Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks

On controllability of neuronal networks with constraints on the average of control gains

Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.

Prediction of novel genes associated with negative regulators of toll-like receptors-induced inflammation based on endotoxin tolerance

Prior exposure of innate immune cells to lipopolysaccharide (LPS) has caused them to be refractory to further endotoxin stimulation, also termed endotoxin tolerance (ET). Bacterial LPS signals through Toll-like receptor (TLR) 4, which was thought to enable the innate immune system to deal with invasive pathogens and to restrain systemic inflammation efficiently. We established a robust model of ET and determined the level of TNF-α and IL-6 in cultured human monocytes. Then, microarray assay was applied to assess gene expression in this model. The results showed that 356 non-tolerizable genes were differentially expressed at a high level in tolerant monocytes. The genes selected were classified into several categories based on gene ontology (GO) and KEGG pathway database. And then literature annotations, protein-protein interaction (PPI) network, and functional consistency were applied to analyze the non-tolerizable genes. Finally, the microarray results were verified by quantitative real-time PCR of seven representative genes, including the two candidate genes, Spry2 and Smurf2, which were supposed to play a critical role in TLRs-induced inflammation based on literature retrieval. Our results would provide useful information for further analysis of regulating TLRs-induced inflammation, and would facilitate the study of associated mechanisms.

Graph theoretical model of a sensorimotor connectome in zebrafish

Mapping the detailed connectivity patterns (connectomes) of neural circuits is a central goal of neuroscience. The best quantitative approach to analyzing connectome data is still unclear but graph theory has been used with success. We present a graph theoretical model of the posterior lateral line sensorimotor pathway in zebrafish. The model includes 2,616 neurons and 167,114 synaptic connections. Model neurons represent known cell types in zebrafish larvae, and connections were set stochastically following rules based on biological literature. Thus, our model is a uniquely detailed computational representation of a vertebrate connectome. The connectome has low overall connection density, with 2.45% of all possible connections, a value within the physiological range. We used graph theoretical tools to compare the zebrafish connectome graph to small-world, random and structured random graphs of the same size. For each type of graph, 100 randomly generated instantiations were considered. Degree distribution (the number of connections per neuron) varied more in the zebrafish graph than in same size graphs with less biological detail. There was high local clustering and a short average path length between nodes, implying a small-world structure similar to other neural connectomes and complex networks. The graph was found not to be scale-free, in agreement with some other neural connectomes. An experimental lesion was performed that targeted three model brain neurons, including the Mauthner neuron, known to control fast escape turns. The lesion decreased the number of short paths between sensory and motor neurons analogous to the behavioral effects of the same lesion in zebrafish. This model is expandable and can be used to organize and interpret a growing database of information on the zebrafish connectome.