Efficient Analysis of Systems Biology Markup Language Models of Cellular Populations Using Arrays.

ACS Synth Biol

Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States.

Published: August 2016

AI Article Synopsis

  • SBML is effective for modeling various biochemical systems, but struggles with large, complex models like whole-cell and cellular populations due to its lack of structure for many variables.
  • The SBML arrays package offers a solution for better representation of these regular structures but requires proper awareness of its use for optimum benefits.
  • The paper introduces a method for efficiently simulating arrayed models, demonstrating its advantages through examples like repressilator and genetic toggle switch circuits, showing memory savings with a reasonable trade-off in runtime.

Article Abstract

The Systems Biology Markup Language (SBML) has been widely used for modeling biological systems. Although SBML has been successful in representing a wide variety of biochemical models, the core standard lacks the structure for representing large complex regular systems in a standard way, such as whole-cell and cellular population models. These models require a large number of variables to represent certain aspects of these types of models, such as the chromosome in the whole-cell model and the many identical cell models in a cellular population. While SBML core is not designed to handle these types of models efficiently, the proposed SBML arrays package can represent such regular structures more easily. However, in order to take full advantage of the package, analysis needs to be aware of the arrays structure. When expanding the array constructs within a model, some of the advantages of using arrays are lost. This paper describes a more efficient way to simulate arrayed models. To illustrate the proposed method, this paper uses a population of repressilator and genetic toggle switch circuits as examples. Results show that there are memory benefits using this approach with a modest cost in runtime.

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Source
http://dx.doi.org/10.1021/acssynbio.5b00242DOI Listing

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