By Alcina A. Rodrigues
Precision medicine is advancing healthcare by tailoring treatment strategies based on an individual’s genetic makeup, environment, and lifestyle. By moving away from a traditional one-size-fits-all approach, this field of medicine aims to improve therapy effectiveness, reduce adverse side effects, and ultimately lower healthcare costs.
Rutgers iJOBS recently hosted a seminar on this rapidly evolving area, featuring Dr. Saurabh Laddha, a Principal Computational Scientist at Johnson & Johnson (J&J). He shared insights into his academic and professional journey, his role in precision medicine, and advice for current graduate students interested in computational biology.
From Bench Biologist to Computational Scientist:
Dr. Laddha began his scientific training in India, where he completed a bachelor’s degree in biotechnology. Inspired by the technological momentum created by the Human Genome Project, he pursued a Master of Science with a concentration in biotechnology and bioinformatics. During this time, he completed a 6-month internship at the Council of Scientific and Industrial Research (CSIR) genomic laboratory, gaining foundational experience in genomic data analysis.
A pivotal moment in his journey occurred when he presented his research at the Human Genome Organization (HUGO) meeting in Dubai. There, he met Dr. Chang Chan, a faculty member at the Rutgers Cancer Institute of New Jersey, who invited him to join his lab. As a doctoral student in Dr. Chan’s lab, his research focused on understanding molecular features of neuroendocrine tumors, particularly pancreatic neuroendocrine tumors (PanNETs). After completing his PhD, he briefly continued as a postdoctoral researcher in the same lab before receiving an opportunity to join J&J as a computational scientist.
Working at J&J Innovative Medicine:
At J&J, Dr. Laddha contributes to early-stage drug discovery by working closely with biologists, clinicians, and data scientists. The company's therapeutic efforts focus on oncology, immunology, neuroscience, and cardiopulmonary diseases, with each area supported by both discovery and translational computational teams. The discovery teamidentifies promising biological targets and investigates the molecular mechanisms underlying diseases, collaborating with biologists to evaluate and validate these targets in preclinical models. The Translational team focuses on developing patient selection strategies, discovering biomarkers, and analyzing clinical trial readouts.
Dr. Laddha’s team specifically works to identify common pathways across autoimmune diseases. They generate large amounts of molecular data from patients and build a robust computational pipeline to identify pathways. A key part of his role involves integrating publicly available and internal single-cell datasets to create comprehensive cellular atlases, distinguish healthy versus pathogenic cell states, and pinpoint cell type-specific pathways that may serve as therapeutic targets.
How can PhD students best prepare for a career in precision medicine?
While PhD training provides many skills that are transferable across various fields, Dr. Laddha emphasized key steps to further bolster one’s preparation in applying for precision medicine. These steps include:
- Summer internships and industry post-docs at major biotech and pharmaceutical companies. You can find more information about internships and full-time positions at J&J at https://www.careers.jnj.com/en/student-opportunities/internships/.
- Workshops, coding bootcamps, and hackathons to strengthen computational skills.
- Staying abreast of current literature and technology.
- Learn to explain complex problems and analysis.
- Networking and informational interviews to build meaningful professional connections.
Q&A highlights:
- What skills should PhD students focus on?
Develop coding skills in R and Python, learn the logic behind computational methods and analysis, not just syntax. - How can someone without a computational background/student doing wet lab get started?
Begin by framing a clear biological question and then explore how computational tools might help to answer it. Books, e.g., “R for biologists,” can be helpful. Look for opportunities to incorporate computational analysis into your current project- many datasets generated in the lab can be analyzed independently with basic coding skills.
Dr. Laddha’s seminar highlighted the interdisciplinary nature of precision medicine and the growing importance of computational biology in drug discovery. His journey emphasized how curiosity, adaptability, and continuous learning can open new career paths.
This article was edited by Senior Editor Joycelyn Radeny.