Stephen M. Welch

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanitys projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

Genetic and physiological bases for phenological responses to current and predicted climates.

We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems—both natural and agricultural—could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology.

Tapping unsustainable groundwater stores for agricultural production in the High Plains Aquifer of Kansas, projections to 2110

Significance Society faces the multifaceted crossroads dilemma of sustainably balancing today’s livelihood with future resource needs. Currently, agriculture is tapping the High Plains Aquifer beyond natural replenishment rates to grow irrigated crops and livestock that augment global food stocks, and science-based information is needed to guide choices. We present new methods to project trends in groundwater pumping and irrigated corn and cattle production. Although production declines are inevitable, scenario analysis substantiates the impacts of increasing near-term water savings, which would extend the usable lifetime of the aquifer, increase net production, and generate a less dramatic production decline. Groundwater provides a reliable tap to sustain agricultural production, yet persistent aquifer depletion threatens future sustainability. The High Plains Aquifer supplies 30% of the nation’s irrigated groundwater, and the Kansas portion supports the congressional district with the highest market value for agriculture in the nation. We project groundwater declines to assess when the study area might run out of water, and comprehensively forecast the impacts of reduced pumping on corn and cattle production. So far, 30% of the groundwater has been pumped and another 39% will be depleted over the next 50 y given existing trends. Recharge supplies 15% of current pumping and would take an average of 500–1,300 y to completely refill a depleted aquifer. Significant declines in the region’s pumping rates will occur over the next 15–20 y given current trends, yet irrigated agricultural production might increase through 2040 because of projected increases in water use efficiencies in corn production. Water use reductions of 20% today would cut agricultural production to the levels of 15–20 y ago, the time of peak agricultural production would extend to the 2070s, and production beyond 2070 would significantly exceed that projected without reduced pumping. Scenarios evaluate incremental reductions of current pumping by 20–80%, the latter rate approaching natural recharge. Findings substantiate that saving more water today would result in increased net production due to projected future increases in crop water use efficiencies. Society has an opportunity now to make changes with tremendous implications for future sustainability and livability.

Paths to Selection on Life History Loci in Different Natural Environments Across the Native Range of Arabidopsis thaliana

Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment‐specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selection on life history QTL across climatic regions, we grew a panel of interconnected recombinant inbred lines (RILs) of Arabidopsis thaliana in four field sites across its native European range. For each environment, we mapped QTL for growth, reproductive timing and development. Several QTL were pleiotropic across environments, three colocalizing with known functional polymorphisms in flowering time genes (CRY2, FRI and MAF2‐5), but major QTL differed across field sites, showing conditional neutrality. We used structural equation models to trace selection paths from QTL to lifetime fitness in each environment. Only three QTL directly affected fruit number, measuring fitness. Most QTL had an indirect effect on fitness through their effect on bolting time or leaf length. Influence of life history traits on fitness differed dramatically across sites, resulting in different patterns of selection on reproductive timing and underlying QTL. In two oceanic field sites with high prereproductive mortality, QTL alleles contributing to early reproduction resulted in greater fruit production, conferring selective advantage, whereas alleles contributing to later reproduction resulted in larger size and higher fitness in a continental site. This demonstrates how environmental variation leads to change in both QTL effect sizes and direction of selection on traits, justifying the persistence of allelic polymorphism at life history QTL across the species range.

A Multiobjective Evolutionary-Simplex Hybrid Approach for the Optimization of Differential Equation Models of Gene Networks

This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder-Mead simplex-based local search. The proposed GA is shown to perform better than NSGA-II and SPEA-2 on standard benchmarks, as well as for the optimization of a genetic model for flowering time control in rice. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks. The hybrid version also compares well with ParEGO on a few other benchmarks. The proposed hybrid algorithm is then applied to estimate the parameters of an elaborate gene network model of flowering time control in Arabidopsis. Overall solution quality is quite good by biological standards. Tradeoffs are discussed between accuracy in gene activity levels versus in the plant traits that they influence. These tradeoffs suggest that data mining the Pareto front may be useful in bioinformatics.

Flowering time control: gene network modelling and the link to quantitative genetics

Flowering is a key stage in plant development that initiates grain production and is vulnerable to stress. The genes controlling flowering time in the model plant Arabidopsis thaliana are reviewed. Interactions between these genes have been described previously by qualitative network diagrams. We mathematically relate environmentally dependent transcription, RNA processing, translation, and protein–protein interaction rates to resultant phenotypes. We have developed models (reported elsewhere) based on these concepts that simulate flowering times for novel A. thaliana genotype–environment combinations. Here we draw 12 contrasts between genetic network (GN) models of this type and quantitative genetics (QG), showing that both have equal contributions to make to an ideal theory. Physiological dominance and additivity are examined as emergent properties in the context of feed-forwards networks, an instance of which is the signal-integration portion of the A. thaliana flowering time network. Additivity is seen to be a complex, multi-gene property with contributions from mass balance in transcript production, the feed-forwards structure itself, and downstream promoter reaction thermodynamics. Higher level emergent properties are exemplified by critical short daylength (CSDL), which we relate to gene expression dynamics in rice (Oryza sativa). Next to be discussed are synergies between QG and GN relating to the quantitative trait locus (QTL) mapping of model coefficients. This suggests a new verification test useful in GN model development and in identifying needed updates to existing crop models. Finally, the utility of simple models is evinced by 80 years of QG theory and mathematical ecology.

An augmented Arabidopsis phenology model reveals seasonal temperature control of flowering time

• In this study, we used a combination of theoretical (models) and experimental (field data) approaches to investigate the interaction between light and temperature signalling in the control of Arabidopsis flowering. • We utilised our recently published phenology model that describes the flowering time of Arabidopsis grown under a range of field conditions. We first examined the ability of the model to predict the flowering time of field plantings at different sites and seasons in light of the specific meteorological conditions that pertained. • Our analysis suggested that the synchrony of temperature and light cycles is important in promoting floral initiation. New features were incorporated into the model that improved its predictive accuracy across seasons. Using both laboratory and field data, our study has revealed an important seasonal effect of night temperatures on flowering time. Further model adjustments to describe phytochrome (phy) mutants supported our findings and implicated phyB in the temporal gating of temperature-induced flowering. • Our study suggests that different molecular pathways interact and predominate in natural environments that change seasonally. Temperature effects are mediated largely during the photoperiod during spring/summer (long days) but, as days shorten in the autumn, night temperatures become increasingly important.

Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models

Hybrid algorithms that combine genetic algorithms with the Nelder-Mead simplex algorithm have been effective in solving certain optimization problems. In this article, we apply a similar technique to estimate the parameters of a gene regulatory network for flowering time control in rice. The algorithm minimizes the difference between the model behavior and real world data. Because of the nature of the data, a multi-objective approach is necessary. The concept of fuzzy dominance is introduced, and a multi-objective simplex algorithm based on this concept is proposed as a part of the hybrid approach. Results suggest that the proposed method performs well in estimating the model parameters.

Adding Local Search to Particle Swarm Optimization

Particle swarm optimization is a stochastic algorithm for optimizing continuous functions. It uses a population of particles that follow trajectories through the search space towards good optima. This paper proposes adding a local search component to PSO to improve its convergence speed. Two possible methods are discussed. The first adds a term containing estimated gradient information to the velocity of each particle. The second explicitly incorporates the Nelder-Mead algorithm, a known local search technique, within PSO. The suggested methods have been applied to the problem of estimating parameters of a gene network model. Results indicate the effectiveness of the proposed strategies.

Multi-objective hybrid PSO using µ-fuzzy dominance

This paper describes a PSO-Nelder Mead Simplex hybrid multi-objective optimization algorithm based on a numerical metric called µ -fuzzy dominance. Within each iteration of this approach, in addition to the position and velocity update of each particle using PSO, the k-means algorithm is applied to divide the population into smaller sized clusters. The Nelder-Mead simplex algorithm is used separately within each cluster for added local search. The proposed algorithm is shown to perform better than MOPSO on several test problems as well as for the optimization of a genetic model for flowering time control in Arabidopsis. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks.