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).

Advanced computing for systems biology

Systems biology is based on computational modelling and simulation of large networks of interacting components. Models may be intended to capture processes, mechanisms, components and interactions at different levels of fidelity. Input data are often large and geographically disperse, and may require the computation to be moved to the data, not vice versa. In addition, complex system-level problems require collaboration across institutions and disciplines. Grid computing can offer robust, scaleable solutions for distributed data, compute and expertise. We illustrate some of the range of computational and data requirements in systems biology with three case studies: one requiring large computation but small data (orthologue mapping in comparative genomics), a second involving complex terabyte data (the Visible Cell project) and a third that is both computationally and data-intensive (simulations at multiple temporal and spatial scales). Authentication, authorisation and audit systems are currently not well scalable and may present bottlenecks for distributed collaboration particularly where outcomes may be commercialised. Challenges remain in providing lightweight standards to facilitate the penetration of robust, scalable grid-type computing into diverse user communities to meet the evolving demands of systems biology.

Inherent size constraints on prokaryote gene networks due to "accelerating" growth

Networks exhibiting accelerating growth have total link numbers growing faster than linearly with network size and either reach a limit or exhibit graduated transitions from nonstationary-to-stationary statistics and from random to scale-free to regular statistics as the network size grows. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single-celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale-free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value. (c) 2005 Elsevier GmbH. All rights reserved.

Accelerated leap methods for simulating discrete stochastic chemical kinetics

Biologists are increasingly conscious of the critical role that noise plays in cellular functions such as genetic regulation, often in connection with fluctuations in small numbers of key regulatory molecules. This has inspired the development of models that capture this fundamentally discrete and stochastic nature of cellular biology - most notably the Gillespie stochastic simulation algorithm (SSA). The SSA simulates a temporally homogeneous, discrete-state, continuous-time Markov process, and of course the corresponding probabilities and numbers of each molecular species must all remain positive. While accurately serving this purpose, the SSA can be computationally inefficient due to very small time stepping so faster approximations such as the Poisson and Binomial τ-leap methods have been suggested. This work places these leap methods in the context of numerical methods for the solution of stochastic differential equations (SDEs) driven by Poisson noise. This allows analogues of Euler-Maruyuma, Milstein and even higher order methods to be developed through the Itô-Taylor expansions as well as similar derivative-free Runge-Kutta approaches. Numerical results demonstrate that these novel methods compare favourably with existing techniques for simulating biochemical reactions by more accurately capturing crucial properties such as the mean and variance than existing methods.

Systems for assessing the effectiveness of management in protected areas

Since the mid-1990s, numerous methodologies have been developed to assess the management effectiveness of protected areas, many tailored to particular regions or habitats. Recognizing the need for a generic approach, the World Commission on Protected Areas (WCPA) developed an evaluation framework allowing specific evaluation methodologies to be designed within a consistent overall approach. Twenty-seven assessment methodologies were analyzed in relation to this framework. Two types of data were identified: quantitative data derived from monitoring and qualitative data derived from scoring by managers and stakeholders. The distinction between methodologies based on data types reflects different approaches to assessing management. Few methodologies assess all the WCPA framework elements. More useful information for adaptive management will come from addressing all six elements. The framework can be used to adapt existing methodologies or to design new, more comprehensive methodologies for evaluation, using quantitative monitoring data, qualitative scoring data, or a combination of both.

Does terrestrial epidemiology apply to marine systems?

Most of epidemiological theory has been developed for terrestrial systems, but the significance of disease in the ocean is now being recognized. However, the extent to which terrestrial epidemiology can be directly transferred to marine systems is uncertain. Many broad types of disease-causing organism occur both on land and in the sea, and it is clear that some emergent disease problems in marine environments are caused by pathogens moving from terrestrial to marine systems. However, marine systems are qualitatively different from terrestrial environments, and these differences affect the application of modelling and management approaches that have been developed for terrestrial systems. Phyla and body plans are more diverse in marine environments and marine organisms have different life histories and probably different disease transmission modes than many of their terrestrial counterparts. Marine populations are typically more open than terrestrial ones, with the potential for long-distance dispersal of larvae. Potentially, this might enable unusually rapid propagation of epidemics in marine systems, and there are several examples of this. Taken together, these differences will require the development of new approaches to modelling and control of infectious disease in the ocean.

Lipid, sugar and liposaccharide based delivery systems 2

The vast majority of biologically active compounds will never be considered as potential drugs due to inherently poor bioavailability. This review discusses the progress in the development of chemical systems to improve the metabolic stability, absorption and physicochemical properties of potential drugs. Delivery systems that involve the conjugation of lipid and/or sugar moieties are highlighted, as well as novel methods of conjugation of these groups to drugs. The use of sugar molecules to target drugs to particular organs or cells is also discussed, as is the use of lipids in the growing area of gene delivery. This is an update of a previous review [1].

Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits-phenology, osmotic adjustment, transpiration efficiency, stay-green-and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.

Animal visual systems and the evolution of color patterns: Sensory processing illuminates signal evolution

Animal color pattern phenotypes evolve rapidly. What influences their evolution? Because color patterns are used in communication, selection for signal efficacy, relative to the intended receiver's visual system, may explain and predict the direction of evolution. We investigated this in bowerbirds, whose color patterns consist of plumage, bower structure, and ornaments and whose visual displays are presented under predictable visual conditions. We used data on avian vision, environmental conditions, color pattern properties, and an estimate of the bowerbird phylogeny to test hypotheses about evolutionary effects of visual processing. Different components of the color pattern evolve differently. Plumage sexual dimorphism increased and then decreased, while overall (plumage plus bower) visual contrast increased. The use of bowers allows relative crypsis of the bird but increased efficacy of the signal as a whole. Ornaments do not elaborate existing plumage features but instead are innovations (new color schemes) that increase signal efficacy. Isolation between species could be facilitated by plumage but not ornaments, because we observed character displacement only in plumage. Bowerbird color pattern evolution is at least partially predictable from the function of the visual system and from knowledge of different functions of different components of the color patterns. This provides clues to how more constrained visual signaling systems may evolve.

Studies on the membrane interactions of the cyclotides kalata B1 and kalata B6 on model membrane systems by surface plasmon resonance

In this study we have demonstrated the interactions of kalata B1 and its naturally occurring analogue kalata B6 with five model lipid membranes and have analyzed the binding kinetics using surface plasmon resonance. Two kalata peptides showed a higher affinity for the phosphatidylethanolamine-containing membranes, indicating that the peptides would bind selectively to bacterial membranes. Also we have optimized the procedure for the immobilization of five liposome mixtures and have shown that the procedure provides reproducible levels of immobilized liposomes and could be used to screen the selective binding of putative antimicrobial peptides to model mammalian or microbial phospholipid membranes. (C) 2004 Elsevier Inc. All rights reserved.

Genetic manipulation of blood group carbohydrates alters development and pathfinding of primary sensory axons of the olfactory systems

Primary sensory neurons in the vertebrate olfactory systems are characterised by the differential expression of distinct cell surface carbohydrates. We show here that the histo-blood group H carbohydrate is expressed by primary sensory neurons in both the main and accessory olfactory systems while the blood group A carbohydrate is expressed by a subset of vomeronasal neurons in the developing accessory olfactory system. We have used both loss-of-function and gain-of-function approaches to manipulate expression of these carbohydrates in the olfactory system. In null mutant mice lacking the alpha(1,2)fucosyltransferase FUT1, the absence of blood group H carbohydrate resulted in the delayed maturation of the glomerular layer of the main olfactory bulb. In addition, ubiquitous expression of blood group A on olfactory axons in gain-of-function transgenic mice caused mis-routing of axons in the glomerular layer of the main olfactory bulb and led to exuberant growth of vomeronasal axons in the accessory olfactory bulb. These results provide in vivo evidence for a role of specific cell surface carbohydrates during development of the olfactory nerve pathways. (c) 2006 Elsevier Inc. All rights reserved.

Regulation of bone biology by prostaglandin endoperoxide H synthases (PGHS): A rose by any other name...

It is well established that prostaglandins are essential mediators of bone resorption and formation. In the early 1990s, it was discovered that enzymatic reactions producing prostaglandins were regulated by two cyclooxygenase enzymes, one producing prostaglandins constitutively in tissues like the stomach, prostaglandin endoperoxide H synthase-1 (PGHS-1 or COX-1), and another induced by mitogens or inflammatory mediators (PGHS-2 or COX-2). This neat distinction has not been maintained because both enzymes act in different cell systems to provide physiological signaling, constitutively or by induction under certain conditions. For example, the regulation patterns of PGHS-1 and PGHS-2 are distinct, but the evidence shows that PGHS-2 functions constitutively in the skeleton. PGHS-2 hits quickly been established, therefore, as a key regulator of bone biology, capable of rapid and transient expression in bone cells, and mediating osteoclastogenesis, mechanotransduction, bone formation and fracture repair. The goal of this review is to Summarize the current state of our knowledge of PGHS regulation of bone metabolism and to identify some of the key unresolved challenges and questions that require further study. (c) 2006 Elsevier Ltd. All rights reserved.

Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.