The term ‘Systems Biology’ can mean many things to different people (Aderem, 2005; Kirschner, 2005). However, it is generally agreed that one of the central aims of systems biology is to understand biological processes in terms of the dynamic interactions between the components that constitute the system. Importantly, this aim is not unique to systems of biomolecules, but can apply at many different spatial and temporal scales (Aderem, 2005; Trewavas, 2006). For example, the dynamic behaviour of individual cells depends on the operation of genetic regulatory networks, while large-scale features of crop systems (such as yield and sustainability) depend on interactions between the individual plants and environmental factors (Yin & Struik, 2007). The insight that drives systems biology is that a full understanding of the role played by any one component in a biological process can be achieved only by considering it in its appropriate context in the whole system. In this sense, systems biology goes beyond a strict reductionist paradigm, in which the properties of system components are considered in isolation.
‘A key prerequisite for the systems methodology is the ability to assay over time the state of as many network components as possible.’
Despite the obvious diversity in the details of systems-level processes and their underlying components, understanding the mechanisms by which interactions between components generate the behaviours of the whole process relies on a number of steps in common: first, it is necessary to determine the identity and nature of the system components that play a significant role in generating the behaviour under study (a ‘parts list’ of the system); second, the network of interactions between these components must be mapped out, and their natures determined; and, finally, it is necessary to use this information to forge an understanding of how the dynamics of the system emerge from the underlying interaction network. Taken together, these three steps provide an outline ‘systems methodology’ that can be applied to systems spanning the range of biological scales. While the techniques required to achieve each stage may be different for different types of system, their integration into a coherent methodology provides a well-defined approach to tackling the difficult question of how systems-level behaviour emerges from component interactions.
The recent upsurge in interest in systems biology stems primarily from technological advances in molecular biology that have dramatically increased the speed with which it is possible to complete the first two steps, namely collating a molecular ‘parts list’ and mapping out a network of interactions (Barabási & Oltvai, 2004). High-throughput transcript and protein profiling, together with interaction screens, such as large-scale yeast-two-hybrid and ChIP-on-chip, now allow large protein and transcription interaction networks to be constructed with relative ease (reviewed in Monk, 2003; Zhu et al., 2007). While it may have become easier to generate large networks, this alone does not provide mechanistic insight into the properties of the intact system under study. Network diagrams provide only a static picture of potential interactions, while it is the dynamics of the network state that govern the behaviour of the system. A key prerequisite for systems methodology is the ability to assay, over time, the state of as many network components as possible. Given such data, statistical and mathematical analysis can be used (Monk, 2008).
An example of how microarray data can be employed to infer network components is provided by Menges et al. (this issue of New Phytologist; pp. 643–662). By combining archived transcriptome data obtained under a range of different conditions using gene ontology information, the authors find new putative components of mitogen-activated protein (MAP) kinase signal transduction networks that provide a focus for further functional studies. Such information need not be generated solely by high-throughput methodologies such as transcriptomics. Gay et al. (this issue of New Phytologist; pp. 663–674) describe the use of high-resolution reflectance spectra to monitor dynamic changes in the metabolism of chlorophyll during leaf senescence. The authors present a strong argument for modelling this pathway using a systems approach, given the extensive knowledge available about the genetic and biochemical basis of chlorophyll breakdown combined with the ability to perturb this pathway and monitor its consequences noninvasively over time. Jansson & Thomas (this issue of New Phytologist; pp. 575–579) propose that leaf senescence itself can be considered a set of modelling routines, where environmental inputs influence which modules are run, loop and interact, and ultimately determine the outputs.
Whilst systems biology naturally lends itself to model molecular to cell to organ scale processes in organisms such as Drosophila and Arabidopsis, how applicable is this approach to higher-scale processes (i.e. from population to ecosystem) or involving more complex organisms such as crops? Yin & Struik (this issue of New Phytologist; pp. 629–642) propose that there is a compelling case for crop systems biology, which builds on the rich history of modelling whole-crop physiology and recent advances in crop functional genomics. The authors argue that crop systems biology will play a crucial role in the understanding of complex crop phenotypes and subsequently crop improvement. Sheehy et al. (this issue of New Phytologist; pp. 579–582) discusses how one such complex trait – engineering the C4 pathway into rice – cannot be achieved without the use of genetic engineering and systems biology approaches. Nevertheless, this ‘grand challenge’ urgently awaits the identification of the genes that control the anatomical and biochemical pathways that confer the C4 trait. Bowen et al. (this issue of New Phytologist; pp. 583–587) argue that simply assembling a series of genes or genetic circuits to produce a desired trait (such as C4 rice) is unlikely to be successful without a detailed quantitative characterization of the network gained from systems biology. The authors argue that such information can be readily applied employing the new field of synthetic biology and significantly improves the chances of success of engineering new traits.
So, is systems biology really a paradigm shift beyond the idea that we need to consider context for components? Or is it largely a technology-driven acceleration of progress towards an integrative understanding of the dynamical behaviour of complex biological systems? Marcum (this issue of New Phytologist; pp. 587–589) discusses these and other related issues, employing Kuhnian philosophy. Irrespective of whether one considers this a paradigm shift or revolution, systems biology is set to move experimental approaches from a traditional reductionist approach to more holistic treatment of complex biology phenomena. Combined with advances in mathematical and computational modelling of interaction networks (Cohen, 2004; Albert, 2007; Monk, 2008), this will facilitate progress towards an integrative understanding of the dynamical behaviour of complex biological systems.