Data Management Analytics

SUPRIYA KAMASAMUDRAM SAIKUMAR

425-***-**** ********@***.*** LinkedIn

EDUCATION

Master of Science in Computational Life Sciences - Arizona State University, Tempe, AZ, USA Courses - Computing for Research, PopHealth Data Management & Analysis, Functional Genomics Aug 2024 - Present

Bachelor of Engineering in Biotechnology Engineering - RV College of Engineering, Bengaluru, India Aug 2017 - May 2021 Courses - Bioinformatics, Systems Biology, Basics of Computer Applications, HPC and Big Data Analytics GPA: 3.68/ 4.0 EXPERIENCE

R & D Associate, Unilever R&D, India July 2021 - Feb 2024 Inception of in silico microbiology first

● Build genome scale metabolic models and community models for microorganisms relevant in home care context and validate the same by performing wet lab experiments. Most of the results are in concordance with the predictions made.

● Analysing and understanding the pathways present in different microorganisms and their respective functions to tackle problems faced in every household with respect to home and laundry microbiome. Microbiome data analysis

● Analysing shotgun, whole genome, 16s and ITS data for microbes relevant in home and laundry context with interkingdom analysis.

● Worked on building a basic R Shiny app to differentiate microbes and classify them as good or bad in the home context which has an accuracy of 87%.

QSAR and Molecular Docking Studies

● Laundry malodor studies by targeting particular metabolic pathways in microorganisms responsible for causing malodour in laundry.

● Targeting particular enzymes and proteins to stop/reduce the reactions and validate the predictions by setting up malodor assays in the lab. Wet lab results are in concordance with the predictions made.

● The structures of the enzymes were predicted using AlphaFold. These structures were not available in the public databases Bioinformatics Intern, Dextrose Technologies Pvt Ltd, India June 2020 - July 2020

● Design and development of novel biomarkers for dental and oral microflora which have given successful results when PCRs were run to test the predictions.

Intern, SyMetric Systems Pvt Ltd, India March 2021 - May 2021

● Clinical Data Management using SyMetric C6 Tool which included CRF designing using C# and Java. TECHNICAL SKILLS

• Programming Languages: R, Matlab, Python, C, Unix

• Bioinformatics, Computational Biology, Systems Biology, Biostatistics ACADEMIC PROJECTS

Developing a QSAR model to isolate the bioactive potentials of the molecules of the medicinal plant (Ledum Palustre) for the treatment of Rheumatoid Arthritis

• Exploring alternative medicine as a potential therapeutic intervention in the treatment of Rheumatoid Arthritis using QSAR model and performing molecular docking to identify the most potent bioactive compound.

• Flt1 receptor from the VEGF pathway involved in Rheumatoid Arthritis was considered for the study.

• QSAR model was built using Random Forest Regressor to predict the pIC50 values of the metabolites present in Ledum Palustre.

• Molecular docking studies gave an insight into the protein ligand interactions, hence elucidating a possible mechanism of action between the receptors and identified metabolites from Ledum Palustre.

• Viridiflorene was identified as one of the potent drug targets against Flt1 which showed better interactions compared to the known inhibitors and has less side effects compared to the known inhibitors. PUBLICATIONS

• International Journal of Current Advanced Research. May 28, 2021 - Brief Insight into 3D Food Printing, Functional Ingredients required, Types of Different 3D Food Printing Technologies, Brief Insight into 3D Food Printing, Functional Ingredients required, Types of Different 3D Food Printing Technologies [http://dx.doi.org/10.24327/ijcar.2021.24283.4814]

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