Research

Interface of Biophysics and Systems Biology 

I use multi-scale biophysical simulations to study spatiotemporal organization principles in biological systems. How biological self-organization is shaped by evolutionary pressure across scales? I have worked on problems ranging from molecular to cellular scale, including gene regulation, signaling events, metabolic control, biochemical noise regulation and network evolution.

Research Keywords: Biomolecular condensates, Phase separation, Spatial organization in Biology, Cell signaling, Bacterial metabolism and antibiotic resistance, Network evolution, Molecular-to-organismal fitness mapping.  

Research Techniques: Dynamical Systems (Kinetic models), Langevin Dynamics, Coarse-grained molecular dynamics, Rule-based Modeling (BioNetGen), Bioinformatics, Evolutionary dynamics, Deterministic and stochastic simulations, Spatial (PDEs) and non-spatial (ODEs) simulations, Network analysis. 

Biomolecular condensates and Phase separation

Formation of biomolecular condensates has emerged as a ubiquitous mechanism to organize cell biology. Biological macromolecules (proteins and nucleic acids) can assemble via reversible phase separations to create non-membrane-bound fluid compartments called condensates. These structures perform a range of biological tasks including signaling, stress response and transcriptional control. Dysregulation of condensates is implicated in several pathological states including neurodegeneration and Cancer. 

Thermodynamic theories of polymer physics are very useful in describing condensate formation. However, there are several open questions that limit our understanding about condensate structure and function. These condensates often have many molecular types with spatially heterogeneous environments inside. It is not clear how their formation is controlled with concentration and the functional relevance of internal structures. Especially, the evolutionarily tuned structural features of biomolecules that lead to the emergence of such plastic yet functional spatial compartments are not well established. Employing techniques from biophysics, systems biology and bioinformatics, my research seeks to address some of the major conundrums in the field and reveal universal rules underlying the condensate biology.

Phase separation criteria for multi-component condensates - Solubility product 

Conundrum 1: What determines the phase separation threshold of a multi-component condensate?  The lack of a fixed saturation concentration poses a conceptual bottleneck in deriving the phase transition criteria of real biological condensates, consisting of 10s to 100s of components. My PhD thesis proposed the concept of “solubility product (SP)”, inspired by the ionic equilibrium of salt solution, in describing the threshold criteria of phase transition. For a multi-component system, instead of looking at the dilute phase concentrations of individual components, a product of those concentrations can define the onset of phase separation. Papers: Chattaraj et al. 2021, Chattaraj & Loew 2023

In collaboration with the Ditlev lab, we provided in-vitro experimental evidence in favor of the SP concept. While comparing with the experiments, we also uncovered the role of soluble oligomers in shaping the SP profile. Paper: Chattaraj et al. 2024

Recently, our collaborator, Prof. Jeremy Schmit formalized the SP concept using a statistical mechanics based approach which provides a foundational step towards understanding the biophysics of multi-component condensates. 

Composition of multivalent cluster 

Clustering of multivalent biomolecules underlies the condensate formation. The effect of multivalency in a multi-component system is non-trivial. Using a complex three-component (Nephrin-Nck-NWASP) system, our simulations revealed the role of stochiometry-matching in determining the cluster composition. This is similar to the "magic-number" effect proposed in the literature. Paper: Chattaraj et al. 2019   

Size distribution of biological condensates

Conundrum 2: Biological condensates often emerge as a multi-droplet state and never coalesce into one large droplet within the experimental timespan. This peculiar phenomenon of size distributed droplets is not compatible with the predictions of equilibrium statistical mechanics. 

In my postdoc research, we first demonstrated a functional utility of the condensate size distribution in the context of actin signaling where multiple smaller condensates are collectively more efficient than fewer larger droplets. This analysis leads us to posit that the existence of size distributed condensates could be an evolutionary strategy to optimize the biological task. Paper: Chattaraj & Shakhnovich 2024

We then showed that the merging of condensates is an entropy-driven process as opposed to the surface-energy driven coalescence observed for canonical liquids. This entropy stems from the sticker exchange between the merging droplets. The causal factor underlying such process is the “sticker-spacer” architecture (Figure below) that condensate forming biopolymers conform to. Due to its single-valent nature (Fig. B), when these stickers are fully saturated within a condensate, they don’t merge with other condensates even after encountering them. Such kinetically arrested metastable configurations may represent the biological condensates that exist as smaller droplets with reduced merging tendency. Paper: Chattaraj & Shakhnovich 2025

These single-valent sticker interactions yield non-intuitive results once we alter the sticker patterning or spacer length in the polymer sequence. My ongoing projects reveal the design rules (chain valence, length and sticker pattering) that can produce condensate with a given internal structure or compositional specificity which is directly related to their biological functions. Overall, these studies collectively push our understanding towards a universal structure-function relationship of biomolecular condensates. 

Molecular grammar of Intrinsically disordered regions (IDRs) 

A class of condensate forming proteins contain intrinsically disordered regions (IDRs) in their sequence. Composition and patterning of amino acids (also known as molecular grammar) play important roles in IDR mediated phase separation. In a collaborative project with Prof. David Weitz's group, we uncovered (unpublished) a novel molecular grammar of Vimentin, a cytoskeletal protein, that contains two IDRs. 

Metabolic Network Modeling 

Antibiotic resistance is a global health crisis. Different classes of antibacterial agents operate by targeting distinct physiological processes. For instance, antifolates selectively target bacterial metabolic enzymes that disrupt the folate synthesis and prevent the bacterial growth. However, prolonged antibiotic treatment often causes evolutionary escape where the pathogen develops strategy to nullify the stress. The potential number of escape routes is too large to test by wet-lab experiments. As another direction of my postdoc research, I have been developing a kinetic model of folate metabolism in E. Coli to design novel antibiotics by identifying sensitive enzyme nodes. More broadly, modeling aims to shed light on the relationship between metabolic network topology and microbial evolvability. 

Computational tool development

I developed multiple computational tools to bridge the gap between large-scale simulations and biophysical insights. During my PhD, I created a pipeline called MolClustPy that performs graph-based simulations of multivalent protein interactions and analyze molecular clusters from multiple stochastic trials. SpringSaLaDpy is an extension of this pipeline designed for the analysis of SpringSaLaD software (spatial simulations) output. 

During my internship at the pharma company, GSK (2021), I developed a MATLAB based pipeline to create large quantitative systems pharmacology (QSP) models from smaller reaction networks. 

Recently, I have been leading an effort called CASPULE that provides a reproducible pipeline to perform Langevin Dynamics of sticker spacer polymers and perform statistical analysis on polymer condensates.