| Årstall |
2010 |
| Abstract |
Bio-scientists generate far more data than their minds can handle cognitively, and this trend will likely continue. With the aid of a small set of versatile tools for mathematical modelling and statistical assessment, bio-scientists can explore their real-world systems without experiencing data over-flow.
The paper outlines an approach for combining modern high-throughput, low-cost but non-selective bio-spectroscopy measurements with soft multivariate bio-chemometrics data-modelling, to over-view complex systems, test hypotheses and making new discoveries:
From preliminary, broad hypotheses and goals, many relevant samples are selected and measurement with respect to many informative variables. The resulting tables represent a “cacophony” of data. From these, the most relevant and reliable “underlying harmonies and rhythms” are extracted and tested statistically, displayed for interpretation and used for prediction. Outliers are detected automatically. Interesting subsets of samples can then be chosen for in-depth analyses in subsequent research cycles.
This pragmatic “top-down” approach takes advantage of developments in “soft” data-driven modelling cultures as well as in “hard” knowledge-driven cultures.
Data analytical examples show how information-rich bio-spectroscopy can be used for characterizing and quantifying known and unknown chemical constituents and physical phenomena in intact bio-samples. This is based on a combination of deductive “hard” and inductive “soft” modelling. The examples represent NIR spectra of bio-chemical mixtures, FTIR spectra of a microbiological fermentation process and FTIR analysis of fatty acids in milk for functional genomics. |
| Referanse |
Martens, H., Kohler, A. 2010. Mathematics and measurements for high-throughput quantitative biology, Biological Theory. Biological Theory, Special issue on Quantifying Biology, Vol 4 , Issue 1, pp 29-43. |
| Utgiver |
Biological Theory, Special issue on Quantifying Biology, |
Harald Martens
Achim Kohler
