Panel Presentation: Advanced Predictive Modeling in Cell Line Development with Live Q&A

Biopharmaceutical manufacturing of proteins requires an efficient cell line. Starting with several thousand clones, researchers use multiple selection rounds to ultimately chose one clone for the manufacturing. Each selection stage, which is based on different criteria, presents resource challenges for researchers developing cell lines.

One of the most important steps in Cell Line Development (CLD) is single cell cloning which involves isolation and expansion of individual monoclonal cells. This process carries the risk of inaccurate clone selection, discarding clones that would outperform others as well as the potential for poor outgrowth after the cloning step.

Automation presents the opportunity to streamline this process to provide more consistent and accurate results and overall reduce time of the workflow.

In this webinar, our speakers will discuss the recent progress in predictive modeling combining artificial intelligence (AI) and machine learning (ML). They will also present how their advanced methodology improves the probability of identifying superior clones over traditional selection strategies that rely solely on fluorescence data.

Learning Objectives: 

1. Identify the integration of artificial intelligence (AI) and machine learning (ML) to enhance predictive capabilities.

2. Summarize the critical role of single cell cloning in the cell line development process.

3. Identify how automation can improve the efficiency of high-throughput clone generation.