Quantitative analysis of MBW complex formation in the context of trichome patterning.

Trichome patterning in Arabidopsis is regulated by R2R3MYB, bHLH and WDR (MBW) genes. These are considered to form a trimeric MBW protein complex that promotes trichome formation. The MBW proteins are engaged in a regulatory network to select trichome cells among epidermal cells through R3MYB proteins that can move between cells and repress the MBW complex by competitive binding with the R2R3MYB to the bHLHL protein. We use quantitative pull-down assays to determine the relative dissociation constants for the protein-protein interactions of the involved genes. We find similar binding strength between the trichome promoting genes and weaker binding of the R3MYB inhibitors. We used the dissociation constants to calculate the relative percentage of all possible complex combinations and found surprisingly low fractions of those complexes that are typically considered to be relevant for the regulation events. Finally, we predict an increased robustness in patterning as a consequence of higher ordered complexes mediated by GL3 dimerization.

Mathematical Modelling in Plant Synthetic Biology.

Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.

Comparative expression analysis in three Brassicaceae species revealed compensatory changes of the underlying gene regulatory network.

Trichomes are regularly distributed on the leaves of Arabidopsis thaliana. The gene regulatory network underlying trichome patterning involves more than 15 genes. However, it is possible to explain patterning with only five components. This raises the questions about the function of the additional components and the identification of the core network. In this study, we compare the relative expression of all patterning genes in A. thaliana, A. alpina and C. hirsuta by qPCR analysis and use mathematical modelling to determine the relative importance of patterning genes. As the involved proteins exhibit evolutionary conserved differential complex formation, we reasoned that the genes belonging to the core network should exhibit similar expression ratios in different species. However, we find several striking differences of the relative expression levels. Our analysis of how the network can cope with such differences revealed relevant parameters that we use to predict the relevant molecular adaptations in the three species.

Identification of the Trichome Patterning Core Network Using Data from Weak ttg1 Alleles to Constrain the Model Space.

The regular distribution of trichomes on leaves in Arabidopsis is a well-understood model system for two-dimensional pattern formation. It involves more than 10 genes and is governed by two patterning principles, the activator-inhibitor (AI) and the activator-depletion (AD) mechanisms, though their relative contributions are unknown. The complexity of gene interactions, protein interactions, and intra- and intercellular mobility of proteins makes it very challenging to understand which aspects are relevant for pattern formation. In this study, we use global mathematical methods combined with a constraining of data to identify the structure of the underlying network. To constrain the model, we perform a genetic, cell biological, and biochemical study of weak ttg1 alleles. We find that the core of trichome patterning is a combination of AI and AD mechanisms differentiating between two pathways activating the long-range inhibitor CPC and the short-range inhibitor TRY.

Genetic and molecular analysis of trichome development in Arabis alpina.

The genetic and molecular analysis of trichome development in Arabidopsis thaliana has generated a detailed knowledge about the underlying regulatory genes and networks. However, how rapidly these mechanisms diverge during evolution is unknown. To address this problem, we used an unbiased forward genetic approach to identify most genes involved in trichome development in the related crucifer species Arabisalpina In general, we found most trichome mutant classes known in A. thaliana We identified orthologous genes of the relevant A. thaliana genes by sequence similarity and synteny and sequenced candidate genes in the A. alpina mutants. While in most cases we found a highly similar gene-phenotype relationship as known from Arabidopsis, there were also striking differences in the regulation of trichome patterning, differentiation, and morphogenesis. Our analysis of trichome patterning suggests that the formation of two classes of trichomes is regulated differentially by the homeodomain transcription factor AaGL2 Moreover, we show that overexpression of the GL3 basic helix-loop-helix transcription factor in A. alpina leads to the opposite phenotype as described in A. thaliana Mathematical modeling helps to explain how this nonintuitive behavior can be explained by different ratios of GL3 and GL1 in the two species.