Performance of networks of artificial neurons: The role of clustering

The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barab{\'a}si-Albert network, and the neuronal network of the C. elegans. Through the use of a systematic way of controlling the clustering coefficient, with the degree of each neuron kept unchanged, we find that the networks with the lower clustering exhibit much better performance. The results are discussed in the practical viewpoint of application, and the biological implications are also suggested.Comment: 4 pages, to appear in PRE as Rapid Com

Genome-scale metabolic model of the rat liver predicts effects of diet restriction.

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver\u27s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected hub genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver\u27s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease

Genetically altered AMPA-type glutamate receptor kinetics in interneurons disrupt long-range synchrony of gamma oscillation

Gamma oscillations synchronized between distant neuronal populations may be critical for binding together brain regions devoted to common processing tasks. Network modeling predicts that such synchrony depends in part on the fast time course of excitatory postsynaptic potentials (EPSPs) in interneurons, and that even moderate slowing of this time course will disrupt synchrony. We generated mice with slowed interneuron EPSPs by gene targeting, in which the gene encoding the 67-kDa form of glutamic acid decarboxylase (GAD67) was altered to drive expression of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptor subunit GluR-B. GluR-B is a determinant of the relatively slow EPSPs in excitatory neurons and is normally expressed at low levels in γ-aminobutyric acid (GABA)ergic interneurons, but at high levels in the GAD-GluR-B mice. In both wild-type and GAD-GluR-B mice, tetanic stimuli evoked gamma oscillations that were indistinguishable in local field potential recordings. Remarkably, however, oscillation synchrony between spatially separated sites was severely disrupted in the mutant, in association with changes in interneuron firing patterns. The congruence between mouse and model suggests that the rapid time course of AMPA receptor-mediated EPSPs in interneurons might serve to allow gamma oscillations to synchronize over distance

Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available

Global and regional brain metabolic scaling and its functional consequences

Background: Information processing in the brain requires large amounts of metabolic energy, the spatial distribution of which is highly heterogeneous reflecting complex activity patterns in the mammalian brain. Results: Here, it is found based on empirical data that, despite this heterogeneity, the volume-specific cerebral glucose metabolic rate of many different brain structures scales with brain volume with almost the same exponent around -0.15. The exception is white matter, the metabolism of which seems to scale with a standard specific exponent -1/4. The scaling exponents for the total oxygen and glucose consumptions in the brain in relation to its volume are identical and equal to $0.86\pm 0.03$, which is significantly larger than the exponents 3/4 and 2/3 suggested for whole body basal metabolism on body mass. Conclusions: These findings show explicitly that in mammals (i) volume-specific scaling exponents of the cerebral energy expenditure in different brain parts are approximately constant (except brain stem structures), and (ii) the total cerebral metabolic exponent against brain volume is greater than the much-cited Kleiber's 3/4 exponent. The neurophysiological factors that might account for the regional uniformity of the exponents and for the excessive scaling of the total brain metabolism are discussed, along with the relationship between brain metabolic scaling and computation.Comment: Brain metabolism scales with its mass well above 3/4 exponen

Dimensionality and dynamics in the behavior of C. elegans

A major challenge in analyzing animal behavior is to discover some underlying simplicity in complex motor actions. Here we show that the space of shapes adopted by the nematode C. elegans is surprisingly low dimensional, with just four dimensions accounting for 95% of the shape variance, and we partially reconstruct "equations of motion" for the dynamics in this space. These dynamics have multiple attractors, and we find that the worm visits these in a rapid and almost completely deterministic response to weak thermal stimuli. Stimulus-dependent correlations among the different modes suggest that one can generate more reliable behaviors by synchronizing stimuli to the state of the worm in shape space. We confirm this prediction, effectively "steering" the worm in real time.Comment: 9 pages, 6 figures, minor correction

Multi-Parametric Analysis and Modeling of Relationships between Mitochondrial Morphology and Apoptosis

Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis

Mitochondrial Morphogenesis, Dendrite Development, and Synapse Formation in Cerebellum Require both Bcl-w and the Glutamate Receptor δ2

Bcl-w belongs to the prosurvival group of the Bcl-2 family, while the glutamate receptor δ2 (Grid2) is an excitatory receptor that is specifically expressed in Purkinje cells, and required for Purkinje cell synapse formation. A recently published result as well as our own findings have shown that Bcl-w can physically interact with an autophagy protein, Beclin1, which in turn has been shown previously to form a protein complex with the intracellular domain of Grid2 and an adaptor protein, nPIST. This suggests that Bcl-w and Grid2 might interact genetically to regulate mitochondria, autophagy, and neuronal function. In this study, we investigated this genetic interaction of Bcl-w and Grid2 through analysis of single and double mutant mice of these two proteins using a combination of histological and behavior tests. It was found that Bcl-w does not control the cell number in mouse brain, but promotes what is likely to be the mitochondrial fission in Purkinje cell dendrites, and is required for synapse formation and motor learning in cerebellum, and that Grid2 has similar phenotypes. Mice carrying the double mutations of these two genes had synergistic effects including extremely long mitochondria in Purkinje cell dendrites, and strongly aberrant Purkinje cell dendrites, spines, and synapses, and severely ataxic behavior. Bcl-w and Grid2 mutations were not found to influence the basal autophagy that is required for Purkinje cell survival, thus resulting in these phenotypes. Our results demonstrate that Bcl-w and Grid2 are two critical proteins acting in distinct pathways to regulate mitochondrial morphogenesis and control Purkinje cell dendrite development and synapse formation. We propose that the mitochondrial fission occurring during neuronal growth might be critically important for dendrite development and synapse formation, and that it can be regulated coordinately by multiple pathways including Bcl-2 and glutamate receptor family members

HUWE1 E3 ligase promotes PINK1/PARKINindependent mitophagy by regulating AMBRA1 activation via IKKa

The selective removal of undesired or damaged mitochondria by autophagy, known as mitophagy, is crucial for cellular homoeostasis, and prevents tumour diffusion, neurodegeneration and ageing. The pro-autophagic molecule AMBRA1 (autophagy/beclin-1 regulator-1) has been defined as a novel regulator of mitophagy in both PINK1/PARKIN-dependent and -independent systems. Here, we identified the E3 ubiquitin ligase HUWE1 as a key inducing factor in AMBRA1-mediated mitophagy, a process that takes place independently of the main mitophagy receptors. Furthermore, we show that mitophagy function of AMBRA1 is post-translationally controlled, upon HUWE1 activity, by a positive phosphorylation on its serine 1014. This modification is mediated by the IKKα kinase and induces structural changes in AMBRA1, thus promoting its interaction with LC3/GABARAP (mATG8) proteins and its mitophagic activity. Altogether, these results demonstrate that AMBRA1 regulates mitophagy through a novel pathway, in which HUWE1 and IKKα are key factors, shedding new lights on the regulation of mitochondrial quality control and homoeostasis in mammalian cells