Gregarious Behaviour of Evasive Prey

Lecture on the first SFB/TR 15 meeting, Gummersbach, July, 18 - 20, 2004We model the formation of a herd as a game between a predator and a prey population. The predator receives some information about the composition of the herd when he chases it, but receives no information when he chases a solitary individual. We describe situations in which the herd and its leader are in conflict and in which the leader bows to the herd’s wish but where this is not to the benefit of the herd

Gregarious Behaviour of Evasive Prey

Lecture on the first SFB/TR 15 meeting, Gummersbach, July, 18 - 20, 2004We model the formation of a herd as a game between a predator and a prey population. The predator receives some information about the composition of the herd when he chases it, but receives no information when he chases a solitary individual. We describe situations in which the herd and its leader are in conflict and in which the leader bows to the herd’s wish but where this is not to the benefit of the herd.

Gregarious Behaviour of Evasive Prey

Gregarious behavior of potential prey was explained by Hamilton (1971) on the basis of risk-sharing: The probability of being picked up by a predator is small when one makes part of a large aggregate of prey. This argument holds only if the predator chooses its victims at random. It is not the case for herds of evasive prey in the open, where prey’s gregarious behavior, favorable for the fast group members, makes it easier for the predator to home in on the slowest ones. We show conditions under which, gregarious behavior of the relatively fast prey individuals leaves slowest prey with no other choice but to join the group.Failing to do so would signal their vulnerability, making them a preferred target for the predator. Analysis of an n + 1 player game of a predator and n unequal prey individuals clarifies conditions for fully gregarious, partially gregarious, or solitary behavior of the prey.

Cell Motility Dynamics: A Novel Segmentation Algorithm to Quantify Multi-Cellular Bright Field Microscopy Images

Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single-cell processing to perform objective, accurate quantitative analyses for various biological applications