Biomat 2005 - Proceedings Of The International Symposium On Mathematical And Computational Biology

Inbunden, Engelska, 2006

Av MONDAINI RUBEM P, Rubem P Mondaini, Rui Dilao, Brazil) Mondaini, Rubem P (Federal Univ Of Rio De Janeiro, Portugal) Dilao, Rui (Inst Superior Tecnico, Rubem P. Mondaini

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This volume contains the contributions of the keynote speakers to the BIOMAT 2005 symposium, as well as a collection of selected papers by pioneering researchers. It provides a comprehensive review of the mathematical modeling of cancer development, Alzheimer's disease, malaria, and aneurysm development. Various models for the immune system and epidemiological issues are analyzed and reviewed. The book also explores protein structure prediction by optimization and combinatorial techniques (Steiner trees). The coverage includes bioinformatics issues, regulation of gene expression, evolution, development, DNA and array modeling, and small world networks.

Produktinformation

Utgivningsdatum2006-04-27
Mått160 x 236 x 25 mm
Vikt716 g
FormatInbunden
SpråkEngelska
Antal sidor408
FörlagWorld Scientific Publishing Co Pte Ltd
ISBN9789812567970

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Biological Modeling: A Mathematical Analysis of Cylindrical Shaped Aneurysms (T A Kwembe & S N Jones); On the Origin of Metazoans (F W Cummings); Optimal Control of Distributed Systems Applied to the Problems of Ambient Pollution (S F Arantes & J E M Rivera); Epidemiology and Immunology: Modeling the in Vivo Dynamics of Viral Infections (R M Ribeiro); The Basic Reproductive Rate in the Malaria Model (A P Wyse et al.); Epidemiological Model with Fast Dispersion (M R Ricard et al.); Protein Structure: Structure Prediction of Alpha-Helical Proteins (S R McAllister & C A Floudas); Steiner Minimal Trees, Twist Angles, and the Protein Folding Problem (J M Smith); Steiner Trees as Intramolecular Networks of the Biomacromolecular Structures (R P Mondaini); Bioinformatics: Optimization of Between Group Analysis of Gene Expression Disease Class Prediction (F Baty et al.); On Biclustering with Features Selection for Microarray Data Sets (P M Pardalos et al.); Simple and Effective Classifiers to Model Biological Data (R L Salvini et al.); and other papers.