Chemistry


by Robert L. Stevenson


Evolving Software Enables Smarter Workflows: An Interview With Gene Tetreault of BIOVIA


he digital revolution in the laboratory began in the late 1960s, with digital integrators measuring the area of chro- matographic peaks in microvolt seconds. The next step was to use microcomputers, such as the PDP-8 from Digital Equipment Corp., to gather signals from the bank of the GCs and provide a formatted report via a teleprinter.


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Computer networks started to appear by the mid-1970s, allowing electronic reports to be routed to other computers, but usually only within the same facility. With the arrival of the Internet two decades later, report files could be sent globally. Soon after came the ability to access reports and historical data in real time with intercomputer communication. It was pre- dicted that paper would be replaced by pixels, and to a large extent this has happened.


Laboratories have responded to the need for more data to support decision-making and re- search with even faster analytics. For example, in chromatography, subminute separations are now common; various spectroscopic instruments are being used in real time. Next- generation sequencing (NGS) and results from ’omics technologies have placed more em- phasis on the handling of very large data files that may span several evolutionary changes in operating systems. Metadata has become important; and natural language processing has opened the scientific literature to smart, convenient computer searching.


The ability to describe and model workflows and processes is now supporting interacting with data from report to prediction. Advances in process analytics can provide near-real-time data that supports within-run optimization.


With this as a background, I interviewed Gene Tetreault, senior director of products and marketing for Dassault Systèmes BIOVIA (San Diego, Calif.).


RLS: We are currently at a convergence of high-throughput analytics with super-computing. How do you view this?


GT: Yes, at BIOVIA, we are impressed with the rapid growth of chemical and espe- cially biochemical information, including next-generation sequencing. It makes little sense to have big databases unless you can use the data. Historically, Accelrys [San Diego, Calif.] served the laboratory space and Dassault Systèmes [Vélizy-Villacoublay, France] is strong in the engineering space, especially 3D design, 3D digital mock-up and product lifecycle manage- ment. Together, we [BIOVIA is the combination of Accelrys and Dassault Systèmes] offer a com- plete package that covers discovery to product.


RLS: To me, data visualization is a weak point in the utility of large databases. What is coming?


GT: Yes, you have a point. The combination and associations of data types is limitless these days. With the correct context such as location, timing [and] control parameters, to name a few, powerful heat maps or spider graphs will pro- vide new insight. But we need visualization tools that are compatible with more dimensions.


RLS: Process monitoring is a traditional role for analytics. Please tell us more about adding predictive capability for optimizing a particular run.


GT: Okay, let’s look at a couple of examples.


Example 1: With a proper correlation of empiri- cal data from prior situations, we can imagine that process monitoring provides critical varia- tions automatically without having to program the scenarios ahead of time.


Example 2: Another possible use of this data is to tune the process dynamically to provide the desired outcome such as the yield or the amount of critical material consumed in the process.


AMERICAN LABORATORY • 40 • AUGUST 2015


I think that these show the power of the confluence of high-speed analytics with super-computing.


RLS: Can we expect similar advances in the clinic?


GT: This is happening now with NGS. The ana- lytics is currently ahead of the computing, but this will change very quickly.


RLS: What is your view of the new human interfaces such as Google Glass (Google, Mountain View, Calif.) and iWatch (Apple Inc., Cupertino, Calif.)? What impact do you foresee, particularly in the laboratory space?


GT: Wearable devices and the Internet of Things (IoT) will increase the amount of available data. Additionally, these devices will seamlessly combine the physical world display with data overlays enhancing what a scientist will see, e.g., a scientist can look at a sample or a device and instantly see additional information, imme- diately augmenting their [sic] understanding of the situation. For example, wearable devices could present a quick display of trend lines and control charts. Instead of having to look at run history, they could see how this run compares to the prior runs.


RLS: How will instrument design re- spond to changes in computing and communication?


GT: Instruments will automatically connect to the Internet and their data will be made available to vendors for diagnostics or to the business for better utilization. This connection will also provide seamless integration with the scientist as they [sic] are performing experi- ments and tests.


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