Significance Plants respond to environmental change by triggering biochemical and developmental networks across multiple scales. Multiscale models that link genetic input to the whole-plant scale and beyond might therefore improve biological understanding and yield prediction. We report a modular approach to build such models, validated by a framework model of Arabidopsis thaliana comprising four existing mathematical models. Our model brings together gene dynamics, carbon partitioning, organ growth, shoot architecture, and development in response to environmental signals. It predicted the biomass of each leaf in independent data, demonstrated flexible control of photosynthesis across photoperiods, and predicted the pleiotropic phenotype of a developmentally misregulated transgenic line. Systems biology, crop science, and ecology might thus be linked productively in a community-based approach to modeling.

Summary• 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.

Whole-cell computational models aim to predict cellular phenotypes from genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment. Whole-cell models have great potential to transform bioscience, bioengineering, and medicine. However, numerous challenges remain to achieve wholecell models. Nevertheless, researchers are beginning to leverage recent progress in measurement technology, bioinformatics, data sharing, rule-based modeling, and multi-algorithmic simulation to build the first whole-cell models. We anticipate that ongoing efforts to develop scalable whole-cell modeling tools will enable dramatically more comprehensive and more accurate models, including models of human cells. IntroductionWhole-cell (WC) computational models aim to predict cellular phenotypes from genotype and the environment by representing the function of each gene, gene product, and metabolite [1]. WC models could unify our understanding of cell biology and enable researchers to perform in silico experiments with complete control, scope, and resolution [2,3]. WC models could also help bioengineers rationally design microorganisms that can produce useful chemicals and act as biosensors, and help physicians design personalized therapies tailored to each patient's genome.

Objective Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate, comprehensive models of complex cells. Methods We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in SBML. Results Our analysis revealed several challenges to representing WC models using the current standards. Conclusion We, therefore, propose several new WC modeling standards, software, and databases. Significance We anticipate that these new standards and software will enable more comprehensive models.