Publications by authors named "Harold F Gómez"

6 Publications

  • Page 1 of 1

Morphological study of embryonic Chd8 mouse brains using light-sheet microscopy.

BMC Res Notes 2021 Jan 12;14(1):23. Epub 2021 Jan 12.

Department of Biosystems, Science and Engineering (D-BSSE), ETH Zurich, Mattenstraße 26, 4058, Basel, Switzerland.

Objective: Autism spectrum disorder (ASD) encompasses a group of neurodevelopmental conditions that remain poorly understood due to their genetic complexity. CHD8 is a risk allele strongly associated with ASD, and heterozygous Chd8 loss-of-function mice have been reported to exhibit macrocephaly in early postnatal stages. In this work, we sought to identify measurable brain alterations in early embryonic development.

Results: We performed light-sheet fluorescence microscopy imaging of N-cadherin stained and optically cleared Chd8 and wild-type mouse brains at embryonic day 12.5 (E12.5). We report a detailed morphometric characterization of embryonic brain shapes and cortical neuroepithelial apical architecture. While Chd8 characteristic expansion of the forebrain and midbrain was not observed this early in embryogenesis, a tendency for a decreased lateral ventricular sphericity and an increased intraocular distance in Chd8 brains was found compared to controls. This study advocates the use of high-resolution microscopy technologies and multi-scale morphometric analyses of target brain regions to explore the etiology and cellular basis of Chd8 haploinsufficiency.
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January 2021

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Mol Syst Biol 2020 08;16(8):e9110

Management & IT Consulting Division, Mizuho Information & Research Institute, Inc., Tokyo, Japan.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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August 2020

Toward Community Standards and Software for Whole-Cell Modeling.

IEEE Trans Biomed Eng 2016 10 10;63(10):2007-14. Epub 2016 Jun 10.

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 the Systems Biology Markup Language.

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.
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October 2016

MOCCASIN: converting MATLAB ODE models to SBML.

Bioinformatics 2016 06 9;32(12):1905-6. Epub 2016 Feb 9.

Department of Neurology, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center and School of Medicine, New York, NY 10029, USA.

Unlabelled: MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs). However, sharing or using these models outside of MATLAB is often problematic. A community standard such as Systems Biology Markup Language (SBML) can serve as a neutral exchange format, but translating models from MATLAB to SBML can be challenging-especially for legacy models not written with translation in mind. We developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability) to help. MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks into the SBML format.

Availability And Implementation: MOCCASIN is available under the terms of the LGPL 2.1 license ( Source code, binaries and test cases can be freely obtained from

Contact: :

Supplementary Information: More information is available at
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June 2016

JSBML 1.0: providing a smorgasbord of options to encode systems biology models.

Bioinformatics 2015 Oct 16;31(20):3383-6. Epub 2015 Jun 16.

University of California, San Diego, La Jolla, CA, USA, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.

Unlabelled: JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active open-source project with contributions from a growing, international team of developers who not only maintain compatibility with SBML, but also drive steady improvements to the Java interface and promote ease-of-use with end users.

Availability And Implementation: Source code, binaries and documentation for JSBML can be freely obtained under the terms of the LGPL 2.1 from the website More information about JSBML can be found in the user guide at

Contact: or

Supplementary Information: Supplementary data are available at Bioinformatics online.
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October 2015

Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action.

PLoS One 2014 24;9(7):e102579. Epub 2014 Jul 24.

Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America; Department of Computational Biomedicine, Boston University Medical Campus, Boston, Massachusetts, United States of America.

Background: Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.

Results: In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.

Conclusion: Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.
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April 2015