Plant organs, including leaves and roots, develop by means of a multilevel cross talk between gene regulation, patterned cell division and cell expansion, and tissue mechanics. The multilevel regulatory mechanisms complicate classic molecular genetics or functional genomics approaches to biological development, because these methodologies implicitly assume a direct relation between genes and traits at the level of the whole plant or organ. Instead, understanding gene function requires insight into the roles of gene products in regulatory networks, the conditions of gene expression, etc. This interplay is impossible to understand intuitively. Mathematical and computer modeling allows researchers to design new hypotheses and produce experimentally testable insights. However, the required mathematics and programming experience makes modeling poorly accessible to experimental biologists. Problem-solving environments provide biologically intuitive in silico objects ("cells", "regulation networks") required for setting up a simulation and present those to the user in terms of familiar, biological terminology. Here, we introduce the cell-based computer modeling framework VirtualLeaf for plant tissue morphogenesis. The current version defines a set of biologically intuitive C++ objects, including cells, cell walls, and diffusing and reacting chemicals, that provide useful abstractions for building biological simulations of developmental processes. We present a step-by-step introduction to building models with VirtualLeaf, providing basic example models of leaf venation and meristem development. VirtualLeaf-based models provide a means for plant researchers to analyze the function of developmental genes in the context of the biophysics of growth and patterning. VirtualLeaf is an ongoing open-source software project (http:// virtualleaf.googlecode.com) that runs on Windows, Mac, and Linux. Plant scientists have gathered a wealth of knowledge about plant growth and plant genetics. Yet, it is still impossible to predict by which mechanisms genetic changes affect plant growth and development. To unravel how genetic information determines the mor-phogenesis of plants, analyzing and reconstructing the dynamics of the genetic regulatory networks (Gonzalez et al., 2009) is only the first step (Merks and Glazier, 2005). To mechanistically predict morphogenesis from gene function, it is essential to have a detailed understanding not only of genetics but also of (1) how genetic networks regulate cell behaviors, (2) how cell behaviors lead to tissue growth and patterning, and (3) how the tissue-level phenomena, including spatial patterns (Swarup et al., 2005; Jönsson et al., 2006; Bayer et al., 2009) and strains and stresses (Green, 1999; Hamant et al., 2008), induce responses at the molecular level. How these multilevel feedbacks between levels of organization produce biological function and form is perhaps the most central question in systems biology (Noble, 2006). Traditionally, biologists are familiar with presenting and develop...

Summary:Cell-based computational modeling and simulation are becoming invaluable tools in analyzing plant development. In a cell-based simulation model, the inputs are behaviors and dynamics of individual cells and the rules describing responses to signals from adjacent cells. The outputs are the growing tissues, shapes and cell-differentiation patterns that emerge from the local, chemical and biomechanical cell-cell interactions.Here, we present a step-by-step, practical tutorial for building cell-based simulations of plant development with VirtualLeaf, a freely available, open-source software framework for modeling plant development. We show how to build a model of a growing tissue, a reaction-diffusion system on a growing domain, and an auxin transport model. The aim of VirtualLeaf is to make computational modeling better accessible to experimental plant biologists with relatively little computational background.

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The Problem with ModelsModellers are producing more and more complex models. Unless these models are sufficiently characterised and made available to the research community their reuse will be minimal, and reproducing simulation experiments incorporating them will prove problematic. Consensus on the content and form of experiment recipes that combine models and simulations will encourage model sharing and facilitate reuse.A set of guidelines specifying the Minimum Information About a Simulation Experiment(MIASE) [1] proposes a common set of information necessary to reproduce simulation experiments that incorporate quantitative models.We have instantiated these guidelines in a web-based content management system. With our system you can create Simulation and Experiment Descriptions, enrich them with experimental data and annotate them with domain meta-information to facilitate classification, searching and cross referencing -all with the goal of reusing your models and reproducing your experimental results. Simulation & Experiment Description Markup LanguageOne instantiation of the MIASE guidelines is SED-ML[2-4] -an XML schema, instances of which are recipes describing the combination of models and simulations into reproducible experiments.In particular, SED-ML describes five components essential to compose a simulation experiment description, i.e.:• simulations -a description of the simulations' method, type and algorithm (KiSAO) [5] • models -a description of the models' location, language and modifications • tasks -the glue that combine models and simulations into experiments • data generators -how to present the simulations' results, e.g. 2D graph • outputs -how to transform raw simulation output into numerical or graphical results Insuring Experiment Results are ReproducibleThe Simulation & Experiment Description Meta Language (SED-ML) is a means -like a recipe -to describe the combination of simulations and models in reproducible experiments.We have built a web-based simulation & experiment description repository based on SED-ML.Implemented as an add-on product to the open-source content management system named Plone™, our repository allows researchers to:• In contrast to EBI's existing BioModels database, which hosts only biochemical models written in SBML, our repository will uniquely record any simulation experiment, including those written in C++, thus making the tool generally applicable to the types of simulation models used within the NCSB.We have extended the SED-ML standard to accommodate source-code models, and enriched the output types with descriptive text, images and animations -which makes the repository useful as a laboratory notebook. Results• We can easily realise almost any collaboration scheme imaginable, e.g. research groups can have their own repositories where only curated and approved simulations & experiments are visible to the public, while works in progress are accessible to group members, and colleagues can collaborate on individual simulations & experiments.• We can annotate sim...