Biologics Software: Tools for Emerging Biotechs

Biologics software refers to a suite of digital tools designed to support scientists and organizations working on complex biologics such as monoclonal antibodies, recombinant proteins, vaccines, and cell or gene therapies. These applications help streamline data capture, sample tracking, analysis, and workflow orchestration across the discovery, development, and preclinical stages.

Biologics R&D is data-intensive, iterative, and highly collaborative—making software essential to ensure scientific rigor, traceability, and operational efficiency. Below, we explore three key categories of biologics software that help teams accelerate discovery while maintaining compliance and scientific integrity.

Electronic Lab Notebooks (ELNs) for Biologics Research

Electronic lab notebooks (ELNs) are foundational tools in biologics research. They replace paper notebooks with digital platforms designed for secure, structured experiment documentation.

Unlike basic note-taking apps, purpose-built ELNs offer advanced functionality tailored to the needs of scientific teams working in regulated or high-throughput environments.

An ELN can help with:

• Experiment Documentation: Document experiments including the methods used, the date and time of each step, the reagents used, and the results obtained.
• Protocol management: Store and share protocols for different experimental procedures, such as cell culture, purification, and characterization.
• Collaboration and data sharing: Facilitate collaboration among different research groups and enable data sharing among team members.
• Audit trail: Maintain an audit trail of all submitted and reviewed notebooks and changes to included data. 

>Learn about our ELN software

Sample Management Software for Biologics Workflows

Biologics research typically generates large volumes of samples—cells, proteins, plasmids, reagents, and derivatives. Sample management software helps labs organize and track these materials across their lifecycle.

Whether you’re screening antibody candidates or banking engineered cell lines, effective sample management is crucial for reproducibility and throughput.

This software supports biologics programs by:

• Sample inventory management: Capture the registration, storage, derivatives, and archiving of each sample. Register samples with their associated metadata such as their source, collection date, and other details.
• Organization: Organize samples by project or other criteria making it easy to find and retrieve specific samples. Track the location and storage of each sample. 
• Laboratory data management: Unify samples with their assay results and store this data in a central repository.
• Security and compliance: Sample management software can be used to ensure the security and confidentiality of samples by implementing user access controls and data encryption.
• Sample archiving: Sample management software can be used to archive samples for long-term storage, ensuring that they are accessible for future use. 
• Sample data integration: Sample management software can be used to integrate sample data with other data generated in the laboratory
  

>Learn about our Sample Management Software 

LIMS Software for Biologics R&D

A Laboratory Information Management System (LIMS) is the most comprehensive type of biologics software, supporting complex workflows, data orchestration, and quality oversight across teams and labs.

In software for antibody discovery—including antibody screening, protein expression, and cell-based assays—a LIMS acts as a central nervous system for operational and scientific processes.

LIMS software can help with:

• Sample tracking and management: Track samples from the point of collection to the final analysis, ensuring proper handling and storage.
• Data management: Manage and analyze large amounts of data generated during the antibody discovery process
• Workflow management: Streamline and automate the different steps in the antibody discovery process, such as cell culture, purification, and characterization.
• Collaboration and data sharing: Facilitate collaboration among different research groups and enable data sharing among team members.
• Quality control: Ensure that the data generated during the discovery process meets established quality control standards.
• Reporting and visualization: Generate reports and visualizations of the data generated during the discovery process, making it easier to understand and communicate results.

>Learn about Biologics LIMS for Antibody Discovery