000			03676nam a22004935i 4500
001			u375079
003			SIRSI
005			20160812084306.0
007			cr nn 008mamaa
008			101001s2010 gw | s |||| 0|eng d
020			_a9783642156038 _9978-3-642-15603-8
040			_cMX-MeUAM
050		4	_aQA76.9.M35
082	0	4	_a620 _223
100	1		_aPatel, Manish. _eauthor.
245	1	4	_aThe Role of Model Integration in Complex Systems Modelling _h[recurso electrónico] : _bAn Example from Cancer Biology / _cby Manish Patel, Sylvia Nagl.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aX, 168 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUnderstanding Complex Systems, _x1860-0832
505	0		_aNature to Numbers: Complex Systems Modelling of Cancer -- Coping with Complexity: Modelling of Complex Systems -- Complexity and Model Integration: Formalisations -- Novel Strategies for Integrating Models into Systems-Level Simulations -- Experiments in Model Integration -- Discussion.
520			_aModel integration – the process by which different modelling efforts can be brought together to simulate the target system – is a core technology in the field of Systems Biology. In the work presented here model integration was addressed directly taking cancer systems as an example. An in-depth literature review was carried out to survey the model forms and types currently being utilised. This was used to formalise the main challenges that model integration poses, namely that of paradigm (the formalism on which a model is based), focus (the real-world system the model represents) and scale. A two-tier model integration strategy, including a knowledge-driven approach to address model semantics, was developed to tackle these challenges. In the first step a novel description of models at the level of behaviour, rather than the precise mathematical or computational basis of the model, is developed by distilling a set of abstract classes and properties. These can accurately describe model behaviour and hence describe focus in a way that can be integrated with behavioural descriptions of other models. In the second step this behaviour is decomposed into an agent-based system by translating the models into local interaction rules. The book provides a detailed and highly integrated presentation of the method, encompassing both its novel theoretical and practical aspects, which will enable the reader to practically apply it to their model integration needs in academic research and professional settings. The text is self-supporting. It also includes an in-depth current bibliography to relevant research papers and literature. The review of the current state of the art in tumour modelling provides added value.
650		0	_aEngineering.
650		0	_aOncology.
650		0	_aBiological models.
650		0	_aPhysics.
650	1	4	_aEngineering.
650	2	4	_aComplexity.
650	2	4	_aStatistical Physics, Dynamical Systems and Complexity.
650	2	4	_aSystems Biology.
650	2	4	_aCancer Research.
700	1		_aNagl, Sylvia. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642156021
830		0	_aUnderstanding Complex Systems, _x1860-0832
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-15603-8
596			_a19
942			_cLIBRO_ELEC
999			_c202959 _d202959