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, Indian Institute of Technology 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.
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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 of chromatin, 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.
How did you go about solving this problem? To solve this problem, we developed a computational method called Inverse Brownian Dynamics (IBD) combined with a polymer model. First, we took experimental data on how often different parts of the chromatin came into contact, which is known as contact probability. Our challenge was to figure out the exact 3D arrangement of the chromatin that would lead to these observed contact probabilities.
We treated this as an “inverse problem,” where we worked backward from the contact data to calculate the interaction strengths between different chromatin segments. By using a bead-spring chain model, we treated chromatin as a chain of connected beads and optimized the interaction forces between them to match the measured contact probabilities.
Our method didn’t assume a direct relationship between spatial distance and contact probability as researched have been assuming, which is a limitation in many existing models. By including hydrodynamic interactions, we were also able to simulate the dynamics of the chromatin structure. This helped us predict how chromatin behaves in different cell types and gene states, giving us insights into gene activation or silencing.
How would you explain your research outcomes (Key findings) to the non-scientific community? In simple terms, our research focused on understanding how the genetic material in our cells, called chromatin, is folded and organized inside the cell’s nucleus. This organization is important because it affects how genes are turned on or off.
We developed a new method to predict the 3D shape of chromatin using experimental data that shows how often different parts of the chromatin come into contact with each other. Our approach was different from previous methods because it didn’t assume any simple rule about how distance between chromatin parts affects their contact. Instead, we calculated the strengths of interactions between different parts to match the contact data.
One of the key findings was that chromatin behaves differently in various states. For example, in certain cell types where a gene is “on” and active, the chromatin is more spread out, while in other types where the gene is “off,” the chromatin is more compact. These results can help us understand gene regulation and how the structure of chromatin affects gene expression.
Our work also showed that different ways of processing experimental data can lead to different interpretations of the chromatin structure. This insight could help scientists design better experiments and understand gene behavior more accurately in the future.
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? The implications of our findings are significant for both the scientific field and society. In terms of research, our work provides a new, more accurate way to study the 3D structure of chromatin, which is crucial for understanding gene regulation. By better understanding how genes are switched on or off based on chromatin structure, we can gain insights into various biological processes, including development, disease progression, and cellular aging.
For society, these findings have the potential to improve medical research, particularly in areas like cancer, genetic disorders, and other diseases linked to gene expression. Understanding how chromatin changes can lead to diseases could help develop new therapies or treatments that target these changes. Additionally, our methods can be applied to study different cell types or disease conditions, providing more personalized approaches to healthcare.
In the future, the techniques and knowledge gained from this research could lead to breakthroughs in precision medicine, where treatments are tailored to individuals based on their unique genetic makeup and chromatin organization.
What was the exciting moment during your research? The most exciting moment during my research was when the inverse Brownian dynamics (IBD) model successfully predicted the 3D organization of the α-globin gene in different cell types. This was a crucial milestone because it showed that our method could accurately capture the complex structure of chromatin based on experimental data. The excitement came from seeing how our computational model, which was designed to solve a difficult problem, could predict real-world biological structures. It felt like we had unlocked a new way to understand the dynamic and intricate organization of genes inside cells, which has significant implications for both basic science and medical research.
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
