Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical ‘phase transition’, whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects

Peptide-mass fingerprinting as a tool for the rapid identification and mapping of cellular proteins

For more than 25 years protein identification has largely depended on automated Edman chemistry (Hewick et al., 1981) or western blotting with an appropriate monoclonal antibody. Several limitations, however, have never been overcome. The Edman procedure is inherently slow (generally one or two peptide or protein samples per day) and does not allow direct identification of many post-translational modifications. In addition, current detection limits are in the low-picomole to upper-femtomole range (Totty et al., 1992). Protein identification by western blotting can be extremely rapid, but requires the ready availability of an extensive library of suitable antibody probes. Large-format 2D-electrophoresis systems now make it possible to resolve several thousand cellular proteins from whole-cell lysates in the low- to upper-femtomole concentration range (Patton et al., 1990), presenting significant analytical challenges. The recent introduction of matrix-assisted laser-desorption (MALD) time-of-flight mass spectrometers (Karas and Hillenkamp, 1988) has led to the rapid analysis (at high sensitivity) of peptide mixtures. New strategies have been developed using a combination of protease digestion, MALD mass spectrometry and searching of peptide-mass databases that promise rapid acceleration in the identification of proteins (Henzel et al., 1993; Pappin et al., 1993; Mann et al., 1993; James et al., 1993; Yates et al., 1993)