7. Data mean nothing without the appropriate models. Producing huge amounts of data is one issue, being able to use them another one. To understand complex systems, quantitative data and models based largely on basic physical laws are
needed. Where complexity is too high and too many matter properties need to be considered, biology can learn tremendously from other sciences such as chemical engineering, material science and geophysics. In the end everything is about transformation of mass, energy and information. Due to the historically based low affinity of biology to mathematics and physics, there may be many obstacles on the way, but on any field of plant sciences efforts should be taken to develop
quantitative models and to interpret results on the background of quantitative systems analysis. Having data of large numbers of real plants available on your
hard drive is the best basis to provide important facts about phenotypic parameters
and variabilities, thus modelling plants completely in silico for a better 6
understanding and prediction of reaction patterns to genetic or environmental
conditions.