Probabilistic models of individual and collective animal behavior.

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.

Comparative Genomic Analysis of Haemophilus haemolyticus and Nontypeable Haemophilus influenzae and a New Testing Scheme for Their Discrimination.

Haemophilus haemolyticus has been recently discovered to have the potential to cause invasive disease. It is closely related to nontypeable Haemophilus influenzae (NT H. influenzae). NT H. influenzae and H. haemolyticus are often misidentified because none of the existing tests targeting the known phenotypes of H. haemolyticus are able to specifically identify H. haemolyticus Through comparative genomic analysis of H. haemolyticus and NT H. influenzae, we identified genes unique to H. haemolyticus that can be used as targets for the identification of H. haemolyticus A real-time PCR targeting purT (encoding phosphoribosylglycinamide formyltransferase 2 in the purine synthesis pathway) was developed and evaluated. The lower limit of detection was 40 genomes/PCR; the sensitivity and specificity in detecting H. haemolyticus were 98.9% and 97%, respectively. To improve the discrimination of H. haemolyticus and NT H. influenzae, a testing scheme combining two targets (H. haemolyticus purT and H. influenzae hpd, encoding protein D lipoprotein) was also evaluated and showed 96.7% sensitivity and 98.2% specificity for the identification of H. haemolyticus and 92.8% sensitivity and 100% specificity for the identification of H. influenzae, respectively. The dual-target testing scheme can be used for the diagnosis and surveillance of infection and disease caused by H. haemolyticus and NT H. influenzae.

Critical cell wall hole size for lysis in Gram-positive bacteria.

Gram-positive bacteria can transport molecules necessary for their survival through holes in their cell wall. The holes in cell walls need to be large enough to let critical nutrients pass through. However, the cell wall must also function to prevent the bacteria's membrane from protruding through a large hole into the environment and lysing the cell. As such, we hypothesize that there exists a range of cell wall hole sizes that allow for molecule transport but prevent membrane protrusion. Here, we develop and analyse a biophysical theory of the response of a Gram-positive cell's membrane to the formation of a hole in the cell wall. We predict a critical hole size in the range of 15-24 nm beyond which lysis occurs. To test our theory, we measured hole sizes in Streptococcus pyogenes cells undergoing enzymatic lysis via transmission electron microscopy. The measured hole sizes are in strong agreement with our theoretical prediction. Together, the theory and experiments provide a means to quantify the mechanisms of death of Gram-positive cells via enzymatically mediated lysis and provides insights into the range of cell wall hole sizes compatible with bacterial homeostasis.

Genome sequences for five strains of the emerging pathogen Haemophilus haemolyticus.

We report the first whole-genome sequences for five strains, two carried and three pathogenic, of the emerging pathogen Haemophilus haemolyticus. Preliminary analyses indicate that these genome sequences encode markers that distinguish H. haemolyticus from its closest Haemophilus relatives and provide clues to the identity of its virulence factors.

Quantifying enzymatic lysis: estimating the combined effects of chemistry, physiology and physics.

The number of microbial pathogens resistant to antibiotics continues to increase even as the rate of discovery and approval of new antibiotic therapeutics steadily decreases. Many researchers have begun to investigate the therapeutic potential of naturally occurring lytic enzymes as an alternative to traditional antibiotics. However, direct characterization of lytic enzymes using techniques based on synthetic substrates is often difficult because lytic enzymes bind to the complex superstructure of intact cell walls. Here we present a new standard for the analysis of lytic enzymes based on turbidity assays which allow us to probe the dynamics of lysis without preparing a synthetic substrate. The challenge in the analysis of these assays is to infer the microscopic details of lysis from macroscopic turbidity data. We propose a model of enzymatic lysis that integrates the chemistry responsible for bond cleavage with the physical mechanisms leading to cell wall failure. We then present a solution to an inverse problem in which we estimate reaction rate constants and the heterogeneous susceptibility to lysis among target cells. We validate our model given simulated and experimental turbidity assays. The ability to estimate reaction rate constants for lytic enzymes will facilitate their biochemical characterization and development as antimicrobial therapeutics.