Circadian control of abscisic acid biosynthesis and signalling pathways revealed by genome-wide analysis of LHY binding targets.

The LATE ELONGATED HYPOCOTYL (LHY) transcription factor functions as part of the oscillatory mechanism of the Arabidopsis circadian clock. This paper reports the genome-wide analysis of its binding targets and reveals a role in the control of abscisic acid (ABA) biosynthesis and downstream responses. LHY directly repressed expression of 9-cis-epoxycarotenoid dioxygenase enzymes, which catalyse the rate-limiting step of ABA biosynthesis. This suggested a mechanism for the circadian control of ABA accumulation in wild-type plants. Consistent with this hypothesis, ABA accumulated rhythmically in wild-type plants, peaking in the evening. LHY-overexpressing plants had reduced levels of ABA under drought stress, whereas loss-of-function mutants exhibited an altered rhythm of ABA accumulation. LHY also bound the promoter of multiple components of ABA signalling pathways, suggesting that it may also act to regulate responses downstream of the hormone. LHY promoted expression of ABA-responsive genes responsible for increased tolerance to drought and osmotic stress but alleviated the inhibitory effect of ABA on seed germination and plant growth. This study reveals a complex interaction between the circadian clock and ABA pathways, which is likely to make an important contribution to plant performance under drought and osmotic stress conditions.

Emerging design principles in the Arabidopsis circadian clock.

Recent experimental advances have enabled the identification of direct regulatory targets for transcription factors. Application of these techniques to the circadian regulatory network in Arabidopsis has uncovered a number of discrepancies within established models as well as novel regulatory interactions. This review integrates these new findings and discusses the functional implications of the revised transcriptional network for the oscillatory mechanism of the clock.

The genotype-phenotype relationship in multicellular pattern-generating models--the neglected role of pattern descriptors.

BACKGROUND: A deep understanding of what causes the phenotypic variation arising from biological patterning processes, cannot be claimed before we are able to recreate this variation by mathematical models capable of generating genotype-phenotype maps in a causally cohesive way. However, the concept of pattern in a multicellular context implies that what matters is not the state of every single cell, but certain emergent qualities of the total cell aggregate. Thus, in order to set up a genotype-phenotype map in such a spatiotemporal pattern setting one is actually forced to establish new pattern descriptors and derive their relations to parameters of the original model. A pattern descriptor is a variable that describes and quantifies a certain qualitative feature of the pattern, for example the degree to which certain macroscopic structures are present. There is today no general procedure for how to relate a set of patterns and their characteristic features to the functional relationships, parameter values and initial values of an original pattern-generating model. Here we present a new, generic approach for explorative analysis of complex patterning models which focuses on the essential pattern features and their relations to the model parameters. The approach is illustrated on an existing model for Delta-Notch lateral inhibition over a two-dimensional lattice. RESULTS: By combining computer simulations according to a succession of statistical experimental designs, computer graphics, automatic image analysis, human sensory descriptive analysis and multivariate data modelling, we derive a pattern descriptor model of those macroscopic, emergent aspects of the patterns that we consider of interest. The pattern descriptor model relates the values of the new, dedicated pattern descriptors to the parameter values of the original model, for example by predicting the parameter values leading to particular patterns, and provides insights that would have been hard to obtain by traditional methods. CONCLUSION: The results suggest that our approach may qualify as a general procedure for how to discover and relate relevant features and characteristics of emergent patterns to the functional relationships, parameter values and initial values of an underlying pattern-generating mathematical model.

Analysis of gene regulatory network models with graded and binary transcriptional responses.

The steep sigmoid framework developed by Plahte and Kjøglum [Plahte, E., Kjøglum, S., 2005. Analysis and generic properties of gene regulatory networks with graded response functions. Phys. D 201, 150-176, doi:10.1016/j.physd.2004.11.014] provides a uniform description of gene regulatory networks in which there may be both graded and binary transcriptional responses, as well as a method for analysing the models developed. Here we extend this framework. We show that there is a relation between the location of steady states and the feedback structure of a system, thus generalising existing results for Boolean type models. In addition, we justify underlying assumptions and generic features of the modelling framework in terms of biology and generalise the overall approach to take into account that each transcription factor only regulates one gene at a given threshold. By this assumption, the analysis of the models are greatly simplified.

Priming nonlinear searches for pathway identification.

BACKGROUND: Dense time series of metabolite concentrations or of the expression patterns of proteins may be available in the near future as a result of the rapid development of novel, high-throughput experimental techniques. Such time series implicitly contain valuable information about the connectivity and regulatory structure of the underlying metabolic or proteomic networks. The extraction of this information is a challenging task because it usually requires nonlinear estimation methods that involve iterative search algorithms. Priming these algorithms with high-quality initial guesses can greatly accelerate the search process. In this article, we propose to obtain such guesses by preprocessing the temporal profile data and fitting them preliminarily by multivariate linear regression. RESULTS: The results of a small-scale analysis indicate that the regression coefficients reflect the connectivity of the network quite well. Using the mathematical modeling framework of Biochemical Systems Theory (BST), we also show that the regression coefficients may be translated into constraints on the parameter values of the nonlinear BST model, thereby reducing the parameter search space considerably. CONCLUSION: The proposed method provides a good approach for obtaining a preliminary network structure from dense time series. This will be more valuable as the systems become larger, because preprocessing and effective priming can significantly limit the search space of parameters defining the network connectivity, thereby facilitating the nonlinear estimation task.