Pachter Lab Biophysics Home

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Our Motivation

As biological data becomes increasingly complex and multimodal, we need tools which can interpret the relationships between these high-dimensional, noisy measurements and illuminate the intertwined components of DNA and RNA regulation. In the Pachter Lab, we harness stochastic, biophysical models to represent these high-throughput, genomics data. With our tools, we aim to explicitly model the noise in the data, to capture and reveal important biological variation as well as technical effects of the sequencing pipeline. By treating the underlying biophysical processes which generate our molecular measurements, we can uncover how the processes of the central dogma define cellular diversity, differentiation, and perturbation.

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Table of tools

Below is a table of the Pachter Lab’s current tools for biophysical modeling of high-throughput genomics data. The main features and input data types are listed across the columns. All methods require data with UMIs (molecular count data).

Tool	Task	Resolution	Modalities	Steady State?	Technical Noise?	Language
Monod	Parameter Inference	gene	U/S RNA	yes	yes (3’ seq)	Python
biVI	Parameter Inference	cell/gene	U/S RNA	yes	coming soon	Python
meK-Means	Clustering	gene	U/S RNA	yes	yes (3’ seq)	Python
Chronocell	Trajectory Inference	gene	U/S RNA	no	no	Python
Spatial: coming soon	Parameter Inference	gene	S RNA	yes	yes	Python

For more details on the available methods see Our Packages

Not sure which tool is best for your data? See Choose Your Tool.

Foundational Literature

Explore the foundational literature for biophysical modeling of transcription in Foundations, and the lab’s publications at Our Contributions.

Note

This project is under active development.