On Systems Thinking, Systems Biology, and the in Silico Plant

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).

Gene interactions constrain the course of evolution of phosphine resistance in the lesser grain borer, Rhyzopertha dominica

Phosphine, a widely used fumigant for the protection of stored grain from insect pests, kills organisms indirectly by inducing oxidative stress. High levels of heritable resistance to phosphine in the insect pest of stored grain, Rhyzopertha dominica have been detected in Asia, Australia and South America. In order to understand the evolution of phosphine resistance and to isolate the responsible genes, we have undertaken genetic linkage analysis of fully sensitive (QRD14), moderately resistant (QRD369) and highly resistant (QRD569) strains of R. dominica collected in Australia. We previously determined that two loci, rph1 and rph2, confer high-level resistance on strain QRD569, which was collected in 1997. We have now confirmed that rph1 is responsible for the moderate resistance of strain QRD369, which was collected in 1990, and is shared with a highly resistant strain from the same geographical region, QRD569. In contrast, rph2 by itself confers only very weak resistance, either as a heterozygote or as a homozygote and was not discovered in the field until weak resistance (probably due to rph1) had become ubiquitous. Thus, high-level resistance against phosphine has evolved via stepwise acquisition of resistance alleles, first at rph1 and thereafter at rph2. The semi-dominance of rph2 together with the synergistic interaction between rph1 and rph2 would have led to rapid selection for homozygosity. A lack of visible fitness cost associated with alleles at either locus suggests that the resistance phenotype will persist in the field.

Simple software processes and tests improve the reliability and usefulness of a model.

Models are abstractions of reality that have predetermined limits (often not consciously thought through) on what problem domains the models can be used to explore. These limits are determined by the range of observed data used to construct and validate the model. However, it is important to remember that operating the model beyond these limits, one of the reasons for building the model in the first place, potentially brings unwanted behaviour and thus reduces the usefulness of the model. Our experience with the Agricultural Production Systems Simulator (APSIM), a farming systems model, has led us to adapt techniques from the disciplines of modelling and software development to create a model development process. This process is simple, easy to follow, and brings a much higher level of stability to the development effort, which then delivers a much more useful model. A major part of the process relies on having a range of detailed model tests (unit, simulation, sensibility, validation) that exercise a model at various levels (sub-model, model and simulation). To underline the usefulness of testing, we examine several case studies where simulated output can be compared with simple relationships. For example, output is compared with crop water use efficiency relationships gleaned from the literature to check that the model reproduces the expected function. Similarly, another case study attempts to reproduce generalised hydrological relationships found in the literature. This paper then describes a simple model development process (using version control, automated testing and differencing tools), that will enhance the reliability and usefulness of a model.