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Mathematical modeling in systems biology

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Mathematical modeling in systems biology. / Yli-Harja, Olli; Emmert-Streib, Frank; Yli-Hietanen, Jari.

Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry - 11th Italian Workshop, WIVACE 2016, Revised Selected Papers. Springer Verlag, 2017. s. 161-166 (Communications in Computer and Information Science; Vuosikerta 708).

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Harvard

Yli-Harja, O, Emmert-Streib, F & Yli-Hietanen, J 2017, Mathematical modeling in systems biology. julkaisussa Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry - 11th Italian Workshop, WIVACE 2016, Revised Selected Papers. Communications in Computer and Information Science, Vuosikerta. 708, Springer Verlag, Sivut 161-166, 1/01/00. https://doi.org/10.1007/978-3-319-57711-1_14

APA

Yli-Harja, O., Emmert-Streib, F., & Yli-Hietanen, J. (2017). Mathematical modeling in systems biology. teoksessa Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry - 11th Italian Workshop, WIVACE 2016, Revised Selected Papers (Sivut 161-166). (Communications in Computer and Information Science; Vuosikerta 708). Springer Verlag. https://doi.org/10.1007/978-3-319-57711-1_14

Vancouver

Yli-Harja O, Emmert-Streib F, Yli-Hietanen J. Mathematical modeling in systems biology. julkaisussa Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry - 11th Italian Workshop, WIVACE 2016, Revised Selected Papers. Springer Verlag. 2017. s. 161-166. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-57711-1_14

Author

Yli-Harja, Olli ; Emmert-Streib, Frank ; Yli-Hietanen, Jari. / Mathematical modeling in systems biology. Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry - 11th Italian Workshop, WIVACE 2016, Revised Selected Papers. Springer Verlag, 2017. Sivut 161-166 (Communications in Computer and Information Science).

Bibtex - Lataa

@inproceedings{96fdcff6317147cdb14a9e1c063ae75d,
title = "Mathematical modeling in systems biology",
abstract = "In this opinion paper we describe how mathematical models can serve as the foundation for communication within multidisciplinary research teams by providing a useful joint context. First we consider the role of mathematical modeling in systems biology in the light of our experiences in cancer research and other biological disciplines in the realm of big data. We examine the methodologies of machine learning, observing the differences between the modeling approach and the black box approach. Next, we consider the role of mathematical models in natural sciences, observing three simultaneous goals: prediction, knowledge accumulation, and communication. Finally, we consider the differences of the pathway model and the attractor model in describing genetic networks, and explore the long-standing criticality hypothesis, discussing its value in multidisciplinary research.",
keywords = "Computational biology, Mathematical modeling, Systems biology",
author = "Olli Yli-Harja and Frank Emmert-Streib and Jari Yli-Hietanen",
note = "JUFOID=53801",
year = "2017",
doi = "10.1007/978-3-319-57711-1_14",
language = "English",
isbn = "9783319577104",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "161--166",
booktitle = "Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry - 11th Italian Workshop, WIVACE 2016, Revised Selected Papers",
address = "Germany",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Mathematical modeling in systems biology

AU - Yli-Harja, Olli

AU - Emmert-Streib, Frank

AU - Yli-Hietanen, Jari

N1 - JUFOID=53801

PY - 2017

Y1 - 2017

N2 - In this opinion paper we describe how mathematical models can serve as the foundation for communication within multidisciplinary research teams by providing a useful joint context. First we consider the role of mathematical modeling in systems biology in the light of our experiences in cancer research and other biological disciplines in the realm of big data. We examine the methodologies of machine learning, observing the differences between the modeling approach and the black box approach. Next, we consider the role of mathematical models in natural sciences, observing three simultaneous goals: prediction, knowledge accumulation, and communication. Finally, we consider the differences of the pathway model and the attractor model in describing genetic networks, and explore the long-standing criticality hypothesis, discussing its value in multidisciplinary research.

AB - In this opinion paper we describe how mathematical models can serve as the foundation for communication within multidisciplinary research teams by providing a useful joint context. First we consider the role of mathematical modeling in systems biology in the light of our experiences in cancer research and other biological disciplines in the realm of big data. We examine the methodologies of machine learning, observing the differences between the modeling approach and the black box approach. Next, we consider the role of mathematical models in natural sciences, observing three simultaneous goals: prediction, knowledge accumulation, and communication. Finally, we consider the differences of the pathway model and the attractor model in describing genetic networks, and explore the long-standing criticality hypothesis, discussing its value in multidisciplinary research.

KW - Computational biology

KW - Mathematical modeling

KW - Systems biology

U2 - 10.1007/978-3-319-57711-1_14

DO - 10.1007/978-3-319-57711-1_14

M3 - Conference contribution

SN - 9783319577104

T3 - Communications in Computer and Information Science

SP - 161

EP - 166

BT - Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry - 11th Italian Workshop, WIVACE 2016, Revised Selected Papers

PB - Springer Verlag

ER -