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Automated Cell Enrichment of Cytomegalovirus-specific T cells for Clinical Applications using the Cytokine-capture System

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CellML 2.0.

Michael Clerx¹, Michael T Cooling², Jonathan Cooper³

¹University of Oxford, Oxford, UK.

Journal of Integrative Bioinformatics

|August 8, 2020

Summary

This summary is machine-generated.

CellML 2.0 is a new XML-based language for creating and sharing mathematical models of physiological systems. It uses MathML for equations and allows models to be built from reusable components for complex systems.

Keywords:

computational physiology modularity physiome project reproducibility reusability

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Area of Science:

Computational Biology
Systems Biology
Biomathematics

Background:

Mathematical models are crucial for understanding complex physiological systems.
Exchanging and reusing these models requires standardized formats.
Existing methods may lack the flexibility for intricate biological systems.

Purpose of the Study:

To introduce CellML 2.0, an XML-based language for describing and exchanging mathematical models of physiological systems.
To provide a standardized syntax and rules for creating modular and reusable biological models.
To facilitate the development of sophisticated computational models in life sciences.

Main Methods:

Utilizes an XML-based structure for defining models.
Embeds MathML to represent mathematical equations and relationships.
Employs a component-based architecture allowing for modularity and reusability.
Supports model import functionality for building complex systems.

Main Results:

CellML 2.0 offers a standardized framework for mathematical modeling in physiology.
The language enables the creation of complex models through the integration of reusable components.
A normative specification is provided for tool developers, alongside an informative rendering for users.

Conclusions:

CellML 2.0 provides a robust and flexible language for computational modeling in physiological sciences.
The specification promotes interoperability and reusability of mathematical models.
This advancement supports the development of more sophisticated and complex biological simulations.