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19 Publications visible to you, out of a total of 19
Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3).

Abstract (Expand)

The creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML … standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment.

Authors: F. T. Bergmann, J. Cooper, M. Konig, I. Moraru, D. Nickerson, N. Le Novere, B. G. Olivier, S. Sahle, L. Smith, D. Waltemath

Date Published: 20th Mar 2018

Publication Type: Not specified

Harmonizing semantic annotations for computational models in biology.

Abstract (Expand)

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability … and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Authors: M. L. Neal, M. Konig, D. Nickerson, G. Misirli, R. Kalbasi, A. Drager, K. Atalag, V. Chelliah, M. T. Cooling, D. L. Cook, S. Crook, M. de Alba, S. H. Friedman, A. Garny, J. H. Gennari, P. Gleeson, M. Golebiewski, M. Hucka, N. Juty, C. Myers, B. G. Olivier, H. M. Sauro, M. Scharm, J. L. Snoep, V. Toure, A. Wipat, O. Wolkenhauer, D. Waltemath

Date Published: 22nd Jan 2018

Publication Type: Not specified

Computational Modeling in Liver Surgery

Abstract (Expand)

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection … strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

Authors: Bruno Christ, Uta Dahmen, Karl-Heinz Herrmann, Matthias König, Jürgen R. Reichenbach, Tim Ricken, Jana Schleicher, Lars Ole Schwen, Sebastian Vlaic, Navina Waschinsky

Date Published: 14th Nov 2017

Publication Type: Not specified

Toward Community Standards and Software for Whole-Cell Modeling.

Abstract (Expand)

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.

Authors: D. Waltemath, J. R. Karr, F. T. Bergmann, V. Chelliah, M. Hucka, M. Krantz, W. Liebermeister, P. Mendes, C. J. Myers, P. Pir, B. Alaybeyoglu, N. K. Aranganathan, K. Baghalian, A. T. Bittig, P. E. Burke, M. Cantarelli, Y. H. Chew, R. S. Costa, J. Cursons, T. Czauderna, A. P. Goldberg, H. F. Gomez, J. Hahn, T. Hameri, D. F. Gardiol, D. Kazakiewicz, I. Kiselev, V. Knight-Schrijver, C. Knupfer, M. Konig, D. Lee, A. Lloret-Villas, N. Mandrik, J. K. Medley, B. Moreau, H. Naderi-Meshkin, S. K. Palaniappan, D. Priego-Espinosa, M. Scharm, M. Sharma, K. Smallbone, N. J. Stanford, J. H. Song, T. Theile, M. Tokic, N. Tomar, V. Toure, J. Uhlendorf, T. M. Varusai, L. H. Watanabe, F. Wendland, M. Wolfien, J. T. Yurkovich, Y. Zhu, A. Zardilis, A. Zhukova, F. Schreiber

Date Published: 10th Jun 2016

Publication Type: Not specified

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