Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Inference of genetic network is an important task to explore and predict the regulatory mechanism inside the cell. Although a number of algorithms have been designed to reverse-engineer regulatory networks effective, it is still a challenge to introduce non-linearity into mathematical models effectively. To address this issue, this work proposes a novel framework to infer genetic networks with non-linearity. A new mathematical model using exponential ordinary differential equations is introduced to realize the non-linearity. Using the hematopoietic stem cell fate determination as the test problem, this work successfully reconstructs two networks for erythroid and granulocyte differentiation respectively, each of which includes 11 genes. Numerical results suggest that our new framework is able to provide accurate realizations of the system states. This work provide new ideas to infer regulatory networks effectively and explore novel regulatory mechanisms.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsHuiru (Jane) Zheng, Zoraida Callejas, David Griol, Haiying Wang, Xiaohua Hu, Harald Schmidt, Jan Baumbach, Julie Dickerson, Le Zhang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2167-2173
Number of pages7
ISBN (Electronic)9781538654880, 9781538654873, 9781538654897
DOIs
Publication statusPublished - 21 Dec 2018
EventIEEE International Conference on Bioinformatics and Biomedicine, 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8609864

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, 2018
Abbreviated titleIEEE BIBM 2018
CountrySpain
CityMadrid
Period3/12/186/12/18
Internet address

Keywords

  • differential equations
  • Gaussian graphical model
  • Hematopoiesis
  • Mathematical modelling
  • Regulatory network

Cite this

Wu, S., Cui, T., & Tian, T. (2018). Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. In H. J. Zheng, Z. Callejas, D. Griol, H. Wang, X. Hu, H. Schmidt, J. Baumbach, J. Dickerson, ... L. Zhang (Eds.), Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2167-2173). [8621476] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIBM.2018.8621476
Wu, Siyuan ; Cui, Tiangang ; Tian, Tianhai. / Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). editor / Huiru (Jane) Zheng ; Zoraida Callejas ; David Griol ; Haiying Wang ; Xiaohua Hu ; Harald Schmidt ; Jan Baumbach ; Julie Dickerson ; Le Zhang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 2167-2173 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).
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title = "Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells",
abstract = "Inference of genetic network is an important task to explore and predict the regulatory mechanism inside the cell. Although a number of algorithms have been designed to reverse-engineer regulatory networks effective, it is still a challenge to introduce non-linearity into mathematical models effectively. To address this issue, this work proposes a novel framework to infer genetic networks with non-linearity. A new mathematical model using exponential ordinary differential equations is introduced to realize the non-linearity. Using the hematopoietic stem cell fate determination as the test problem, this work successfully reconstructs two networks for erythroid and granulocyte differentiation respectively, each of which includes 11 genes. Numerical results suggest that our new framework is able to provide accurate realizations of the system states. This work provide new ideas to infer regulatory networks effectively and explore novel regulatory mechanisms.",
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author = "Siyuan Wu and Tiangang Cui and Tianhai Tian",
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Wu, S, Cui, T & Tian, T 2018, Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. in HJ Zheng, Z Callejas, D Griol, H Wang, X Hu, H Schmidt, J Baumbach, J Dickerson & L Zhang (eds), Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)., 8621476, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 2167-2173, IEEE International Conference on Bioinformatics and Biomedicine, 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621476

Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. / Wu, Siyuan; Cui, Tiangang; Tian, Tianhai.

Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). ed. / Huiru (Jane) Zheng; Zoraida Callejas; David Griol; Haiying Wang; Xiaohua Hu; Harald Schmidt; Jan Baumbach; Julie Dickerson; Le Zhang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 2167-2173 8621476 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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N2 - Inference of genetic network is an important task to explore and predict the regulatory mechanism inside the cell. Although a number of algorithms have been designed to reverse-engineer regulatory networks effective, it is still a challenge to introduce non-linearity into mathematical models effectively. To address this issue, this work proposes a novel framework to infer genetic networks with non-linearity. A new mathematical model using exponential ordinary differential equations is introduced to realize the non-linearity. Using the hematopoietic stem cell fate determination as the test problem, this work successfully reconstructs two networks for erythroid and granulocyte differentiation respectively, each of which includes 11 genes. Numerical results suggest that our new framework is able to provide accurate realizations of the system states. This work provide new ideas to infer regulatory networks effectively and explore novel regulatory mechanisms.

AB - Inference of genetic network is an important task to explore and predict the regulatory mechanism inside the cell. Although a number of algorithms have been designed to reverse-engineer regulatory networks effective, it is still a challenge to introduce non-linearity into mathematical models effectively. To address this issue, this work proposes a novel framework to infer genetic networks with non-linearity. A new mathematical model using exponential ordinary differential equations is introduced to realize the non-linearity. Using the hematopoietic stem cell fate determination as the test problem, this work successfully reconstructs two networks for erythroid and granulocyte differentiation respectively, each of which includes 11 genes. Numerical results suggest that our new framework is able to provide accurate realizations of the system states. This work provide new ideas to infer regulatory networks effectively and explore novel regulatory mechanisms.

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ER -

Wu S, Cui T, Tian T. Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. In Zheng HJ, Callejas Z, Griol D, Wang H, Hu X, Schmidt H, Baumbach J, Dickerson J, Zhang L, editors, Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 2167-2173. 8621476. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). https://doi.org/10.1109/BIBM.2018.8621476