Gennady Gorin, Ph.D.

I am a computational biologist with experience in bioinformatics and drug discovery. I am excited to continue using my expertise in the biotech industry. If you are working with challenging and noisy data, let’s talk!

Most recently, I was a Senior Scientist, Computational Biology at Fauna Bio, a biotech startup leveraging non-model organism biology to improve human health. I developed methods for hibernator data, wrote and pitched drug target concept sheets, and integrated external data to support mechanistic studies. 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.

selected publications

2025

Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data

Gennady Gorin, Tara Chari, Maria Carilli, John J. Vastola, and Lior Pachter

Nature Methods, Nov 2025

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Single-cell RNA sequencing analysis centers on illuminating cell diversity and understanding the transcriptional mechanisms underlying cellular function. These datasets are large, noisy and complex. Current analyses prioritize noise removal and dimensionality reduction to tackle these challenges and extract biological insight. We propose an alternative, physical approach to leverage the stochasticity, size and multimodal nature of these data to explicitly distinguish their biological and technical facets while revealing the underlying regulatory processes. With the Python package Monod, we demonstrate how nascent and mature RNA counts, present in most published datasets, can be meaningfully ‘integrated’ under biophysical models of transcription. By using variation in these modalities, we can identify transcriptional modulation not discernible through changes in average gene expression, quantitatively compare mechanistic hypotheses of gene regulation, analyze transcriptional data from different technologies within a common framework and minimize the use of opaque or distortive normalization and transformation techniques.

2023

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.