Systems biology or the biology of systems: routes to reducing hunger
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Each day passes with 854 million people hungry and, for that reason, the United Nations Millennium Declaration committed the world's nations to ‘eradicate extreme poverty and hunger’. Nonetheless, developed nations are both reducing their investments in agricultural research and turning their remaining research investments away from productivity gains (Pardey et al., 2006). The elite rice cultivars, which dominate the food supplies of the millions of poor people in Asia, have approached a yield barrier (Kropff et al., 1994; Sheehy, 2001; Sheehy et al., 2007a), and the gains made from the Green Revolution technologies (centred on canopy architecture and crop nutrition) have been fully exploited (Dawe, 2007). During the coming century, climate change will probably result in more extreme variations in weather and may cause adverse shifts in the world's existing climatic patterns, further disadvantaging the poor (Agarwal & Narain, 1991). Water scarcity will grow; and the increasing demand for biofuels will result in competition between grain for fuel and grain for food, resulting in price increases (Cassman & Liska, 2007). In the face of the above problems, an increase in rice yields of > 50% will be required by 2050 to keep pace with population growth in Asia (Mitchell & Sheehy, 2006).
‘Modern’ systems biology is loosely associated with the use of genomic technologies to understand specific biological processes, although ecologists and physiologists have been using a systems approach to model crops for many years (Gutierrez et al., 2005). A weakness of genetic engineering approaches (bottom-up) to crop improvement is that changes at the molecular level can be dissipated when scaled up through biochemical and physiological levels to the response of crops in the field (Sinclair et al., 2004). In this article, we address the following two issues:
- • can the top-down approaches of systems modelling identify a broad solution to the problem of increasing yields?
- • can the ‘modern’ concepts of systems biology (bottom-up) identify the details of the solution at the molecular level?
Yield: plasticity, plant community and environmental variables
Here we describe briefly the factors that must be considered in a systems approach to yield improvement. The phenotype of a given genotype can vary markedly according to its interaction with the environment (Miflin, 2000). Such plasticity in plants is probably associated with their ability to succeed despite changes in weather, climate, competition for resources and soil types. In order to increase yields, plants growing in communities have to convert more solar energy into chemical energy or use the absorbed energy more efficiently in the synthesis of biomass or grain. Even something as simple as the spacing between plants can markedly alter their morphology and functionality. Crop communities are crowded neighborhoods in which leaves and stems compete for light. Full light absorption by a crop canopy is set by the leaf area per unit ground area and its angular distribution; the angular distribution also has consequences for the diurnal pattern of light interception. That pattern determines the maximum amount of radiation absorbed per unit area of a leaf, the time of day when that peak absorption occurs and the photochemical consequences of that pattern. At full light interception, the size of individual leaves is proportional to the tiller or plant number per unit ground area, and specific leaf area is determined by competition for light. Leaf photosynthesis and specific leaf area can be linearly related (Pearce et al., 1969).
Heat plays a role in the efficiency with which chemical energy can be accumulated; in part it determines the length of the growing season and the rate at which panicles develop. The same daily quantity of solar energy can be delivered to a crop in both temperate and tropical environments. This is because long days in temperate environments often have less intense solar irradiance than in tropical environments, which have short days. However, the temperatures in those environments are usually very different. The interplay of temperature and irradiance results in growing seasons of different lengths in temperate and tropical environments, although the radiation-use efficiencies are the same. The radiation-use efficiency is the slope of the relationship between cumulative above-ground biomass and cumulative photosynthetically active radiation intercepted by the crop. Usually, irrigation ensures that water is not a limiting factor, but rainfall varies in intensity, duration and frequency in the growing season so the availability of water is a complex problem. Fertilizers are applied to ensure maximum yields, but the demand from the crop varies throughout crop development as does the quantity of fertilizer available at any given instant. In rice, more than half of the nitrogen (N) in the grain comes from the vegetative parts of the plant, although halfway through grain filling, N is diverted from grain to ratoon tillers (Sheehy et al., 2004). The availability of resources and their rate of capture have a huge influence on yield.
Whenever the issue of yield increases is discussed, at some point the relative importance of source strength vs sink capacity arises. Work by Sheehy et al. (2001) showed that the sink capacity in rice was greatly in excess of that actually utilized, even at high yield, suggesting that the yield barrier was the consequence of source limitations. Experiments in which increased concentrations of CO2 were made available to rice resulted in increased yields (Yoshida, 1973; Ziska et al., 1997), suggesting that improvements in photosynthesis might have a role to play in increasing yield.
In well-managed crops, in which the fraction of grain per unit of biomass has been maximized, future yield improvements must be accompanied by increases in radiation-use efficiency. Mitchell et al. (1998) showed that C4 crops had radiation-use efficiencies that were 50% greater than C3 crops and that radiation-use efficiency was a function of photosynthesis. This led to the suggestion that rice photosynthesis would have to be converted from the C3 to the C4 syndrome to achieve yield increases of 50%. Sheehy et al. (2007b) went some way to confirming this conclusion when they reported that rice and maize crops grown without limitations of water or nutrients at the International Rice Research Institute (IRRI) in the dry season of 2006 yielded 8.3 tonne ha−1 13.9 tonne ha−1 respectively. Furthermore, although C4 plants display plasticity (Sage & McKown, 2006), their C4 nature is not lost during plastic responses to the environment. The attraction of the full C4 system is not only the high productivity and yield, but also the better use made of water and N. No non-C4 solution offers this complete package of benefits.
What is a system and what does it mean for a crop scientist?
A system can be defined as a number of interacting elements existing within a boundary that is surrounded by an environment. A system could be a cell, a plant, a crop, an ecosystem or a factory; quantitative descriptions of those systems are called models. Consideration of most problems often leads to a quantitative approach and then calculations to describe what is happening or what might happen given different circumstances. The principal function of systems analysis is to understand and quantify the relationship between the inputs and outputs of materials. The analytical procedures appropriate for systems analysis are often reductionist: they are designed to analyse the individual parts of the system. Once the parts have been described, quantitatively, an integrated description of the system can be produced. In the absence of redundancy, a change to any part of the system affects the performance of the whole. The crop scientist looks at systems biology as the most coherent method of describing and understanding a complex system. The crop modeller quickly recognizes many interacting components, within and between organisms, and the hierarchical nature of the system (for example, genes–transcriptome–biochemistry–cells–organs–plants–crops). Empirical models seek to describe the system as simply as possible, whereas mechanistic models look for understanding and generality. Mechanistic models offer understanding only at one level in the hierarchy, being empirical at lower levels for the sake of making progress at the higher level of prime interest. Mechanistic models of crops tend to be based on empirical descriptions of how organs work in relation to environmental and management variables. A particular difficulty is describing mechanisms controlling assimilate partitioning between organs.
Biological systems depend on control mechanisms, although they are often ill understood at a mechanistic level. If there are n interacting elements, there are n(n−1) possible interactions or routes for information exchange. If information literally flows in one direction from one element to another in a simple system containing four elements, twelve channels (actions and reactions) are required to carry the information necessary to coordinate the activities of the elements within the system. Not surprisingly, at a cellular level this rule is likely to result in a very large number of signaling pathways.
Building a mechanistic model of a biological system at any scale is no easy feat, and a hypothesis, mathematics and substantial amounts of information are required (Thornley & France, 2007). Perhaps ‘modern’ systems biology is such a young branch of science that the measuring technologies have overwhelmed scientists’ ability to make quantitative models describing the way that cells work at a molecular level. In this context it is important to note that crop modelling often involves building caricatures of systems with the most important and critical features included and the fine details ignored. Crop models are often quasi-mechanistic, using factors such as radiation-use efficiency as if they were universal constants that summarize the physiological behaviour of a crop (Mitchell et al., 1998). Common to both crop systems and ‘modern’ systems approaches is the possibility that emergent properties will be found (i.e. aspects of the behaviour of the system that could not be predicted from knowledge of the individual components). Owing to the complexity inherent in both approaches, marrying them in a single coherent model of crop yield remains a distant prospect. However, that does not preclude a working and profitable partnership between the top-down and bottom-up approaches.
Can ‘modern’ systems biology solve crop production problems?
It would seem as if the goal for plant systems biology is to describe the functioning of cells, tissues and the entire plant through molecular analysis and mathematical modelling of physical and chemical interactions between components of living plants and cells. It is an ambitious goal that will take considerable time to realize. Nonetheless, Nelson et al. (2007) suggested that a systems approach is designed to be broad and unbiased, to permit the discovery of ‘emergent’ properties that might not be revealed in hypothesis-driven experimentation that is targeted at specific genes, proteins, activities, or metabolites. By evaluating all components of the system when it is perturbed, computational approaches are able to infer networks of relationships that can then be tested. With the rice genome completely sequenced and with constantly improving annotation, Nelson et al. (2007) suggests that it now makes sense to build ‘-omics’ data sets from developing cells that will permit this computational approach to discovery. New techniques such as laser microdissection of cell types and microarray profiling may provide the comprehensive data needed for such a systems approach. Despite the current optimism, it is not yet possible to know whether ‘modern’ systems biology will play a significant role in solving global food problems in the next few decades. Nonetheless, the work of Nelson et al. (2007) is an extremely exciting approach to understanding the control of leaf development at the molecular level.
Identifying and manipulating genes responsible for important traits
The techniques of genetic engineering enable genes from sexually incompatible species to be used to create transgenic crops. This development has led to progress in hypothesis-driven plant improvements. Thus far, success has generally come from inserting a single gene for increasing the tolerance to environmental pressures such as submersion, resistance to pests and diseases, as well as tolerance to herbicides. However, attempts have been made to engineer novel multigene pathways to increase photosynthesis in leaves (Suzuki et al., 2006) and to recapture CO2 from photorespiration (Kebeish et al., 2007). Thus, whole suites of genes encoding desirable traits governing yield can be introduced using the same technology. Of course, the traits have to be identified and understood. Biological N fixation is such a trait, but the genetics of the symbiosis are not yet fully understood (Ladha & Reddy, 2000). To guarantee success in genetic engineering, it is important to know how the trait functions at a physiological level in individual plants and in the communities of plants that form crops. Then the genes responsible for the traits have to be identified, which often involves the generation and screening of large numbers of mutants. Given the rate of progress in sequencing technologies (Service, 2006), candidate regions in the genome will be identified by sequencing the wild type and the mutant. Then, by using bioinformatics tools to compare those sequences, the specific genes of interest can eventually be identified. To create a successful transgenic using a multigene trait, an increased understanding of the regulatory networks that control the tissue-specific expression of genes will probably be needed (Gowik & Westhoff, 2007).
How does a program of genetic engineering ensure that an introduced trait is not obscured during the plastic responses displayed when the plants are grown as a crop community? It is not infrequent to read that the expression or overexpression of a particular gene inserted in rice will increase yield by a large percentage (Xiao et al., 1998; Ku et al., 1999). However, a convincing quantitative assessment of its metabolic role in the context of a cell, an organ, a whole plant and a crop should accompany such claims (Fukayama et al., 2003; Sinclair et al., 2004). The possibility of resource rejection by higher plants (Thomas & Sadras, 2001) is often overlooked by molecular biologists, as is the concept of plasticity. Given the genetic complexity which underlies that plasticity, and that the ‘same’ crop is grown in geographically different regions with different climates, weather conditions and on different soil types with different histories of management, it is not surprising that in field experimentation precise repeatability, in the usual scientific sense, is the exception rather than the rule. As a result of this imprecision and the absence of universally acceptable theoretical models of crop growth, disagreements about what precisely determines both biomass and grain yield are commonplace. Consequently, even using systems approaches, it is no easy task to identify traits that will guarantee yield improvements.
Conclusions: a partnership
The conclusion that to make large increases in rice yield without further damaging the environment meant introducing the C4 pathway, was reached by taking a top-down view of crop performance and using simple crop systems modeling and crop experimentation. To make C4 rice a reality, the genes controlling the anatomical and biochemical networks distinguishing the C4 syndrome from the C3 syndrome must be discovered. This cannot be undertaken without the use of genetic engineering and ‘modern’ systems biology. Two approaches are being adopted: bioinformatics coupled to the identification of genes using mutagenesis; and the emergent properties of developing cells using ‘modern’ systems biology, as proposed by Nelson et al. (2007).
In hindsight, the C4 rice concept was the result of trying to solve a problem using both the top-down and bottom-up approaches. Installing C4 in rice may be difficult, but that is different from impossible and it is worth bearing in mind the statement of Jones (2000) with respect to the sequencing of the human genome: ‘It reaffirmed one of the most misunderstood facts in science; that it is possible to solve most problems by throwing money at them’.