Automated navigation of condensate phase behavior with active machine learning

Y.H.A Leurs¹, W. van den Hout¹, A. Gardin¹, J.L.J. van Dongen, Andoni Rodriguez-Abetxuko, Nadia A. Erkamp J.C.M. van Hest^* Francesca Grisoni^*, L. Brunsveld^*
1These authors contributed equally to this work.
^*Corresponding authors: [email protected], [email protected], [email protected].

Abstract

Biomolecular condensates are essential cellular structures formed via biomacromolecule phase separation. Synthetic condensates allow for systematic engineering and understanding of condensate formation mechanisms and to serve as cell-mimetic platforms. Phase diagrams give comprehensive insight into phase separation behavior, but their mapping is time-consuming and labor-intensive. Here, we present an automated platform for efficiently mapping multi-dimensional condensate phase diagrams. The automated platform incorporates a pipetting system for sample formulation and an autonomous confocal microscope for particle property analysis. Active machine learning is used for iterative model improvement by learning from previous results and steering subsequent experiments towards efficient exploration of the binodal. The versatility of the pipeline is demonstrated by showcasing its ability to rapidly explore the phase behavior of various polypeptides, producing detailed and reproducible multidimensional phase diagrams. The self-driven platform also quantifies key condensate properties such as particle size, count, and volume fraction, adding functional insights to phase diagrams.

Content

This repository contains the code used to apply the active machine learning pipeline described in the main paper and depiceted in the Figure above, section III, panel H, I, J.
This repository is structured in the following way:

• experiments/ : folder containing the experiments completed by our platform and some test cases.
• figures/ : folder containig high resolution figure as reported in the main paper, and instructions on how to re-create the figures.
• robotexperiments/ : main folder containig the .py files defining the package.
• script/ : folder containig scripts for setting up (experiments/) and running (cycles/) the experiments, and for plotting results (plots/).
• environment.yml : the environment file to isntall the package.
• setup.py : the file for installing the package.

Installation

Install dependencies from the provided env.yaml file. This typically takes a couple of minutes (tested on Ubuntu 22.04.3).

conda env create -f env.yaml

The installation requires another custom python package, stated in the environment.yml file and available at this link, ActiveLearningCLassiFier.

How to cite

You can currently cite our preprint:

Automated navigation of condensate phase behavior with active machine learning.
Yannick Leurs, Willem van den Hout, Andrea Gardin, Joost van Dongen, Andoni Rodriguez-Abetxuko, Nadia A. Erkamp, Jan van Hest, Francesca Grisoni, Luc Brunsveld
ChemRxiv, 04 December, 2024. DOI: https://doi.org/10.26434/chemrxiv-2024-frnj3