Author interview
Kiran Kumari is currently pursuing her PhD at the IITB-Monash Research Academy, a joint program between IIT Bombay, India, and Monash University, Australia, where she developed a model to reconstruct chromatin configurations. She grew up in Ranchi, India, and earned her Master’s degree in Chemical Engineering from the Indian Institute of Technology Dhanbad.
Lab: Prof. Ranjith Padinhateeri, IIT Bombay, India and Prof. Ravi Jagadeeshan, Monash University, Australia
Research Summary:
DNA folding influences gene activity and cell function. This study presents a method to predict 3D DNA structures from experimental contact data, providing insights into gene regulation across different cell types.
What was the core problem you aimed to solve with this research?
The core problem we aimed to solve was how to accurately predict the 3D structure of chromatin (DNA and proteins) inside cells using experimental contact data. While scientists can measure how often different DNA segments come in contact and produce a 2D image of the structure, they cannot directly see the full 3D structure. This structure is important because it affects gene activity, influencing biological processes and diseases.
Our research developed a computational method to reconstruct chromatin’s 3D structure, helping scientists better understand how genes are regulated and how chromatin organization impacts cellular function.
Comparison of the original experimental contact probabilities (a and b) with the predicted contact probabilities (c and d) using the IBD method for K562 and GM12878 cell lines.
How did you go about solving this problem?
To solve this, we developed a computational method called Inverse Brownian Dynamics (IBD) combined with a polymer model. We started with experimental contact probability data — how often different chromatin regions interact — and treated the challenge as an inverse problem: working backward to determine interaction strengths between chromatin segments.
We used a bead-spring chain model where chromatin is treated as a chain of beads, and we optimized interaction forces between beads to match observed contacts. Unlike many existing models, we did not assume a direct relationship between spatial distance and contact probability, which helped us overcome key limitations.
We also incorporated hydrodynamic interactions, which enabled us to simulate the dynamic behavior of chromatin. This helped in predicting chromatin structure in different gene states and cell types, offering insight into gene activation or silencing.
How would you explain your research outcomes (Key findings) to the non-scientific community?
Our research focused on understanding how chromatin, the genetic material inside our cells, is folded and organized in 3D space — a structure that affects whether genes are turned on or off.
We created a method to predict the 3D shape of chromatin based on how often different parts of it touch each other. Previous models assumed fixed rules, but we instead calculated interaction strengths to fit real-world data.
A key finding was that chromatin behaves differently when a gene is active (“on”), where it’s more spread out, versus inactive (“off”), where it’s more compact. This gives us deeper insight into gene regulation.
We also learned that how scientists process experimental data can change their interpretation of chromatin structure — an important insight for improving future experiments.
Our study offers a novel computational approach to uncover the dynamics of chromatin, enhancing our understanding of gene organization and regulation.
What are the potential implications of your findings for the field and society?
For the scientific field, our method provides a new and more accurate tool to study chromatin’s 3D structure and gene regulation. It can help understand biological processes like development, disease, and aging.
For society, the findings can improve research into cancer, genetic disorders, and other gene expression-related diseases. Understanding chromatin behavior can lead to new therapies targeting gene regulation.
In the long run, this could support breakthroughs in precision medicine, tailoring treatments based on an individual’s genetic structure and chromatin organization.
What was the exciting moment during your research?
The most exciting moment was when the IBD model successfully predicted the 3D structure of the α-globin gene in different cell types. It was a crucial milestone, confirming that our method could accurately reconstruct real chromatin structures based on contact data.
Seeing the model capture real biological complexity gave me a sense of accomplishment — like we had opened a new window into the invisible world of gene architecture, with great promise for both basic science and medicine.
Reference:
Computing 3D Chromatin Configurations from Contact Probability Maps by Inverse Brownian Dynamics.
Kumari, Kiran et al.
🔗 Biophysical Journal, Volume 118, Issue 9, 2193–2208
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