New DNA sequences, the results of thousands of trials in fields and glasshouses worldwide, three-dimensional images of plants – the flood of data produced by research and development is currently growing faster than the methods available for analyzing it. How can these data be captured, structured, made accessible and then examined in a way that will facilitate new findings? This question was at the heart of the two-day Computational Life Science (CLS) conference held by Bayer CropScience in Monheim and attended by internal and external experts as well as colleagues from Bayer HealthCare and Bayer Technology Services.
"Bayer CropScience has clearly recognized the data challenge. As we look for integrated solutions across our technologies, we see CLS as one major element to create those inter-disciplinary cross-links,” said Adrian Percy, Head of Research & Development at Bayer CropScience. Some 85 scientists in the Computational Life Science group are currently examining this issue. The researchers hope to obtain new findings on subjects such as the development of resistances and starting points for new, safe active ingredients or for the development of new plant traits. “When large quantities of data are analyzed with computer-based and mathematical models, entirely new findings can be generated and correlations identified,” said Yves Van de Peer of Ghent University/VIB in Belgium.
"Our Computational Scientists not only enable data management and analysis, but contribute scientifically into projects, thus fostering collaboration and open-minded data sharing mentality across Research and Development,” said Christian Paulitz, Head of Computational Life Science at Bayer CropScience.
The technologies implemented can come from different areas. For example, a software program that helps astronomers detect minor differences in distant galaxies is now also being used by cancer research scientists to investigate tissues. “Technology hopping” is how an expert refers to this use of technologies from other areas. Bayer, with its comprehensive expertise in the fields of human, animal and plant health, is predestined for such interdisciplinary harnessing of technology.
Barend Mons of Leiden University in the Netherlands regards the status of analysis options as critical: “We are currently missing up on 95 percent of what would be possible.” Much information is not available, for instance, because it is not machine readable, links are no longer current and databases can't communicate with each other, he added. In today's world, data is just as important as oil – but the transport pipelines and options for preparing the new raw material are still largely missing.
The unique challenges facing researchers in the field of crop science were another subject of discussion. Participants pointed out the high level of uncertainty regarding future growing conditions and risks for harvests, as well as the fact that new varieties can only be tested under the conditions that currently prevail but remained optimistic: “Generally speaking, the potential to greatly increase yields is there.”
Between presentations by external experts, CropScience scientists, most of them from the CLS team, presented their projects. Some of these teams are examining topics such as how chemical and biological data from different sources can be made uniformly accessible or what influence molecules have on epigenetic changes in genetic material, i.e. not in the DNA itself, but how it is translated. Others are aiming to assess the potential toxic effects of molecules at as early a stage as possible. Decades worth of data collected by CropScience and HealthCare could lead to new findings in these areas.
The approximately 100 participants unanimously agreed on the pivotal importance of the new opportunities. The two existing pillars on which science is based – formation of a hypothesis and its subsequent testing in an experiment – have now been joined by a third pillar, Comutational Life Science or “e-science”. This third pillar is necessary in order to cope with the “new reality” of more complex theories, larger quantities of data and teams whose work frequently spans numerous countries.
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