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Case Study: Life Sciences

International Life Science
Database Modernisation

Scaling research through high-performance protein data management and future-ready AI architectures.

Zero
Downtime Achieved
40%
Faster Feature Delivery
Context

The Challenge

Client Overview

Our client is an international research society in the field of life science. Their members include the leading Universities and life science companies in North America, Europe, and rest of the world. The society occupies a prominent position in the sector with a vast network of scientists and researchers who collaborate through it.

Problem / The Ask

The society is the custodian of information relating to specific types of proteins and responsible for maintaining reference information for each of the different categories. There is a vast amount of information including protein structures, chemical composition, 3D models and other associated parameters. With the demands of the modern working environment, there was a critical need to modernise the system, improve accessibility, and lay the foundation for advanced Artificial Intelligence capabilities.

Execution

Our Process

01

Foundation & Replatforming

Created a reliable development, testing, staging and release environment. Replicated as-is features using Python/Django while introducing significantly better security and reliability via Digital Ocean and Docker containers.

02

Code Refactoring

Refactored the original code and introduced significant structural improvements and critical fixes. Enabled better processing and visualisation of database contents, dramatically improving overall availability and security.

03

AI & Future Capabilities

Engaged in experimental design to target potential breakthroughs using artificial intelligence techniques, laying the foundation for ML-assisted protein analysis and accelerated discovery.

Impact

Outcome & Benefits

Stability

Public-facing protein database now operates on a secure, high-availability platform with strictly zero downtime.

Agility

Introduction of Agile development methodologies has improved feature delivery speed and cadence by 40%.

Enhancement

Further work is being undertaken to massively increase performance and visualization capabilities of the core database.

Vision

Positioned the society at the cutting-edge forefront of AI and ML-enabled bioinformatics and research capabilities.

"This is a prestigious project for iGen43 as we have an opportunity to collaborate with some of the most advanced research work happening in the field of life sciences. There is a significant potential for AI and ML techniques to enhance the research process and we are excited to contribute to a global research society."

Arnab Dutta, Director – Digital & AI, iGen43

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