Over the last decade, there has been a debate over building tools that are GUI or CLI first. We think that debate is no longer relevant.
AI is giving natural language tools to assay folks to automate lab solution with minimal dependence on computational folks. The debate between wet lab and dry lab is irrelevant now. This is because we can enable wet lab scientists to work more efficiently with their programming colleagues.
Chat bots are not Generative AI. Gen AI's true power in science is its ability to integrate lab systems and automate workflows. This integration and automation make lab tooling more efficient.
Over the last decade, the rapid evolution of high throughput screening (HTS) and next-generation sequencing (NGS) technologies has significantly advanced wet lab research capabilities. However, this technological progress has inadvertently widened the gap between wet lab and dry lab environments.
Wet lab is for hands-on experiments, while dry lab is for analyzing data using computers. The division is due to the different focuses of each domain. Specialists in assays and computational analysis work separately, causing a problem that slows down lab workflow management.
It is almost like wet lab folks operate on planet earth while dry lab folks have crossed the earth space barrier. This gap has widened with orthogonal tooling for the two respective domains.
To build a lab workflow system, we need to solve this barrier. Assay researchers manage their experiment runs, and rely on dry lab to conduct downstream analysis. This creates a back and forth handshake making lab automation software a nightmare.
Some of the challenges that are most common in laboratory workflow automation are:
Technicality between wet lab vs dry lab: Wet lab experts plan experiments, prepare samples, and conduct physical tests. They are skilled at working with chemicals and seeing how things change. On the other hand, dry lab professionals focus on analyzing data, using computers, and making models.
Wet lab experts excel at planning experiments, preparing samples, and conducting physical tests. They are highly skilled in handling chemicals and observing changes.
On the contrary, dry lab professionals specialize in analyzing data, utilizing computers, and creating models. They play a crucial role in comprehending the vast amount of data generated by specific tests. However, their skill set differs from that of wet lab workers.
The handshake problem occurs when two groups have different languages and goals, making communication and data transfer hard. Wet lab researchers may lack the technical equipment to understand the nuances of data analysis software or computational requirements. Similarly, dry lab analysts might lack the context of experimental conditions and nuances that could impact data interpretation. Lack of shared understanding hinders lab workflow automation and improvement, as communication is a challenge for both sides.
Lab Integration Software and Workflow Automation: While lab integration software and test lab workflow automation tools promise to bridge this gap, the inherent differences in operation and objectives between wet and dry labs create barriers to their effective implementation. Wet lab researchers often view these tools as overly technical and not tailored to their hands-on, experimental workflow. However, dry lab experts may feel that these tools don't have enough computational depth for complex data analysis.
The division causes problems in lab processes. It also causes problems in project timelines and data transfer between wet and dry labs. These problems lead to inefficiencies, delays, and increased errors. The need for constant clarification, verification, and adjustment adds layers of complexity to lab workflow management, undermining the potential for seamless lab workflow optimization.
But that was the last decade.
The biggest impact of Generative AI and LLMs in lifesciences will be bridging the gap between dry lab vs wet lab. We can now ship the wet lab folks from earth (experiments technicality) to space (analysis and data technicality). Both the domains can operate in one island rather than two disparate islands with multiple handshakes.
Scispot is collaborating with labs in North Carolina and San Francisco that are using Scispot's AI Assist features. AI is just another tooling, however the power comes from bridging the gap between wetlab research and lab results.
The time has come to now build an integrated laboratory system with automation solutions. Wet lab workers can save time by using simple language to run scripts in their sample management process. Computational bio experts can collaborate with non-technical scientists by sharing data through APIs and natural language.
Scispot is extremely excited about this future. We enhanced our product by adding advanced AI features that go beyond a regular chat bot, reducing friction. The AI assist helps scientists in their workflow connecting natural language with Scispot's API and python scripts.
Let's combine the wet lab and dry lab, and simplify procedures, lab management, R&D, and clinical lab workflow by reducing variables. One day maybe one day we can use natural language to get into space. Till then, let's solve the lab workflow integration.
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