Computational Systems Biology
Inference and Modelling
Nonfiction, Computers, Application Software, Business Software, Science & Nature, Science, Biological Sciences
| Author: | Paola Lecca, Angela Re, Adaoha Elizabeth Ihekwaba, Ivan Mura, Thanh-Phuong Nguyen | ISBN: | 9780081001158 |
| Publisher: | Elsevier Science | Publication: | July 29, 2016 |
| Imprint: | Woodhead Publishing | Language: | English |
| Author: | Paola Lecca, Angela Re, Adaoha Elizabeth Ihekwaba, Ivan Mura, Thanh-Phuong Nguyen |
| ISBN: | 9780081001158 |
| Publisher: | Elsevier Science |
| Publication: | July 29, 2016 |
| Imprint: | Woodhead Publishing |
| Language: | English |
Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems.
This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling.
The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists.
- Provides a unified presentation of network inference, analysis, and modeling
- Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systems
- Includes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology
- Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications
Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems.
This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling.
The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists.
- Provides a unified presentation of network inference, analysis, and modeling
- Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systems
- Includes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology
- Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications
