Until relatively recently, life-science research was characterized by test tubes, Petri dishes, and centrifuges. Now, as with many industries, the life sciences are undergoing a digital transformation.
Computational science is changing laboratory design. The healthcare industries always have generated large amounts of data. What has changed is the available information technology. With the growth of cloud computing, large data sets — and the high-speed tools for analyzing them — are available increasingly to a degree not possible with traditional servers and desktop computers. For example, one researcher can sequence 18,000 human-genome base pairs and generate about 1.8 petabytes of data, the equivalent of 9,000 hard drives.
To better understand treatment efficacy, life-science organizations now can access real-world data from a host of sources: e.g., medical insurance claims, genomic research, electronic medical records, and clinical trials. With artificial intelligence and machine learning, companies can uncover insights quickly about how their therapies are affecting patients with specific characteristics.
Voluminous data also comes from classic “wet research.” Wet laboratories are wired progressively, with high-tech equipment relaying many data points about each experiment. Data analysts also use sophisticated software and mathematical modeling to perform “virtual” experiments based on existing data, thus accelerating and supporting research programs.
Through interviews with executives at 15 leading biopharmaceutical and medical device companies, JLL’s recent Journey to the Next-Generation Lab report found growing interest among them in configuring laboratories to accommodate the growth of computational science. Wet labs are shrinking to make way for more computational science and flexible space.
This evolution represents a fairly significant shift in the use of space from that in traditional R&D facilities. In the average biopharmaceutical laboratory today, just half of available space is dedicated to wet labs, with 25% for flexible, multidisciplinary laboratories and 25% to computational science offices. In the future, those three are likely to get equal amounts of space, a reflection of “big data’s” growing role.
The shift is evident at organizations such as The Research Institute of Nationwide Children’s Hospital (NCH) in Columbus, OH. Of three research facilities constructed by that institute over the past three decades, the first has no space dedicated to computational research, but the third devotes 41% to it. The total “dry” research space in the three buildings is about 27% of their total area, and such researchers now make up >35% of the institute’s faculty.
This trend is part of a larger push toward collaborative and agile R&D, as reflected in the flexible designs seen in next-generation laboratories. In fact, a new term has emerged to describe them: moist labs combining wet labs and computational space.
Laboratory facilities always have included office space, but administrative and computational offices typically were located on different floors or even in different buildings from wet labs. Now some laboratories are designed to bring wet and dry workspaces more closely together. As these environments become more datacentric, laboratory designers need to develop floor plans that incorporate expanded areas for computer-based labs staffed with data analysts and researchers while maintaining proximity to traditional wet-lab functions.
The shift in research modes not only influences the design and layout of R&D spaces, but it also affects their budgets. With more computational research becoming standard, research organizations might not need to ensure delivery of high air-exchange rates and the other costly, specialized infrastructure and operations costs associated with wet-lab environments. Instead, research organizations need to invest in technology infrastructure to support powerful computer systems, electronic laboratory notebooks, and the like. Those investments will be offset by lower investment in specialized wet-lab fixtures and furnishings (e.g., bacteria-resistant countertops).
Although computational laboratory space does not require piping for such wet-lab essentials as vacuum tubes, pneumatic supply, natural gas, other gases, and distilled water, it does require its own special design considerations. Typical needs include extensive cooling and humidity controls (because computers generate significant heat) and fire-suppression systems for high-density computing spaces. Also important are surfaces with built-in prevention of static electricity and numerous access points for electrical power and hardwired Internet connections.
In today’s environment of rapidly shifting research priorities, computational space is being factored into flexible laboratory designs with modular walls, benches, and tables. Computer hardware can be stacked on mobile carts, for instance, with adjustable (and lockable) shelving to allow for fast adjustments as needed. Some computational space could require other kinds of ventilated and secure hardware enclosures to protect equipment.
Data scientists (and the space they need for their work) are a growing part of life-science research. Forward-looking organizations are designing their next-generation laboratories for the “moist” paradigm in which wet labs and computational science converge.
Roger Humphrey is executive managing director of the life sciences group at JLL, 200 East Randolph Drive, Chicago, IL 60601; 1-908-698-1379; email@example.com. Complete findings of the cited report are online at http://link.jll.com/journey-to-the-next-gen-lab.