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Open Access Methodology article

Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search

Marianna Budnikova1, Jeffrey W Habig1*, Daniel Lobo2, Nicolas Cornia1, Michael Levin2 and Tim Andersen1*

Author Affiliations

1 Department of Computer Science, Boise State University, 1910 University Drive, Boise, ID 83725, USA

2 Department of Biology and Tufts Center for Regeneration and Developmental Biology, Tufts University, 200 Boston Ave, Medford, MA 02155, USA

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BMC Bioinformatics 2014, 15:178  doi:10.1186/1471-2105-15-178

Published: 10 June 2014

Abstract

Background

The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism.

Results

We are developing a model discovery framework that uses a cell-based modeling platform combined with evolutionary search to automatically search for and identify plausible mechanisms for the biological behavior described in PlanformDB. To automate the evolutionary search we developed a way to compare the output of the modeling platform to the morphological descriptions stored in PlanformDB. We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust, cell-based simulation data. These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation, which provides a quantitative metric of similarity between graphs. The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration. We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism, and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB.

Conclusion

The work presented here, including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository, has made feasible the automated development, training, and validation of computational models using morphology-based data. This work is part of an ongoing project to automate the search process, which will greatly expand our ability to identify, consider, and test biological mechanisms in the field of regenerative biology.