari, and Lior Pachter

PLOS Computational Biology, Sep 2022

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We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.