Sirisha Narayanabhatla

About

Computational oncology and bioinformatics professional with 10+ years of experience in precision medicine, systems biology, multi-omics interpretation, and real-world evidence analysis. Experienced in genomic and clinical data analysis, predictive modelling, and translational research to support data-driven oncology decisions.

Experience

• Consultant
  Cellworks Research India Pvt. Ltd.
  2024 – 2026

  Performed integrative analysis of large-scale, high-dimensional genomic and multi-omics datasets to identify biomarkers, characterize tumour biology, and investigate therapeutic response patterns in oncology cohorts. Utilized genomics and cancer cell-line datasets to validate drug sensitivity and support computational oncology-driven drug discovery initiatives. Applied systems biology and computational modelling approaches to investigate molecular pathway interactions, therapeutic efficacy, and mechanisms of action of emerging cancer therapies. Investigated deregulated signalling pathways and genomic alterations associated with drug-response relationships in oncology models. Resolved simulation inconsistencies and optimized computational pathway models to improve biological accuracy and translational relevance. Mentored research team members in computational workflow optimization, mathematical modelling, and biological simulation frameworks.

• Bioinformatics Analyst
  Oncodynamix Life Sciences Pvt. Ltd.
  2020 – 2024

  Analysed NGS-derived genomic and multi-omics patient datasets to support biomarker interpretation, therapy rationalization, and precision oncology decision-making. Supported translational biomarker analytics by integrating TCGA datasets, real-world evidence (RWE), and multi-omics patient data for computational oncology applications. Developed computational approaches to associate patient-specific drug response profiles with genomically characterized cancer cell lines. Conducted NGS-based differential expression analysis on public GEO datasets to identify disease-associated RNA expression changes and maintained an integrated database linking expression profiles with drug response data. Developed a modular DSL2 RNA-seq workflow integrating FastQC, Trimmomatic, HISAT2, SAMtools, featureCounts, and MultiQC. Interpreted somatic and clinically relevant genomic variants, including variants of unknown significance (VUS), using ClinVar, cBioPortal, scientific literature, and pathway-level biological interpretation. Built and managed large-scale multi-omics databases integrating DepMap, TCGA, and CancerRx datasets to support AI-driven oncology research. Led development of computational workflows for oncology risk stratification by translating NCCN oncology guidelines into functional analytical frameworks. Delivered internal training programs on cancer signalling pathways, genomic interpretation, multi-omics analysis, and precision oncology workflows. Performed WES variant-calling workflow using Galaxy, including QC, BWA-MEM2 alignment, BAM processing, duplicate marking, variant calling, filtering, and SnpEff annotation.

• Associate Scientist R&D II
  Cellworks Research India Pvt. Ltd.
  2014 – 2019

  Developed systems biology and mathematical models to investigate drug-sensitive and drug-resistant signalling pathways associated with chemotherapy and targeted cancer therapies. Integrated literature-derived genomic and signalling pathway information into computational frameworks to correlate genomic signatures with therapeutic outcomes. Performed computational analysis of pharmacological responses in cancer cell lines by integrating phenotypic outcomes with genomic and multi-omics profiles. Interpreted patient-derived multi-omics datasets to support personalized therapy selection and translational precision oncology research. Collaborated with software engineering teams to optimize computational simulation performance through hybrid local-cloud execution strategies and contributed to scalable in silico infrastructure for translational oncology and drug discovery.

Education

• IB
  MSc

  Institute of Bioinformatics and Applied Biotechnology (IBAB)

  Bioinformatics and Biotechnology

  Bioinformatics and Biotechnology

Skills

Tools / apps / platforms

Projects

• ClinVar Variant Retrieval-Augmented Generation (RAG) Pipeline
  LangChain, ChromaDB, embeddings, LLM

  Developed a RAG pipeline to retrieve and answer questions from curated ClinVar variant records using structured documents and semantic retrieval.

• Patient-Cell Line Drug-Response Matching (Closest Cousin)
  Python, K-Nearest Neighbors

  Developed a method to identify the cancer cell line whose drug-response profile most closely matches a given patient's, using binarized IC50 calls across a shared drug panel compared via K-Nearest Neighbors with Hamming distance.

• NMD-Status Classification and Survival Analysis (TCGA-UCEC)
  Python, Cox regression

  Built a classifier integrating expression-based nonsense-mediated decay scoring, exon-position rules, copy-number status, and variant allele frequency to classify truncating mutations as NMD-triggering or NMD-escaping. Tested association with overall survival using Cox regression.

• Multi-Omics Integration and Drug Sensitivity Prediction (Afatinib, DepMap Cell Lines)
  R, Python

  Built a Lasso regression model integrating RNA expression and copy-number features across approximately 533 cancer cell lines to predict Afatinib IC50 drug sensitivity. Identified the ERBB2/HER2 amplicon as the dominant predictive signature.

📚 Publications

• Computational Modeling of Protein Networks Predicts Treatment Outcomes in Multiple Myeloma · 2017
• Azacitidine response prediction in MDS patients with NGS data using a computational biology modeling (CBM) based clinical decision support system · 2018
• OncodynamiX as an artificial intelligence (AI) based platform for precision medicine · 2024