Shraddha Puntambekar

Bioinformatics Scientist

Enabling data-driven drug discovery through bioinformatics, multi-omics, and scientific workflows

Scientific Research

Selected academic research projects from my postdoctoral work and Ph.D., framed as concise case studies so you can scan each in under a minute, in the same format as the Industry Projects page.

GATA3 Regulatory Biology

Institution: CSIR-National Chemical Laboratory · Role: Project Scientist II · Period: 2023 – Mar 2024

Problem

The transcription factor GATA3 is a major regulator of luminal breast cancer, but its DNA-binding behavior across the diverse GATA motif landscape, and how that binding translates into regulatory signaling was not well characterized.

Approach

  • Evaluated and benchmarked ChIP-seq tools for alignment, peak detection, and de novo motif analysis of GATA3 binding sites.
  • Built ATAC-seq and RNA-seq pipelines for differential chromatin-accessibility and expression analysis.
  • Integrated motif, binding, and expression signals to characterize the regulatory landscape of GATA3 in luminal breast cancer.

Outcome

Technologies

ChIP-seq, ATAC-seq, RNA-seq, de novo motif analysis, Regulatory network analysis

Pan-Cancer Analysis of Novel Open Reading Frames (nORFs)

Institution: Indian Institute of Science Education and Research, Pune (IISER-Pune) · Role: Postdoctoral Researcher · Period: 2018 – 2019

Problem

Transcripts encoding novel open reading frames (nORFs): short, previously uncharacterized peptides, were emerging as a potential class of cancer biomarkers and therapeutic targets, but they had not been systematically characterized across cancer types or assessed for clinical utility.

Approach

  • Built a proteogenomic pipeline integrating publicly available RNA-seq and Ribo-seq data across multiple cancer types.
  • Performed functional annotation, structure prediction, disease-mutation mapping, pan-cancer survival analysis, and in-silico drug screening to assess therapeutic and diagnostic potential of nORF-encoded peptides.

Outcome

Technologies

RNA-seq, Ribo-seq, Proteogenomic pipelines, Protein structure prediction, In-silico drug screening, Pan-cancer survival analysis

Comparative Transcriptomics of Cichlid Speciation

Institution: Indian Institute of Science Education and Research, Pune (IISER-Pune) · Role: Postdoctoral Researcher · Period: 2018 – 2019

Problem

Cichlid fishes are a textbook example of rapid speciation, but the genomic substrate that supports that speciation, particularly the role of de novo coding regions and accelerated evolution, was not well mapped using state-of-the-art comparative transcriptomics.

Approach

  • Benchmarked state-of-the-art read alignment and assembly tools; built an optimized de novo + reference-based comparative-transcriptomics pipeline for two cichlid species.
  • Performed phyloP-based evolutionary rate analysis to identify accelerated regions in the cichlid genome.

Outcome

Technologies

RNA-seq · de novo and reference-based assembly · phyloP · Comparative genomics · Evolutionary rate analysis

Mechanistic Modeling of Skin Pigmentation

Institution: CSIR-National Chemical Laboratory (Biosystems Analysis Lab) · Role: Doctoral Researcher (Ph.D.) · Period: 2011 – 2017

Problem

Clinical and experimental observations in skin pigmentation — including how IFN-γ regulates melanosome maturation, needed mechanistic explanations grounded in signaling-network dynamics, in collaboration with three experimental labs.

Approach

  • Manually curated and reconstructed signaling and metabolic networks for melanosome transport and melanogenesis from primary literature.
  • Built Boolean and ODE-based mathematical models to test hypotheses and propose mechanistic explanations for the experimental observations.
  • Ran parameter estimation and sensitivity analyses to identify high-leverage nodes in the network.

Outcome

  • Findings published as co-author in Natarajan et al., Proceedings of the National Academy of Sciences (PNAS) (2014) — “IFN-γ signaling maintains skin pigmentation homeostasis through regulation of melanosome maturation.”
  • Doctoral-coursework literature review of mathematical models in skin pigmentation: PDF, presentation.

Technologies

Boolean modeling, ODE modeling, MATLAB, Parameter estimation, Sensitivity analysis, Network reconstruction, Cross-functional collaboration with wet labs

Mathematical Modeling of Sumoylation

Institution: CSIR-National Chemical Laboratory (Biosystems Analysis Lab) · Role: Doctoral Researcher (Ph.D.) · Period: 2011 – 2017

Reaction network of the SUMO conjugation system modeled in the paper, showing SUMO preprocessing by SENP, E1-mediated activation, E2-mediated conjugation and ligation, autosumoylation of E2, and SENP-mediated deconjugation. Reaction network of the SUMO conjugation system modeled in the paper. Adapted from Puntambekar et al., JBC (2016).

Problem

Sumoylation is a major post-translational modification system, but how SUMO modification levels respond to perturbations in enzyme activity — and which features of that response are non-obvious — required a model-driven analysis rather than purely descriptive biochemistry.

Approach

  • Curated the sumoylation signaling network from primary literature.
  • Developed an ODE-based mathematical model of SUMO conjugation dynamics.
  • Ran steady-state and sensitivity analyses to identify counter-intuitive, system-level features of the network’s response to enzyme perturbations.

Outcome

Technologies

ODE modeling · MATLAB · Steady-state analysis · Sensitivity analysis · Parameter estimation · Network reconstruction