000			03302nam a22004095i 4500
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/274647764/ _9106996
999			_c103460 _d103460 _x1
001			103460
003			DE-He213
005			20230102113131.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			150225s2015 gw | s |||| 0|eng d
020			_a9783319156064
024	7		_a10.1007/978-3-319-15606-4 _2doi
040			_bspa _dES-MaUEC
050		4	_aQH450 _b2015 EB
100	1		_aAhsen, Mehmet Eren. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/3254153716406858820002/
245	1	0	_aAnalysis of Deterministic Cyclic Gene Regulatory Network Models with Delays _cby Mehmet Eren Ahsen, Hitay Özbay, Silviu-Iulian Niculescu.
264		1	_aCham _bSpringer International Publishing _bSpringer International Publishing _c2015.
300			_a1 recurso en línea (XIII, 94 páginas 15 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aSpringerBriefs in Control, Automation and Robotics, _x2192-6786
490	0		_aEngineering (Springer-11647)
505	0		_aPreface -- Introduction -- Basic Tools from Systems and Control Theory -- Functions with Negative Schwarzian Derivatives -- Deterministic ODE-Based Model with Time Delay -- Gene Regulatory Networks under Negative Feedback -- Gene Regulatory Networks under Positive Feedback -- Summary and Concluding Remarks -- References.
520	3		_aThis brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.
988			_aEBSPRINGER_2018
650		7	_9162770 _aExpresión génica _2embne
700	1		_aÖzbay, Hitay. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n90695772 _1http://viaf.org/viaf/30230661/
700	1		_aNiculescu, Silviu-Iulian. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n99052433 _1http://viaf.org/viaf/10737800/ _998104
776	0	8	_iEdición impresa: _z9783319156071
776	0	8	_iEdición impresa: _z9783319156057
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-15606-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI