Computational morphodynamics of plants: integrating development over space and time

On evaluating models in Computational Morphodynamics

Recent advances in experimental plant biology have led to an increased potential to investigate plant development at a systems level. The emerging research field of Computational Morphodynamics has the aim to lead this development by combining dynamic spatial experimental data with computational models of molecular networks, growth, and mechanics in a multicellular context. The increased number of published models may lead to a diversification of our understanding of the systems, and methods for evaluating, comparing, and sharing models are main challenges for the future. We will discuss this problem using ideas originating from physics and use recent computational models of plant development as examples.

Dynamic network-based epistasis analysis: boolean examples

In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and single-path assumption, but also by demonstrating the importance of considering temporal dynamics, and specifically introducing the usefulness of Boolean network models and also reviewing some key properties of network approaches.

A computational model of auxin and pH dynamics in a single plant cell

Directed cell-to-cell movement of the plant growth hormone auxin is often referred to as polar auxin transport, and has gained much interest since its discovery at the beginning of the 20th century, both by biologists and theoreticians. Computational modelling of auxin transport at tissue and whole plant scales has given valuable insights into the feedback dynamics between auxin and its transport, which often leads to cell polarisation. However, one cellular feedback mechanism that has been overlooked so far in previous models is the interplay between auxin and pH during auxin transport, even though this is well known from biology. We propose a kinetic model of such a feedback mechanism, linking knowledge about auxin-induced acidification of cell wall compartments to the chemiosmotic hypothesis of auxin transport. Our results suggest that proton fluxes may play a significant role in auxin transport. Since active auxin transport relies on the proton motive force over the cellular membrane, allocation of auxin is linked to its effects on compartmental pH. Our auxin/pH feedback model predicts enhanced accumulation of auxin in cells and increases in both auxin influx and efflux when this feedback is in effect. These results were robust in all simulations and consistent with biological evidence, thus providing a framework for generating and testing hypotheses of auxin-related polarisation events at a cellular level.

Salicylic Acid biosynthesis and metabolism

Salicylic acid (SA) has been shown to regulate various aspects of growth and development; it also serves as a critical signal for activating disease resistance in Arabidopsis thaliana and other plant species. This review surveys the mechanisms involved in the biosynthesis and metabolism of this critical plant hormone. While a complete biosynthetic route has yet to be established, stressed Arabidopsis appear to synthesize SA primarily via an isochorismate-utilizing pathway in the chloroplast. A distinct pathway utilizing phenylalanine as the substrate also may contribute to SA accumulation, although to a much lesser extent. Once synthesized, free SA levels can be regulated by a variety of chemical modifications. Many of these modifications inactivate SA; however, some confer novel properties that may aid in long distance SA transport or the activation of stress responses complementary to those induced by free SA. In addition, a number of factors that directly or indirectly regulate the expression of SA biosynthetic genes or that influence the rate of SA catabolism have been identified. An integrated model, encompassing current knowledge of SA metabolism in Arabidopsis, as well as the influence other plant hormones exert on SA metabolism, is presented.