This presentation surveys how Flagship Pioneering’s portfolio companies use cloud technologies to scale AI/ML models. We’ll examine architecture patterns, strategies, and lessons learned, showcasing methods for scaling compute and pipelines. Additionally, the talk will highlight emerging trends in cloud and AI/ML, offering insights into how these innovations are shaping the future of biotech research. Attendees will gain practical strategies for optimizing cloud environments to drive AI/ML growth in biotech.

This presentation will detail challenges and solutions in implementing large-scale human genetics to support sequence-based precision medicines like RNAi therapeutics. Key topics will include data management, cost effectiveness, and navigating ever more stringent data security standards. The talk will highlight innovative strategies and technologies that Alnylam put in place that paved the way for the development and delivery of novel RNAi therapeutics to patients in need. Human genetics is at the heart of drug discovery at Alnylam. Effective, large scale human genetics is a data management and computational challenge. The loss of trust and the growth of human genetics projects as data.

Flexible Architecture for Machine Learning for Genomics: This talk will explore the architecture for developing, deploying, and scaling machine learning models in genomics. It will emphasize the importance of creating a flexible infrastructure that can accommodate a range of ML workloads for drug development. Key topics will cover optimizing model training, managing datasets, and efficiently utilizing compute resources to meet different workload demands while ensuring cost-effectiveness and scalability.

From the design of embarrassingly parallel compute for High Content Image Analysis, to management of large clinical data sets, to emerging therapeutic modalities, the demand to design performant architectures at scale while not breaking the bank is an essential skill for any organization looking to leverage the benefits of cloud technologies. In this talk, we explore strategies and techniques to keep costs down while balancing demanding user requirements and tight budgets.

In today's landscape, system architectures are frequently developed in partnership with external providers, with the software hosted in the cloud by the chosen vendor. Responsibilities for support, maintenance, and customization are often distributed among the user, the provider, and occasionally a third-party support service. Running a system in an external cloud environment presents both organizational and technical challenges. This presentation will explore how these challenges can be effectively addressed, using the example of an ELN software.

In this talk, we will show how image analysis can be taken from research level software to high quality and production ready systems. Attendees will learn how Jackson Laboratory transitioned research-level software into production-ready systems for scalable and durable image analysis. This presentation will feature biological image data, offer insights into new paradigms for cloud-based analysis, and appeal to those interested in scaling analysis of any type using distributed programming and cloud technologies.

The collaborative efforts within Takeda, supported by external vendors, led to the development and prototyping of One Research Digital—an ecosystem comprised of FAIR data lakes. During the design phase, we adhered to the FAIR principles and utilized a data lake infrastructure with a flexible data storage schema as the system's cornerstone. These advancements enable us to enhance AI-augmented analysis, modeling, and reporting across preclinical and translational sciences.

Reaction-based virtual libraries have expanded our access to chemical spaces of billions of virtual molecules. Searching such spaces for molecules of interest can be performed in many ways, but they can usually be parallelized at multiple levels. We describe a virtual screening infrastructure that uses Nextflow to parallelize a Genetic Algorithm Virtual Screening (GAVS) coupled to computational chemistry primitives, such as shape matching and docking, using AWS Lambda. Finally, we present some benchmark results highlighting the efficiency and accuracy of the method, along with the performance gains achieved by virtue of cloud-based parallelization.

This presentation showcases the construction of a serverless data pipeline for biotech start-ups. Using Apache Airflow on Google Cloud Composer, it automates data transfers between Egnyte, CDD Vault, and BigQuery. Highlighting CDD Vault's robust API and data model, the talk demonstrates how this solution enhances data management, accelerates insights, and supports innovation, offering a scalable and cost-efficient approach for biotech organizations.

As the world's largest publicly available repository of raw sequence data, the Sequence Read Archive (SRA) plays a pivotal role in advancing public health and life sciences research. This presentation highlights state-of-the-art tools and strategies for managing and analyzing the SRA’s massive datasets, showcasing its impact on infectious disease surveillance, genomic epidemiology, and precision medicine. Discover how innovative informatics solutions are transforming raw data into actionable insights for global health challenges.

With AI taking center stage in coding, research teams are leveraging AI-enabled IDEs to refactor legacy systems into modern, efficient languages. This talk presents a balanced view of generative AI-supported coding, cutting through the hype to focus on real-world applications. Discover practical strategies and examples of how AI is transforming outdated research systems into scalable, maintainable, and future-ready platforms for life sciences.

Since 2010, “Trends from the Trenches” has been a cornerstone of the Bio-IT program, delivering candid and occasionally blunt assessments of the most impactful and overhyped IT technologies in life sciences. This talk will provide a deep dive into computing, storage, cloud, data science, machine learning, and more, with a focus on supporting data-intensive science. Looking ahead, this talk will share forward-thinking predictions about emerging technologies and trends poised to shape the future of life sciences innovation, offering actionable insights for navigating the next wave of IT evolution.