Computational Modeling in Systems Biology

2010 ◽  
pp. 97-120 ◽  
Author(s):  
Ravishankar R. Vallabhajosyula ◽  
Alpan Raval
Keyword(s):  
Systems Biology ◽  
Computational Modeling

scholarly journals Systems Glycobiology: Past, Present, and Future

2020 ◽  
Author(s):  
Songül Yaşar Yıldız
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Structure Function ◽  
Leading Edge ◽  
Research Area ◽  
Basic Knowledge ◽  
Bioinformatics Tools ◽  
Wet Lab ◽  
Living Organisms ◽  
The Impact

Glycobiology is a glycan-based field of study that focuses on the structure, function, and biology of carbohydrates, and glycomics is a sub-study of the field of glycobiology that aims to define structure/function of glycans in living organisms. With the popularity of the glycobiology and glycomics, application of computational modeling expanded in the scientific area of glycobiology over the last decades. The recent availability of progressive Wet-Lab methods in the field of glycobiology and glycomics is promising for the impact of systems biology on the research area of the glycome, an emerging field that is termed “systems glycobiology.” This chapter will summarize the up-to-date leading edge in the use of bioinformatics tools in the field of glycobiology. The chapter provides basic knowledge both for glycobiologists interested in the application of bioinformatics tools and scientists of computational biology interested in studying the glycome.

scholarly journals Mathematical and Computational Modeling in Complex Biological Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhiwei Ji ◽  
Ke Yan ◽  
Wenyang Li ◽  
Haigen Hu ◽  
Xiaoliang Zhu
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Cancer Progression ◽  
High Throughput ◽  
Computational Models ◽  
Molecular Mechanisms ◽  
Biological Process ◽  
Biological Systems ◽  
Complex Diseases ◽  
Modeling Approaches

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

scholarly journals Systems biology: impressions from a newcomer graduate student in 2016

2016 ◽  
Vol 40 (4) ◽  
pp. 443-445 ◽  
Author(s):  
Melanie Rae Simpson
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Graduate Student ◽  
Living System ◽  
Personal Reflection ◽  
Multidisciplinary Collaboration ◽  
Philosophical Basis ◽  
Introductory Overview ◽  
Effective Education

As a newcomer, the philosophical basis of systems biology seems intuitive and appealing, the underlying philosophy being that the whole of a living system cannot be completely understood by the study of its individual parts. Yet answers to the questions “What is systems biology?” and “What constitutes a systems biology approach in 2016?” are somewhat more elusive. This seems to be due largely to the diversity of disciplines involved and the varying emphasis placed on the computational modeling and experimental aspects of systems biology. As such, the education of systems biology would benefit from multidisciplinary collaboration with both instructors and students from a range of disciplines within the same course. This essay is the personal reflection of a graduate student trying to get an introductory overview of the field of systems biology and some thoughts about effective education of systems biology.

scholarly journals Systems Biology in Immunology: A Computational Modeling Perspective

2011 ◽  
Vol 29 (1) ◽  
pp. 527-585 ◽  
Author(s):  
Ronald N. Germain ◽  
Martin Meier-Schellersheim ◽  
Aleksandra Nita-Lazar ◽  
Iain D.C. Fraser
Keyword(s):  
Systems Biology ◽  
Computational Modeling

Chapter 22 Enzyme Kinetics and Computational Modeling for Systems Biology

2009 ◽  
pp. 583-599 ◽  
Author(s):  
Pedro Mendes ◽  
Hanan Messiha ◽  
Naglis Malys ◽  
Stefan Hoops
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Enzyme Kinetics

scholarly journals Key challenges facing data-driven multicellular systems biology

GigaScience ◽  
2019 ◽  
Vol 8 (10) ◽  
Author(s):  
Paul Macklin
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Large Scale ◽  
Computational Models ◽  
Short Review ◽  
Data Driven ◽  
Current State

Abstract Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and financial) commitment.

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