Title |
Mathematical Models for Immunology: Current State of the Art and Future Research Directions
|
---|---|
Published in |
Bulletin of Mathematical Biology, October 2016
|
DOI | 10.1007/s11538-016-0214-9 |
Pubmed ID | |
Authors |
Raluca Eftimie, Joseph J. Gillard, Doreen A. Cantrell |
Abstract |
The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 17% |
Unknown | 5 | 83% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 50% |
Members of the public | 3 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 1% |
Unknown | 260 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 57 | 22% |
Researcher | 45 | 17% |
Student > Master | 25 | 10% |
Student > Doctoral Student | 19 | 7% |
Student > Bachelor | 18 | 7% |
Other | 48 | 18% |
Unknown | 51 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 33 | 13% |
Mathematics | 33 | 13% |
Agricultural and Biological Sciences | 28 | 11% |
Immunology and Microbiology | 21 | 8% |
Engineering | 17 | 6% |
Other | 68 | 26% |
Unknown | 63 | 24% |