A Computational Approach towards a Gene Regulatory Network for the Developing Nematostella vectensis Gut Daniel Botman 1 , Eric Ro ¨ ttinger 2,3,4 , Mark Q. Martindale 5 , Johann de Jong 6 , Jaap A. Kaandorp 1 * 1 Computational Science, University of Amsterdam, Amsterdam, The Netherlands, 2 Universite ´ Nice Sophia Antipolis, Institute for Research on Cancer and Aging, Nice (IRCAN), UMR 7284, Nice, France, 3 Centre National de la Recherche Scientifique (CNRS), Institute for Research on Cancer and Aging, Nice (IRCAN), UMR 7284, Nice, France, 4 Institut National de la Sante ´ et de la Recherche Me ´dicale (INSERM), Institute for Research on Cancer and Aging, Nice (IRCAN), U1081, Nice, France, 5 Whitney Lab for Marine Bioscience, University of Florida, St. Augustine, Florida, United States of America, 6 Computational Cancer Biology Group, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, the Netherlands Abstract Background: The starlet sea anemone Nematostella vectensis is a diploblastic cnidarian that expresses a set of conserved genes for gut formation during its early development. During the last decade, the spatial distribution of many of these genes has been visualized with RNA hybridization or protein immunolocalization techniques. However, due to N. vectensis’ curved and changing morphology, quantification of these spatial data is problematic. A method is developed for two- dimensional gene expression quantification, which enables a numerical analysis and dynamic modeling of these spatial patterns. Methods/Result: In this work, first standardized gene expression profiles are generated from publicly available N. vectensis embryo images that display mRNA and/or protein distributions. Then, genes expressed during gut formation are clustered based on their expression profiles, and further grouped based on temporal appearance of their gene products in embryonic development. Representative expression profiles are manually selected from these clusters, and used as input for a simulation-based optimization scheme. This scheme iteratively fits simulated profiles to the selected profiles, leading to an optimized estimation of the model parameters. Finally, a preliminary gene regulatory network is derived from the optimized model parameters. Outlook: While the focus of this study is N. vectensis, the approach outlined here is suitable for inferring gene regulatory networks in the embryonic development of any animal, thus allowing to comparatively study gene regulation of gut formation in silico across various species. Citation: Botman D, Ro ¨ ttinger E, Martindale MQ, de Jong J, Kaandorp JA (2014) A Computational Approach towards a Gene Regulatory Network for the Developing Nematostella vectensis Gut. PLoS ONE 9(7): e103341. doi:10.1371/journal.pone.0103341 Editor: Andreas Hejnol, Sars International Centre for Marine Molecular Biology, Norway Received June 24, 2014; Accepted June 26, 2014; Published July 30, 2014 Copyright: ß 2014 Botman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files. Funding: This work is part of the BioPreDyn project (www.biopredyn.eu). BioPreDyn is a Cooperation project of the Knowledge Based Bio-Economy (KBBE) EU grant, with the number 289434. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * Email: j.a.kaandorp@uva.nl Introduction During animal development asymmetric signals set up during the early cleavage stages are utilized to initiate different pathways of cell type specific differentiation. Individual cells undergo a complex sequential and combinatorial pattern of differential activation/repression of gene activity that are causally required for the correct assignment of cell identity [1]. The body plan is thus formed by interactions between genes and proteins. A collection of such interactions defines a gene regulatory network (GRN). A GRN can be described using mathematical models. The goal of modeling GRNs is to understand the basic properties of these networks. Various mathematical frameworks have been proposed for the description of GRNs [2]. Some models are quantitative, some models include time or spatial compartments, but combined quantitative spatio-temporal models are rare. Dynamic models that simulate quantitative gene expression levels in interacting domains can capture the formation of gene expression patterns during early animal development [3]. These dynamic simulation models are validated by their ability to reproduce spatio-temporal patterns based on experimental measurements. The general model building process contains three main steps [4]. First, quantitative gene expression data is required, which is extracted from spatio-temporal measurements. Second, a model- ing framework is established from a set of mathematical equations. Third, the parameters in the modeling framework are estimated: the optimal parameters produce simulated expression patterns that correspond to the quantitative gene expression data. An overview of the modeling cycle is shown in Figure 1. Modeling GRNs has the advantage that parameters can be investigated without the noise and limited precision of experimen- tal measurements. The influence of the proposed mechanisms can be tested without the interference of many other processes that PLOS ONE | www.plosone.org 1 July 2014 | Volume 9 | Issue 7 | e103341