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. Although the system can be used for any signaling cascade in any cell type, to validate the system we modeled Toll-like receptor (TLR) signaling in two very different types of cells; tumor cells and white blood cells. The iterative process of using experimental data to improve a computational model, and using predictions from the model to design additional experiments strengthened our understanding of how TLR signaling differs between normal white blood cells and tumor cells. The model and experimental data showed that some of the differences between the tumor cells and normal white blood cells were related to NFκB and TAB3 levels, and also suggested that tumor cells lacked IRAKM-dependent feedback inhibition as a negative regulator of TLR signaling. Finally, we found that these different cell types had distinctly different responses when exposed to two signals indicating that a more biologically relevant model and experimental system should address activation of multiple interconnected signaling cascades, the complexity of which further reinforces the need for a combined computational and molecular approach.

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. SD is a Jaccardian measure generated by averaging all of the differences in the connection patterns between any two nodes of a system. We calculated SD over large random samples of matrices and found that high SD matrices have a low average pathlength and a larger number of clustered structures. SD is a measure of degree distribution with high SD matrices maximizing entropic properties. Phi (Φ), an information theory metric that assesses a system's capacity to integrate information, correlated well with SD - with SD explaining over 90% of the variance in systems above 11 nodes (tested for 4 to 13 nodes). However, newer versions of Φ do not correlate well with the SD metric. CONCLUSIONS: The new network measure, SD, provides a link between high entropic structures and degree distributions as related to small world properties.

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. To probe how postural and form reconfiguration interact, the yaw turning of leopard sharks was examined using morphometric and kinematic analyses. To test how undulatory reconfiguration might evolve, the digital robotic fish were subjected to selection for enhanced performance in a simulated ecology in which each individual had to detect and move towards a food source. In addition to the general issue of reconfiguration, these investigations are united by the fact that the dynamics of undulatory and postural reconfigurations are predicted to be determined, in part, by the structural stiffness of the fish's body. Our method defines undulatory reconfiguration as the combined, point-by-point periodic motion of the body, leaving postural reconfiguration as the combined deviations from undulatory reconfiguration. While undulatory reconfiguration appears to be the sole or primary propulsive driver, postural reconfiguration may contribute to propulsion in hagfish and it is correlated with differences in forward, and backward, swimming in lamprey. Form reconfigures over developmental time in leopard sharks in a manner that is consistent with an allometric scaling theory in which structural stiffness of the body is held constant. However, correlation of a form proxy for structural stiffness of the body suggests that body stiffness may scale in order to limit maximum postural reconfiguration during routine yaw turns. When structural stiffness and undulatory frequency are modeled as determining the tail's undulatory wave speed, both factors evolve under selection for enhanced foraging behavior in the digital fish-like robots. The methods used in making these distinctions between kinds of reconfiguration have broad applicability in fish biology, especially for quantifying complex motor behaviors in the wild and for simulating selection on behavior that leads to directional evolution of functional phenotypes.