Computer Methods Part B

The combination of faster, more advanced computers and more quantitatively oriented biomedical researchers has recently yielded new and more precise methods for the analysis of biomedical data. These better analyses have enhanced the conclusions that can be drawn from biomedical data, and they have changed the way that experiments are designed and performed. This volume, along with previous and forthcoming Computer Methods volumes for the Methods in Enzymology serial, aims to inform biomedical researchers about recent applications of modern data analysis and simulation methods as applied to biomedical research.

* Presents step-by-step computer methods and discusses the techniques in detail to enable their implementation in solving a wide range of problems * Informs biomedical researchers of the modern data analysis methods that have developed alongside computer hardware *Presents methods at the "nuts and bolts" level to identify and resolve a problem and analyze what the results mean

1. Correlation Analysis: A New Tool for Comparing Relaxation-Type Models to Experimental Data (Maurizio Tomaiuolo, Joel Tabak, Richard Bertram) 2. Mathematical modeling of cancer (Vito Quaranta) 3. Pattern recognition in biological systems (Michael Ochs) 4. Modeling and simulations of the immune system as a self regulating network (Peter P. Lee, Peter S. Kim and Doron Levy) 5. Entropy Demystified: The Thermodynamics of Stochastically Fluctuating Systems (Hong Qian) 6. Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates 9P. Holland Alday, Walter F Stafford and John J Correia) 7. High Throughput Computing in the Sciences (Mark Morgan and Andrew Grimshaw) 8. Traffic theory applications in biology (Steven Pincus) 9. Large scale transcriptome data integration across multiple tissues to decipher stem cell signatures (Ghislain Bidaut) 10. DynaFit (Peter Kuzmic) 11. Discrete dynamic modeling of signaling networks (Reka Albert and Ruisheng Wang) 12. Fitting kinetic data by computer simulation methods (Kenneth A. Johnson) 13. Molecular modeling (David Sept) 14. Data integration and analysis of transcriptional networks (Ilya Shmulevich and John D. Aitchison) 15. A Bayesian probability approach to ADHD appraisal (Raina Robeva) 16. Enzyme kinetics parameter estimation using numerical simulation and optimization (Pedro Mendes) 17. Modelling partial hormonal feedback networks (Leon Farhy, William S. Evans and Michael L Johnson)