The computing transformation being effected by mobile computing may not be one we fully appreciate while burying our heads and thumbs in the latest cool app or game. Yet this transformation is likely the most important since the introduction of the personal computer. It’s not just about the inherent portability of smartphones and tablets. It’s that the transformation marks a complete change to the underlying platform. This stands in contrast to the last 30 years, where despite changes to the user experience, the fundamental concepts driving the technology would be familiar on the whole to late 1980s power users.
Making software available on a mobile device requires a complete code rewrite based on entirely different underlying concepts. Here are a few to consider:
- A mobile app is always a modular component rather than a monolith
- User interaction is done by touchscreen, which has very different properties compared with keyboard and mouse
- Computational resources must be presumed expensive, because battery life is trump
- Networking and communication features are core features rather than add-ons
The most popular mobile device platforms—iOS and Android—made a clean break from legacy software when it came to design. They have also set a high bar for user experience, such that users now take ease of use, trivial installation, and automatic versioning for granted. With the introduction of numerous high quality apps for almost every conceivable purpose, the number of users has expanded geometrically, and likewise has the level of satisfaction: mobile devices have become an integral part of the lifestyle of many people who regard a laptop or desktop computer as just a tool.
So why has the mobile device revolution not yet made a significant impact in the R&D branch of life sciences? Some argue that it’s difficult to adapt certain science-specific functionality, such as chemical structure drawing, to the mobile form factor. Others mention that bioinformatics and computer assisted drug design involve lengthy calculations on big datasets, tasks better suited to traditional computers. And portability and facile network communication is not necessarily a positive when it comes to handling sensitive proprietary data.
But none of these issues is insurmountable. For example, my company, Molecular Materials Informatics, has demonstrated that chemical structures can be drawn on a on a palm-sized device just as effectively as on desktop software. Big data/long calculation workflow scenarios can be handled by installing data and software on servers and, through the creative use of open-source components, protocols, algorithms, and a flexible API (application programming interface), building the connections so that apps can plug into these resources. And while security issues will always be important for enterprise app deployment, methods exist for ensuring data privacy in cloud-based environments, and the option always exists for companies to deploy services in-house.
What other issues have prevented apps from taking off in life science? I’ll give my thoughts in my next entry.