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dc.contributor.authorNguyen, Khuyen L.en
dc.contributor.authorKulasiri, Don D.en
dc.date.accessioned2010-04-14T05:10:43Z
dc.date.issued2009-05-11en
dc.identifier.citationNguyen, L. K., & Kulasiri, D. (2009). On the functional diversity of dynamical behaviour in genetic and metabolic feedback systems. BMC Systems Biology, 3(51). doi:10.1186/1752-0509-3-51en
dc.identifier.issn1752-0509en
dc.identifier.urihttps://hdl.handle.net/10182/1654
dc.description.abstractBackground: Feedback regulation plays crucial roles in the robust control and maintenance of many cellular systems. Negative feedbacks are found to underline both stable and unstable, often oscillatory, behaviours. We explore the dynamical characteristics of systems with single as well as coupled negative feedback loops using a combined approach of analytical and numerical techniques. Particularly, we emphasise how the loop's characterising factors (strength and cooperativity levels) affect system dynamics and how individual loops interact in the coupled-loop systems. Results: We develop an analytical bifurcation analysis based on the stability and the Routh- Hurwitz theorem for a common negative feedback system and a variety of its variants. We demonstrate that different combinations of the feedback strengths of individual loops give rise to different dynamical behaviours. Moreover, incorporating more negative feedback loops always tend to enhance system stability. We show that two mechanisms, in addition to the lengthening of pathway, can lower the Hill coefficient to a biologically plausible level required for sustained oscillations. These include loops coupling and end-product utilisation. We find that the degradation rates solely affect the threshold Hill coefficient for sustained oscillation, while the synthesis rates have more significant roles in determining the threshold feedback strength. Unbalancing the degradation rates between the system species is found as a way to improve stability. Conclusion: The analytical methods and insights presented in this study demonstrate that reallocation of the feedback loop may or may not make the system more stable; the specific effect is determined by the degradation rates of the newly inhibited molecular species. As the loop moves closer to the end of the pathway, the minimum Hill coefficient for oscillation is reduced. Furthermore, under general (unequal) values of the degradation rates, system extension becomes more stable only when the added species degrades slower than it is being produced; otherwise the system is more prone to oscillation. The coupling of loops significantly increases the richness of dynamical bifurcation characteristics. The likelihood of having oscillatory behaviour is directly determined by the loops' strength: stronger loops always result in smaller oscillatory regions.en
dc.format.extent1-30en
dc.language.isoenen
dc.publisherBioMed Central Ltd.en
dc.relationThe original publication is available from - BioMed Central Ltd. - http://hdl.handle.net/10182/1654en
dc.rights© 2009 Nguyen and Kulasiri; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectfeedback regulationen
dc.subjectfeedback loopsen
dc.subjectBioinformaticsen
dc.subject.meshEscherichia colien
dc.subject.meshTryptophanen
dc.subject.meshOperonen
dc.subject.meshBasic Helix-Loop-Helix Transcription Factorsen
dc.subject.meshGene Regulatory Networksen
dc.subject.meshFeedback, Physiologicalen
dc.titleOn the functional diversity of dynamical behaviour in genetic and metabolic feedback systemsen
dc.typeJournal Article
dc.subject.marsdenFields of Research::230000 Mathematical Sciences::230100 Mathematics::230113 Dynamical systemsen
dc.subject.marsdenFields of Research::230000 Mathematical Sciences::239900 Other Mathematical Sciences::239901 Biological Mathematicsen
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciencesen
lu.contributor.unitResearch Management Officeen
lu.contributor.unit/LU/Research Management Office/2018 PBRF Staff groupen
dc.subject.anzsrc1199 Other Medical and Health Sciencesen
dc.relation.isPartOfBMC Systems Biologyen
pubs.issue51en
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/WFMB
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/2018 PBRF Staff group
pubs.publication-statusPublisheden
pubs.publisher-urlhttp://hdl.handle.net/10182/1654en
pubs.volume3en
dc.rights.licenceAttributionen
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0001-8744-1578


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