Computational Biology: From Observation to Statistical Image Analysis to Modelling and Back to Biology
Cameron W. Harvey, Oleg A. Igoshin, Roy D. Welch, Mark Alber and Lawrence J. Shimkets
from: Myxobacteria: Genomics, Cellular and Molecular Biology (Edited by: Zhaomin Yang and Penelope I. Higgs). Caister Academic Press, U.K. (2014)
Abstract
Emergence refers to the manner in which complex behaviors self-organize from a multitude of relatively simple events. Myxococcus xanthus is one of the premier organisms for studying emergence as genetic and biochemical approaches can be wielded in equal measure with computational approaches. Computational modeling has been successfully used to study a range of topics related to myxobacterial self-organization. By reviewing a number of modeling studies from the myxobacteria literature, this chapter aims to demonstrate the cycle of computational modeling. The path from experimental observations to modeling and predictions is traced for three emergent behaviors: swarming, formation of ripples, and fruiting body development. A number of important questions regarding emergent behavior can potentially benefit from computational modeling including the motility mechanisms, signaling during development and biochemical pathways driving direction reversals. Computational models can provide critical insight and predictive power, and are becoming important tools in the study of bacterial behavior read more ...
