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Efficient Analysis of Systems Biology Markup Language Models of Cellular Populations Using Arrays
- Source :
- ACS synthetic biology. 5(8)
- Publication Year :
- 2016
-
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.
- Subjects :
- 0301 basic medicine
Structure (mathematical logic)
education.field_of_study
Markup language
Theoretical computer science
Computer science
Systems biology
Systems Biology
Population
Biomedical Engineering
02 engineering and technology
General Medicine
Biochemistry, Genetics and Molecular Biology (miscellaneous)
Models, Biological
020202 computer hardware & architecture
Variety (cybernetics)
03 medical and health sciences
030104 developmental biology
Chromosome (genetic algorithm)
0202 electrical engineering, electronic engineering, information engineering
Programming Languages
SBML
education
Software
Subjects
Details
- ISSN :
- 21615063
- Volume :
- 5
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- ACS synthetic biology
- Accession number :
- edsair.doi.dedup.....afb0a7cee5ed907c82c8f3a0ccdcb630