The Challenges of Software Engineering in the Life Sciences

The Challenges of Software Engineering in the Life Sciences

Table of Contents

By Jane Cook
September 16, 2021

There is a huge knowledge gap between bench biologists and software developers. Many research scientists lack the basic computing knowledge to communicate effectively with software developers. Similarly, many software developers are not extensively trained in biological research.

Life Sciences Software Engineering

This gap leads to many software development challenges unique to the life sciences – and this is in addition to the normal demands of custom software development.

One hurdle lies in the fact that often when software developers are brought into a life science research setting, they are expected to play unrelated roles. That’s right, they become the “hardware expert” and somehow also become basic IT support.

Even if that’s not the case, a general lack of understanding of the applications and limitations of custom software development can lead to ineffective planning and thus, project failure for the developer.

Biotech Software Developers

More specific to biotech and life sciences, the ground is effectively shifting under software developers’ feet. The nature of biological research is constant change and revision of fundamental ideas, making it extremely difficult to find a static starting point or base for software.

This constant innovation also means constant learning to stay up-to-date with the latest technologies. Every time a new sequencing method is developed, for example, software engineers have to develop a new program to interpret the different data that is produced.

Thus, the speed of grant-driven research in the life sciences favors quick solutions over solutions that can be used long-term, after the associated publication or grant is accepted.

Bridge Informatics

At Bridge Informatics, we understand and address these challenges, by providing the communication and expertise link between research scientists and software engineers.

The result is an ability to create effective tools to answer a given biological question that is professionally developed and built to last.

The line between data science and the life sciences is blending together, and collaboration between data scientists, software developers, and life science researchers will be essential for the next generation of scientific discoveries.

Jane Cook, Journalist & Content Writer, Bridge Informatics

Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].


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