PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN
Hypoxia is a pathological condition in which a whole or a region of the body is deprived of adequate oxygen supply. Hypoxia inducible factor (HIF) is a main protein involved in adaptation to low oxygen pressure. There are two oxygen sensors, prolyl hydroxylase (PHD) and factor inhibitting HIF (FIH)....
| Main Authors: | , |
|---|---|
| Format: | Thesis |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2012
|
| Subjects: |
| _version_ | 1797032826462273536 |
|---|---|
| author | , Putu Indah Ciptayani , Dr.-Ing. MHD. Reza M.I. Pulungan, S.Si., M.Sc |
| author_facet | , Putu Indah Ciptayani , Dr.-Ing. MHD. Reza M.I. Pulungan, S.Si., M.Sc |
| author_sort | , Putu Indah Ciptayani |
| collection | UGM |
| description | Hypoxia is a pathological condition in which a whole or a region of the body is
deprived of adequate oxygen supply. Hypoxia inducible factor (HIF) is a main protein involved in adaptation to low oxygen pressure. There are two oxygen sensors, prolyl hydroxylase (PHD) and factor inhibitting HIF (FIH). This research use stochastic modelling to model gene regulation on graded hypoxia. The purpose of this research is to assess stochastic modelling to model gene regulation in graded hypoxia. Through this model, we can investigate the effect of oxygen pressure changing on HIF and the role of oxygen sensor FIH. Continuous-time Markov chain (CTMC) is used as a stochastic model here, in which the number of biological species involved at current time is expressed as a state and the transition from one state to another is taken through a reaction with certain rate. By this model, we can do transient analysis of the amount biological species at any given time with certain probability. The model has been constructed show that the decrease amount of HIF is resulted in increase of oxygen pressure. The model also able to demonstrate the role of FIH. Model is able to correctly classify 21 of the 25 genes that are observed.
These resultsindicate that the use of stochastic modeling in a model of gene regulation in the graded hypoxia can synergize with biological experiments in order to strengthen the understanding of gene regulation pathway in hypoxia. |
| first_indexed | 2024-03-13T22:44:35Z |
| format | Thesis |
| id | oai:generic.eprints.org:101021 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T22:44:35Z |
| publishDate | 2012 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:1010212016-03-04T08:48:18Z https://repository.ugm.ac.id/101021/ PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN , Putu Indah Ciptayani , Dr.-Ing. MHD. Reza M.I. Pulungan, S.Si., M.Sc ETD Hypoxia is a pathological condition in which a whole or a region of the body is deprived of adequate oxygen supply. Hypoxia inducible factor (HIF) is a main protein involved in adaptation to low oxygen pressure. There are two oxygen sensors, prolyl hydroxylase (PHD) and factor inhibitting HIF (FIH). This research use stochastic modelling to model gene regulation on graded hypoxia. The purpose of this research is to assess stochastic modelling to model gene regulation in graded hypoxia. Through this model, we can investigate the effect of oxygen pressure changing on HIF and the role of oxygen sensor FIH. Continuous-time Markov chain (CTMC) is used as a stochastic model here, in which the number of biological species involved at current time is expressed as a state and the transition from one state to another is taken through a reaction with certain rate. By this model, we can do transient analysis of the amount biological species at any given time with certain probability. The model has been constructed show that the decrease amount of HIF is resulted in increase of oxygen pressure. The model also able to demonstrate the role of FIH. Model is able to correctly classify 21 of the 25 genes that are observed. These resultsindicate that the use of stochastic modeling in a model of gene regulation in the graded hypoxia can synergize with biological experiments in order to strengthen the understanding of gene regulation pathway in hypoxia. [Yogyakarta] : Universitas Gadjah Mada 2012 Thesis NonPeerReviewed , Putu Indah Ciptayani and , Dr.-Ing. MHD. Reza M.I. Pulungan, S.Si., M.Sc (2012) PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57506 |
| spellingShingle | ETD , Putu Indah Ciptayani , Dr.-Ing. MHD. Reza M.I. Pulungan, S.Si., M.Sc PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN |
| title | PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON
GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN |
| title_full | PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON
GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN |
| title_fullStr | PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON
GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN |
| title_full_unstemmed | PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON
GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN |
| title_short | PEMODELAN STOKASTIK REGULASI GEN DALAM MERESPON
GRADED HYPOXIA DENGAN CONTINUOUS-TIME MARKOV CHAIN |
| title_sort | pemodelan stokastik regulasi gen dalam merespon graded hypoxia dengan continuous time markov chain |
| topic | ETD |
| work_keys_str_mv | AT putuindahciptayani pemodelanstokastikregulasigendalammerespongradedhypoxiadengancontinuoustimemarkovchain AT dringmhdrezamipulunganssimsc pemodelanstokastikregulasigendalammerespongradedhypoxiadengancontinuoustimemarkovchain |