Guest post

November 29, 2012 at 9:18 am Leave a comment

Understanding Computation in Biotechnology from Masters Programs to Public Relations

Computer science and engineering has been growing exponentially in recent years, though much has been happening more or less behind the scenes. When most people think about scientific innovations, they imagine lab workers with goggles spending tireless hours testing hypotheses with physical experiments. This still happens, but computers are playing a more and more prominent role. New algorithms and engineer-designed programs can actually help scientists make better, faster, and often less expensive discoveries. The implications are huge in both medical and agricultural sciences, but—in what is surely a rarity in today’s strapped market—there are often more opportunities than there are qualified researchers and analysts to fill them. The time has never been better for analytically-minded students to pursue advanced training in computational biotechnology.

Computational biotechnology is a relatively new field that combines traditional processes like gene isolation and cell fermentation with computer tracking and algorithmic advantages. Cutting-edge scientists have used computers for as long as the technology has been available, but most of the prior generations’ interactions were linear: a researcher would input data into a particular program, for instance, or would use databasing and amalgamating tools to sort and process data collected externally. Today, the computers themselves do a lot more of the actual guiding using codes and particular programs that scientists are designing. The relationship is much more interactive, in other words.

In many cases, this leads to a sort of inverse approach to experimentation. “Instead of systematically testing the effects of known compounds—the pharmaceutical industry’s basic model for more than a century—scientists can now investigate backward, combing through genomic data to find links between specific genotypes and diseases and then screening drug data to identify therapeutic candidates,” Science magazine reported in 2012. Computational algorithms are allowing pharmacologists to come up with new drug breakthroughs by allowing them to better understand how proteins function, how the body processes different reactions, and how new compounds can be isolated and essentially “created” from what already exists.

Getting started usually requires more than an interest in pharmacy or an understanding of advanced computer science, however. “It’s not a job for traditional computer scientists,” Russ Altman, director of Stanford University’s biomedical informatics training program, told Science. “The comfort with ambiguity and fuzziness that we introduce in our training programs and, most importantly, the biological vocabulary, mean that people with computational biology training wind up being extremely valuable to companies.”

The United States Department of Labor’s Bureau of Labor Statistics has been projecting a surge in CSE professional demand as far back as 2002; current trends show that the need is even greater now. “There is a world-wide shortage of high-quality computational biologists,” the Cambridge Computational Biology Institute said in a 2012 press release. “One of the great challenges for the field is analysing the large amounts of complex data generated, and synthesising them into useful systems-wide models of biological processes. Whether operating on a large or small scale the use of mathematical and computational methods is becoming an integral part of biological research.”

As the Institute intimated, more data does mean more jobs—but these positions often require extensive schooling, usually at least to the master’s degree level. Programs are competitive, but rarely as expensive as comparable degrees in the business and humanities sectors as graduate students are often able to secure grant money and external funding to cover their studies that do not have to be repaid. The National Institutes of Health, the National Library of Medicine, and the National Institute of General Medical Sciences are among the government entities that regularly fund graduate work and professional research in computational biotechnology; a number of private, university, and local government programs may also provide some money.

The different focus areas for graduates represent a lot of diversity, too. Pharmacology is but one of the growing areas. Little do most transplant patients know how instrumental computer technology is to the optimization of their tissue regeneration, for instance, and in many labs, scientists are using computers to map the specifics of human organs in the hopes that one day they may be able to be created completely from scratch. Computer mapping and planning is also at the heart of today’s highly specific diagnostic medical imaging. Ultrasounds that can detect minute heart defects in growing fetuses and the precise metastatic rate of cancerous and precancerous growths are two of the most breakthrough procedures that would not have been possible without a lot of background structural framework.

Tapping into these new technologies takes a lot of work, and as a result, most of the latest procedures and possibilities are incredibly expensive—some so much so that they cannot be realistically introduced to the mass market. Broadening these fields and training the right people to fill jobs will both change people’s lives for the better while making access to life-changing technology more affordable.

Many universities are also quite aggressive when it comes to matching promising students with job opportunities, though this is often something of a growing edge. Many CES companies are startups, and are often unable to invest a lot of money in recruitment and job advertising. The lack of available talent has in this respect worked against the industry. The work is there, but the money for funding and initial capitalization is not. Innovation often comes from the bottom up, as many entrepreneurs know. With only a handful of trained workers and big companies doing most of the recruiting, it can be hard for new market entrants to make a splash.

Complex computer algorithms are silently changing the way we as a society do many things. Though the most profound developments are usually found in the medical sciences sector, the continued expansion of this sort of analytical technology also has implications for things as commonplace as social networking and consumer management. The complexity underlying the field is one of the reasons why it remains something of an enigma to so many, but the field’s relatively young status is undoubtedly also a factor. As more budding scholars seek futures applying technology to standard science, the more we will be able to do—and the more we will likely be able to know.

About the Author

Sophia Foster, a blogger over at, brings some of her higher ed expertise to Canadian BioTechnologist2.0 as she discusses the growing need for computational biologists in firms across North America. This site has mentioned biotechnology education in Canada before, but Sophia’s article highlights an area of real need. The job prospects in this hybrid field are also looking to be quite good. Feel free to save this information, or pass it along to students you may know with an interest in computational analysis as a career.

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