A.Basic skills in integrated biology should include knowledge of chemistry, biochemistry, bioinformatics and molecular biology. I have also found it necessary to learn some physiology and immunology. You don’t have to be an expert in all, but it helps to have at least some background in each field.
Human Biological Chemistry & Genetics Department
University of Texas Medical Branch
A.Engineering skills are extremely valuable for integrated biology. The kind of mental models that engineers use to understand and design complex systems have not widely been employed in the life sciences until recently. We’re finally starting to have enough data regarding cells and organisms to make this kind of thinking very fruitful.
Vice President, ProSanos
A.Scientific domain knowledge, statistics, chemometrics, and programming language (especially Matlab).
Senior Scientist, Smiths Detection
A.Integrated biology demands unification of biology. The most crucial skill boils down to basic collaborative communication.
Since biology is inherently a competitive process, communication concerning informatics projects is often hampered by the desire to create the next “killer app.” But, as evident by larger genome-based informatics projects, scientist can work in conjunction to solve the most difficult problems in biological information management.
These projects exemplify the underlying requisite of communication and competition that will take integrated biology into the future.
Joseph Murray, Informatics Specialist
A.If we’re talking about making scientific progress in an inherently interdisciplinary field, the most important thing is not skill but attitude.
People from a predominantly life sciences background are going to need to see their idiosyncratic, detail-oriented world from the formal perspective instilled in those trained in mathematics, computer science, physics, or philosophy.
People from formal backgrounds will need extensive hands-on experience in empirical lab or field work to appreciate the true complexity of organic systems, which they often tend to abstract out of their models.
Eric Minch, Senior Research Fellow
Applied Computer Science and Mathematics
Merck Research Laboratories
A.Keeping up on current trends and technology, and learning computer programming skills such as SQL, Perl, Java. With more information available every day and the requirement to be knowledgable across disciplines, using automated Web tools to search and filter papers, books, and internet content is crucial to efficiently select the most useful materials to read.
London Regional Genomics Centre
Robarts Research Institute
A.The key skills involve acquiring and maintaining skills in scientific inference, including statistical and causal modeling, involving large arrays of data. This includes understanding the problems in dealing with laboratory variability, multiple inference, data reduction, pattern searching, and robust inference. Systems biology needs to provide the framework for understanding these large sets of data to avoid the “astrology” problem: finding more patterns than are really there.
Timothy R. Church
University of Minnesota School of Public Health
Here’s the question for our July/AUGUST issue:
Will off-target silencing prove too great a challenge to the future success of RNAi? Why or why not?
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