Plant Physiology 134:909-911 (2004)
© 2004 American Society of Plant Biologists
SCIENTIFIC CORRESPONDENCE
On Systems Thinking, Systems Biology, and the in Silico Plant
Graeme L. Hammer,
Thomas R. Sinclair*,
Scott C. Chapman and
Erik van Oosterom
Agricultural Production Systems Research Unit, School of Land and Food Sciences, The University of Queensland, St. Lucia, Queensland, Australia (G.L.H., E.v.O.); Agricultural Production Systems Research Unit, Queensland Department of Primary Industries, Toowoomba, Queensland, Australia (G.L.H.); Agronomy Physiology Laboratory, United States Department of Agriculture-Agricultural Research Service, University of Florida, Gainesville, Florida (T.R.S.); and Commonwealth Scientific and Industrial Research Organization Plant Industry, St. Lucia, Queensland, Australia (S.C.C.)
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ABSTRACT
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The recent summary report of a Department of Energy Workshop on Plant Systems Biology (P.V. Minorsky [2003] Plant Physiol 132: 404–409) offered a welcomed advocacy for systems analysis as essential in understanding plant development, growth, and production. The goal of the Workshop was to consider methods for relating the results of molecular research to real-world challenges in plant production for increased food supplies, alternative energy sources, and environmental improvement. The rather surprising feature of this report, however, was that the Workshop largely overlooked the rich history of plant systems analysis extending over nearly 40 years (Sinclair and Seligman, 1996 ) that has considered exactly those challenges targeted by the Workshop. Past systems research has explored and incorporated biochemical and physiological knowledge into plant simulation models from a number of perspectives. The research has resulted in considerable understanding and insight about how to simulate plant systems and the relative contribution of various factors in influencing plant production. These past activities have contributed directly to research focused on solving the problems of increasing biomass production and crop yields. These modeling approaches are also now providing an avenue to enhance integration of molecular genetic technologies in plant improvement (Hammer et al., 2002).
We suggest that the future requires a broader view of plant systems biology than the one recommended by the Workshop so that research effectively connects emerging capabilities at the molecular and cellular level to whole-plant improvement and to performance at the crop level. This is certainly true if the objective of the systems modeling is to predict complex responses in plant behavior to meet the real-world challenges outlined by the Workshop. Lessons learned over the past decades of plant systems analysis are that exact, careful definition of the system and careful integration across scales of biological organization are absolutely critical. In fact, the progress on simulation of plant systems using biochemical and physiological information has been explored in two recent symposia in which we participated, one held in Europe in 2001 by the European Society of Agronomy (Donatelli et al., 2002) and the other held in 2000 in the United States by the Crop Science Society of America and American Society of Agronomy (Weiss, 2003). Papers presented at these symposia explored the opportunities and methods for systems analysis and modeling to provide the link between genome scale molecular biology and plant improvement.
A system can be generally defined as a network of interacting elements receiving certain inputs and producing certain outputs. Quantitative models are generated as tools to aid understanding and prediction of system output in response to the environment and system inputs. Models are simplified representations of system dynamics, usually in mathematical form. A key challenge is defining the scale of operation within the system to be simulated. In plants, a system can be described equally at the level of gene action, biochemical pathway, an organelle, a cell, an organ, a whole plant, or a community of plants (crop or ecosystem). Defining the system boundary has proven to be an absolutely critical step in systems thinking. Orders of magnitude of complexity are introduced as we move from molecular scales to whole organisms. Although system function can be viewed and modeled at all of these scales, experience has shown that only two or three scales are desirable in effectively simulating any particular system.
The Department of Energy Workshop (Minorsky, 2003) suggested that the very core of systems biology is the goal of being able to model a living organism and that the focus should be on the structure and dynamics of gene function. Although this aligns with our notion of systems thinking, the implicit assumption in the discussion was that the molecular or genome scale provided the starting point to address real-world problems. Although we accept this as a possible starting point, we suggest it is not the only one, and probably not the best one, if the target is to design and engineer improved plants.
Although there are examples of success in the use of genome-wide strategies to understand the control systems of specific components of development that are highly conserved, for example segment polarity genes of fruitfly (Drosophila melanogaster; von Dassow et al., 2000) and endomesoderm specification in the sea urchin (Strongylocentrotus purpuratus) embryo (Davidson et al., 2002), it is much more difficult to discern the controls of overall plant growth, which is critical in crop yield formation. The difficulty in anticipating the controls of growth, i.e. mass accumulation, were highlighted in a study on yeast (Saccharomyces cerevisiae) growth response (Giaever et al., 2002) that included a nearly complete collection of gene deletion mutants (approximately 6,000 genes). It was discovered that only 7% of the genes that exhibited an increase in mRNA expression under optimal growth conditions were actually involved in decreased growth under those conditions when the genes were deleted. Ideker et al. (2001) had more success in obtaining alteration of yeast growth as a result of gene mutations by focusing on the single process of Gal utilization, but they still found a relatively high level of posttranscriptional control. We suggest that a starting point focused on structure and dynamics of organism function will likely be advantageous and can inform targeting of relevant process and component studies. We discuss this "top-down" approach further below.
A molecular "bottom-up" approach to understanding plant growth and development is, on its own, likely to flounder in complexity. Morandini and Salamini (2003) highlighted the complexities associated with distributed control of fluxes in plant metabolic pathways. They noted the difficulties this complexity engenders in predicting overall system dynamics from the enzyme level and referred to similar problems experienced by fermentation engineers, even when modeling simple yeast reactions within closely controlled artificial incubators. These difficulties suggest that a focus on higher order control may be more appropriate, especially when the goal is to understand overall plant performance. Plant growth is influenced by the interplay of the continually changing environment with complex gene networks (containing many more genes than yeast), and the functional consequences usually operate at higher levels of biological organization, i.e. organ and whole-plant development and growth. Successful adaptive strategies for an organism are related to the flow of information across levels of organization in a manner that allows the plant to best adjust to the prevailing environment. It is the organism level of organization at which selection and evolutionary fitness are effected, although the successful adaptive strategies must be contained in the genetic make-up.
Plants are complex adaptive systems. The functioning of such systems is best understood by exploring how they handle information (for discussion, see Gell-Mann, 1994). A plant acquires information about its environment and its interaction with that environment and uses that information to dictate its adaptive responses over time scales of seconds to months or years. The underlying processes (Gell-Mann's "schemata") generate consequences for the plant phenotype. These processes are encoded in networks of genes and transcription factors that control signaling and communication processes, and these in turn initiate the growth and development responses of organs. Selection pressures will ultimately identify the mix of genetic schemata giving rise to the most superior phenotype. However, the genetic schemata likely contain numerous, redundant networks. They have become increasingly complex as modifications have accumulated through evolution via chance modifications of existing networks. This notion of evolution as tinkering suggests that selective pressures operating on the organism led to retention of useful, rather than purposeful, biochemical, and molecular changes (for discussion, see Jacob, 1977). As a consequence, many insights at the genome scale are likely to be uninformative at the scale of organism growth, as found in yeast by Giaever et al. (2002). Hence, in relation to plant systems biology, it makes sense to think much more about function at the organism scale as the entry point for considering consequences on processes at lower levels of biological organization, i.e. a "top-down" approach.
Significant endeavors in the field of whole-plant modeling are now being directed at understanding genetic regulation and aiding crop improvement (for review, see Hammer et al., 2002; for examples, see Cooper et al., 2002; Chapman et al., 2003). Crop models with generic approaches to underlying physiological processes and software structure to support scientific investigation (Wang et al., 2002) provide a means to link phenotype and genotype—the in silico or virtual plant. This approach has utilized a "top-down" approach to dissect the physiological basis of adaptive traits and their control at the whole-plant level and, thus, provide the understanding to support predictive modeling. The models must quantify the functional controls that drive plant adaptive responses. This is akin to "modeling plant hormone action without modeling the hormones" (de Wit and Penning de Vries, 1983). It is necessary to connect across levels of biological organization. For example, Sinclair and Horie (1989) derived simple and robust relationships for key phenomena at the crop level by integrating the functional plant biological processes at the lower, explanatory organizational level. This provides the basis for the dialectic and linkage between "top-down" and "bottom-up" approaches. We suggest that this dialectic is required if new molecular capabilities and tools are to have significant impact on improvement of crop plants beyond single gene traits. Snape (2001) also has suggested that combining the "forward" and "reverse" approaches will allow us to get comprehensive knowledge of the biology of agronomic traits at the physiological, biochemical, and molecular levels so that the "circuitry" of crop plants can be elucidated.
There is evidence emerging that engaging a "top-down" approach will be critical in linking molecular genetics to plant improvement. Tardieu (2003) outlines use of an ecophysiological model of plant water use to explore the genomics of tolerance to water deficit. The model contained both physical and control equations. Parameterization of the latter represented coordinated genotypic responses that quantified a "meta-mechanism" at a higher level of organization. This could then be connected to gene regulatory networks or quantitative trait loci. Studies by Reymond et al. (2003) on responses of maize (Zea mays) leaf growth to temperature and water deficit, Leon et al. (2001) on photoperiod responses in sunflower (Helianthus annuus), and Borrell et al. (2001) on the staygreen trait in sorghum (Sorghum bicolor) have pursued physiological dissection and quantitative analysis of the phenotype as a means to predict responses of genotypes. They elucidated Tardieu's "meta-mechanisms," Gell-Mann's "schemata," or Snape's "circuitry" in a manner that facilitates linkage to more detailed understanding of genetic regulation. These studies offer approaches for crossing scales of biological organization and illustrate the potential for spanning the gap between genome scale molecular biology and functional control at the organism scale.
Katagiri (2003) discussed in the same issue of Plant Physiology as the Workshop report a "top-down" approach and the need to link "top-down" and "bottom-up" approaches. However, his "top" was recognizing coherent patterns in microarray-based gene expression profiling. This approach still remains a long way from the plant level. Perhaps, though, this can still provide an initial point of linkage between the worlds of molecular physiologists and whole-plant physiologists. This possibility is reinforced by studies on genetic controls and biochemical signals operating over short time periods within hypocotyls (Nemhauser et al., 2003) or young plants (Thum et al., 2003). Groups of coregulated genes reflect Gell-Mann's "schemata," Tardieu's "meta-mechanisms," or Snape's "circuitry." We propose that developing models that mimic plant functional biology is the key to identifying these critical control factors. With this type of quantitative understanding of control at the organ or organism level, experimenters would be able to better target and simplify their large combinatorial experiments. Hence, we suggest that supporting and investing in the dialectic across biological levels of organization is of far greater importance to progress in crop improvement than suggestions arising from the Department of Energy Workshop (Minorsky, 2003) on enhancing cross-disciplinary activities within the confines of plant molecular biology. Only by understanding the links across all biological levels will the "in silico plant" have a real chance of germinating and flourishing in support of the design of improved plants to meet real-world challenges.
Received October 15, 2003;
returned for revision October 29, 2003;
accepted December 7, 2003.
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FOOTNOTES
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http://www.plantphysiol.org/cgi/doi/10.1104/pp.103.034827.
* Corresponding author; e-mail trsincl{at}ifas.ufl.edu; fax 352–392–6139.
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ABSTRACT
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