Background
How can AI help us understand the structure of DNA and its function, the very organization of life? The DNA sequence has for a long time been viewed as linear, but since the molecule is compactly folded to fit in the cells, the complex 3D organization likely affects how the information is read. DNA is arranged in chromosomes in the cells, ready to be activated when needed. The relationship between chromosome structure and function is a topic of major interest.
The project proposes that the complex organization of DNA and its relation to the constantly moving 3D-structured chromosomes are affecting the regulation and expression of genes and ultimately protein production. The project aims to draw up a map of how DNA is organized inside an Escherichia coli (E. Coli) cell and how the organization of DNA changes from the birth of the cell until it divides.
Research Question
To improve our fundamental understanding of the design principles of chromosomes, it is essential to first grasp the most basic principles, starting with prokaryotes—before adding layers of eukaryotic complexity (chromatin, chromosome numbers, cell-cell communication).
By identifying the rules of chromosomal organization in a less complex, prokaryotic system such as E. coli, we aim to define the basic building blocks that will allow for optimal design of synthetic chromosomes.
Objective 1
Generating a large set of single-cell bacterial 3D genome structures with partial dynamic information. We apply three orthogonal single-cell approaches to generate the first-of-its-kind database of 3D genome organization and dynamics adopted by the model prokaryote organism, Escherichia coli.
Objective 2
Developing machine learning solutions capable of uncovering the rules that govern the dynamics of the bacterial chromosome structure. This bridging effort will combine learning-based spatiotemporal modeling to integrate the data from the three complementary types of data-rich single-cell experiments in WP1 into a 4D model of the bacterial chromosome.
Aim
The main goal is to decipher the fundamental rules that:
- drive the structural dynamics of bacterial chromosomes in space and time, and
- determine the regulatory effects of spatial gene positioning.
Synergy and Team
The Bienko and Elf groups have ample experience working with big data, already using AI and statistical learning tools to curate and analyze. The effort described in this project is too far-reaching for a standard solution, and the input from Schön’s group is crucial for us to reach the ambitious goals. Schön has a long history of the interplay between basic science and applied problems from the real world.
Cover photo by: National Cancer Institute on Unsplash