A computational approach to unraveling TLR signaling in murine mammary carcinoma.

We developed an agent-based model to simulate a signaling cascade which allowed us to focus on the behavior of each class of agents independently of the other classes except when they were in physical contact. A critical piece was the ratio of the populations of agents that interact with one another, not their absolute values. This ratio reflects the effects of the density of each agent in the biological cascade as well as their size and velocity.

A new measure based on degree distribution that links information theory and network graph analysis.

Background: Detailed connection maps of human and nonhuman brains are being generated with new technologies, and graph metrics have been instrumental in understanding the general organizational features of these structures. Neural networks appear to have small world properties: they have clustered regions, while maintaining integrative features such as short average pathlengths.

Results: We captured the structural characteristics of clustered networks with short average pathlengths through our own variable, System Difference (SD), which is computationally simple and calculable for larger graph systems.

Go reconfigure: how fish change shape as they swim and evolve.

The bodies of fish change shape over propulsive, behavioral, developmental, and evolutionary time scales, a general phenomenon that we call "reconfiguration". Undulatory, postural, and form-reconfiguration can be distinguished, studied independently, and examined in terms of mechanical interactions and evolutionary importance. Using a combination of live, swimming fishes and digital robotic fish that are autonomous and self-propelled, we examined the functional relation between undulatory and postural reconfiguration in forward swimming, backward swimming, and yaw turning.