title: Making models match measurements: model optimization for morphogen patterning networks.
abstract: Mathematical modeling of developmental signaling networks has played an increasingly important role in the identification of regulatory mechanisms by providing a sandbox for hypothesis testing and experiment design. Whether these models consist of an equation with a few parameters or dozens of equations with hundreds of parameters, a prerequisite to model-based discovery is to bring simulated behavior into agreement with observed data via parameter estimation. These parameters provide insight into the system (e.g., enzymatic rate constants describe enzyme properties). Depending on the nature of the model fit desired - from qualitative (relative spatial positions of phosphorylation) to quantitative (exact agreement of spatial position and concentration of gene products) - different measures of data-model mismatch are used to estimate different parameter values, which contain different levels of usable information and/or uncertainty. To facilitate the adoption of modeling as a tool for discovery alongside other tools such as genetics, immunostaining, and biochemistry, careful consideration needs to be given to how well a model fits the available data, what the optimized parameter values mean in a biological context, and how the uncertainty in model parameters and predictions plays into experiment design. The core discussion herein pertains to the quantification of model-to-data agreement, which constitutes the first measure of a model's performance and future utility to the problem at hand. Integration of this experimental data and the appropriate choice of objective measures of data-model agreement will continue to drive modeling forward as a tool that contributes to experimental discovery. The Drosophila melanogaster gap gene system, in which model parameters are optimized against in situ immunofluorescence intensities, demonstrates the importance of error quantification, which is applicable to a wide array of developmental modeling studies.
authors.affiliation: Department of Biological Sciences, Purdue University, 247 S. Martin Jischke Drive, West Lafayette, IN 47907, United States.
authors.forename: J B
authors.initials: JB
authors.lastname: Hengenius
authors.affiliation: Department of Biological Sciences, Purdue University, 247 S. Martin Jischke Drive, West Lafayette, IN 47907, United States.
authors.forename: M
authors.initials: M
authors.lastname: Gribskov
authors.affiliation: Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907, United States.
authors.forename: A E
authors.initials: AE
authors.lastname: Rundell
authors.affiliation: Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907, United States; Department of Agricultural and Biological Engineering, Purdue University, 225 S. University Street, West Lafayette, IN 47907, United States. Electronic address: dumulis@purdue.edu.
authors.forename: D M
authors.initials: DM
authors.lastname: Umulis
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D029721:Drosophila Proteins; D004331:Drosophila melanogaster / Q000196:embryology / Q000235:genetics*; D018507:Gene Expression Regulation, Developmental; D018398:Homeodomain Proteins / Q000235:genetics* / Q000378:metabolism; D008957:Models, Genetic; D009024:Morphogenesis / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*; D015534:Trans-Activators / Q000235:genetics* / Q000378:metabolism
chemical_list: D029721:Drosophila Proteins; D018398:Homeodomain Proteins; D015534:Trans-Activators; C056478:bcd protein, Drosophila
keywords: Developmental biology; Dynamic modeling; Mathematical modeling; Morphogens; Objective functions; Parameter estimation