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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03384naa a2200457 4500 | |
| 001 | oai:DiVA.org:uu-22765 | |
| 003 | SwePub | |
| 008 | 070122s2007 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227652 URI |
| 024 | 7 | a https://doi.org/10.1016/j.mbs.2006.11.0092 DOI |
| 040 | a (SwePub)uu | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Tucker, Warwicku Uppsala universitet,Matematiska institutionen4 aut0 (Swepub:uu)watuc425 |
| 245 | 1 0 | a Estimating parameters for generalized mass action models using constraint propagation |
| 264 | 1 | b Elsevier BV,c 2007 |
| 338 | a print2 rdacarrier | |
| 520 | a As modern molecular biology moves towards the analysis of biological systems as opposed to their individual components, the need for appropriate mathematical and computational techniques for understanding the dynamics and structure of such systems is becoming more pressing. For example, the modeling of biochemical systems using ordinary differential equations (ODEs) based on high-throughput, time-dense profiles is becoming more common-place, which is necessitating the development of improved techniques to estimate model parameters from such data. Due to the high dimensionality of this estimation problem, straight-forward optimization strategies rarely produce correct parameter values, and hence current methods tend to utilize genetic/evolutionary algorithms to perform non-linear parameter fitting. Here, we describe a completely deterministic approach, which is based on interval analysis. This allows us to examine entire sets of parameters, and thus to exhaust the global search within a finite number of steps. In particular, we show how our method may be applied to a generic class of ODEs used for modeling biochemical systems called Generalized Mass Action Models (GMAs). In addition, we show that for GMAs our method is amenable to the technique in interval arithmetic called constraint propagation, which allows great improvement of its efficiency. To illustrate the applicability of our method we apply it to some networks of biochemical reactions appearing in the literature, showing in particular that, in addition to estimating system parameters in the absence of noise, our method may also be used to recover the topology of these networks. | |
| 650 | 7 | a NATURVETENSKAPx Matematik0 (SwePub)1012 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Mathematics0 (SwePub)1012 hsv//eng |
| 650 | 7 | a NATURVETENSKAPx Biologi0 (SwePub)1062 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Biological Sciences0 (SwePub)1062 hsv//eng |
| 653 | a S-systems | |
| 653 | a GMA systems | |
| 653 | a Metabolic modelling | |
| 653 | a Parameter estimation | |
| 653 | a Biochemical systems | |
| 653 | a Interval analysis | |
| 653 | a Constraint propagation | |
| 653 | a MATHEMATICS | |
| 653 | a MATEMATIK | |
| 653 | a Biology | |
| 653 | a Biologi | |
| 700 | 1 | a Kutalik, Zoltan4 aut |
| 700 | 1 | a Moulton, Vincent4 aut |
| 710 | 2 | a Uppsala universitetb Matematiska institutionen4 org |
| 773 | 0 | t Mathematical Biosciencesd : Elsevier BVg 208:2, s. 607-620q 208:2<607-620x 0025-5564x 1879-3134 |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-22765 |
| 856 | 4 8 | u https://doi.org/10.1016/j.mbs.2006.11.009 |
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