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</script>According to most commentators, systems biology is transforming the biological sciences in many ways, although it is debatable exactly at what levels and to what extent. The formalization and use of computational models, the development of high-throughput experimental techniques, the new links with other scientific domains (like physics, engineering, mathematics, or computer sciences) and the transfers of explanatory models that have resulted, and other changes have had a profound impact on how biological research is conducted and, consequently, how biology must be taught. In this chapter, I will particularly focus on what challenges and opportunities the massive use of formal models has brought to biology. Not only must biologists learn how to use new tools in order to represent and analyze their objects of study, but they also have to realize that new questions must be asked in order to reveal aspects of biological systems that remained hidden in the framework of traditional molecular biology and genetics. Systems biology is integrative and interdisciplinary but transfers of methods, models and concepts are not straightforward and they raise many difficult questions. Another problem is that the transformation of biology into a “complex science” leads to a partial loss of intuitive understanding, which can be troubling for biologists, who are used to think with the help of words and diagrams. How can biologists regain some intelligibility? I will also discuss how systems biology has already started to challenge some of the standard views about the status of biology as a science, the nature of biological explanations, the relation between different domains of biology, or the nature of living systems and their evolution. I will argue that these philosophical analyses of systems biology’s foundations must be seriously integrated in contemporary reflections on biology education.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
