Chapter title |
Computational Modeling of Mitochondrial Function from a Systems Biology Perspective
|
---|---|
Chapter number | 14 |
Book title |
Mitochondrial Bioenergetics
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7831-1_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7830-4, 978-1-4939-7831-1
|
Authors |
Sonia Cortassa, Steven J. Sollott, Miguel A. Aon, Cortassa, Sonia, Sollott, Steven J., Aon, Miguel A. |
Abstract |
The advent of "big data" in biology (e.g., genomics, proteomics, metabolomics), holding the promise to reveal the nature of the formidable complexity in cellular and organ makeup and function, has highlighted the compelling need for analytical and integrative computational methods to interpret and make sense of the patterns and changes in those complex networks. Computational models need to be built on sound physicochemical mechanistic principles in order to integrate, interpret, and simulate high-throughput experimental data. Energy transduction processes have been traditionally studied with thermodynamic, kinetic, or thermo-kinetic models, with the latter proving superior to understand the control and regulation of mitochondrial energy metabolism and its interactions with cytoplasmic and other cellular compartments. In this work, we survey the methods to be followed to build a computational model of mitochondrial energetics in isolation or integrated into a network of cellular processes. We describe the use of analytical tools such as elementary flux modes, linear optimization of metabolic models, and control analysis, to help refine our grasp of biologically meaningful behaviors and model reliability. The use of these tools should improve the design, building, and interpretation of steady-state behaviors of computational models while assessing validation criteria and paving the way to prediction. |
X Demographics
Geographical breakdown
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 14 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 3 | 21% |
Professor > Associate Professor | 2 | 14% |
Researcher | 2 | 14% |
Student > Doctoral Student | 1 | 7% |
Lecturer | 1 | 7% |
Other | 1 | 7% |
Unknown | 4 | 29% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 4 | 29% |
Mathematics | 1 | 7% |
Nursing and Health Professions | 1 | 7% |
Agricultural and Biological Sciences | 1 | 7% |
Medicine and Dentistry | 1 | 7% |
Other | 0 | 0% |
Unknown | 6 | 43% |