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https://www.readbyqxmd.com/read/28928941/computational-design-of-molecular-motors-as-nanocircuits-in-leishmaniasis
#1
Dipali Kosey, Shailza Singh
Cutaneous leishmaniasis is the most common form of lesihmaniasis, caused by Leishmania major and is spread by the bite of a sandfly .This species infects the macrophages and dendritic cells Due to multi-drug resistance, there is a need for a new therapeutic technique. Recently, a novel molecular motor of Leishmania, Myosin XXI, was classified and characterized. In addition, the drug resistance in this organism has been linked with the overexpression of ABC transporters. Systems biology aims to study the simulation and modeling of natural biological systems whereas synthetic biology deals with building novel and artificial biological parts and devices  Together they have contributed enormously to drug discovery, vaccine design and development, infectious disease detection and diagnostics...
2017: F1000Research
https://www.readbyqxmd.com/read/28923160/applying-the-neurodynamics-of-emotional-circular-causalities-in-psychosocial-and-cognitive-therapy-using-multi-sensory-environments-an-orbde-case-study-analysis
#2
Janice Ryan
This exploratory, evidence-based practice research study focuses on presenting a plausible mesoscopic brain dynamics hypothesis for the benefits of treating clients with psychosocial and cognitive challenges using a mindful therapeutic approach and multi-sensory environments. After an extensive neuroscientific review of the therapeutic benefits of mindfulness, a multi-sensory environment is presented as a window of therapeutic opportunity to more quickly and efficiently facilitate the neurobiological experience of becoming more mindful or conscious of self and environment...
October 2017: Nonlinear Dynamics, Psychology, and Life Sciences
https://www.readbyqxmd.com/read/28892489/funneled-potential-and-flux-landscapes-dictate-the-stabilities-of-both-the-states-and-the-flow-fission-yeast-cell-cycle
#3
Xiaosheng Luo, Liufang Xu, Bo Han, Jin Wang
Using fission yeast cell cycle as an example, we uncovered that the non-equilibrium network dynamics and global properties are determined by two essential features: the potential landscape and the flux landscape. These two landscapes can be quantified through the decomposition of the dynamics into the detailed balance preserving part and detailed balance breaking non-equilibrium part. While the funneled potential landscape is often crucial for the stability of the single attractor networks, we have uncovered that the funneled flux landscape is crucial for the emergence and maintenance of the stable limit cycle oscillation flow...
September 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28878644/coexisting-behaviors-of-asymmetric-attractors-in-hyperbolic-type-memristor-based-hopfield-neural-network
#4
Bocheng Bao, Hui Qian, Quan Xu, Mo Chen, Jiang Wang, Yajuan Yu
A new hyperbolic-type memristor emulator is presented and its frequency-dependent pinched hysteresis loops are analyzed by numerical simulations and confirmed by hardware experiments. Based on the emulator, a novel hyperbolic-type memristor based 3-neuron Hopfield neural network (HNN) is proposed, which is achieved through substituting one coupling-connection weight with a memristive synaptic weight. It is numerically shown that the memristive HNN has a dynamical transition from chaotic, to periodic, and further to stable point behaviors with the variations of the memristor inner parameter, implying the stabilization effect of the hyperbolic-type memristor on the chaotic HNN...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28814968/asp-based-method-for-the-enumeration-of-attractors-in-non-deterministic-synchronous-and-asynchronous-multi-valued-networks
#5
Emna Ben Abdallah, Maxime Folschette, Olivier Roux, Morgan Magnin
BACKGROUND: This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton)...
2017: Algorithms for Molecular Biology: AMB
https://www.readbyqxmd.com/read/28780929/a-spiral-attractor-network-drives-rhythmic-locomotion
#6
Angela M Bruno, William N Frost, Mark D Humphries
The joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia's pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit - a spiral - in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation...
August 7, 2017: ELife
https://www.readbyqxmd.com/read/28777728/rat-prefrontal-cortex-inactivations-during-decision-making-are-explained-by-bistable-attractor-dynamics
#7
Alex T Piet, Jeffrey C Erlich, Charles D Kopec, Carlos D Brody
Two-node attractor networks are flexible models for neural activity during decision making. Depending on the network configuration, these networks can model distinct aspects of decisions including evidence integration, evidence categorization, and decision memory. Here, we use attractor networks to model recent causal perturbations of the frontal orienting fields (FOF) in rat cortex during a perceptual decision-making task (Erlich, Brunton, Duan, Hanks, & Brody, 2015). We focus on a striking feature of the perturbation results...
August 4, 2017: Neural Computation
https://www.readbyqxmd.com/read/28777725/memory-states-and-transitions-between-them-in-attractor-neural-networks
#8
Stefano Recanatesi, Mikhail Katkov, Misha Tsodyks
Human memory is capable of retrieving similar memories to a just retrieved one. This associative ability is at the base of our everyday processing of information. Current models of memory have not been able to underpin the mechanism that the brain could use in order to actively exploit similarities between memories. The current idea is that to induce transitions in attractor neural networks, it is necessary to extinguish the current memory. We introduce a novel mechanism capable of inducing transitions between memories where similarities between memories are actively exploited by the neural dynamics to retrieve a new memory...
October 2017: Neural Computation
https://www.readbyqxmd.com/read/28767672/bi-stability-in-type-2-diabetes-mellitus-multi-organ-signalling-network
#9
Shubhankar Kulkarni, Sakshi Sharda, Milind Watve
Type 2 diabetes mellitus (T2DM) is believed to be irreversible although no component of the pathophysiology is irreversible. We show here with a network model that the apparent irreversibility is contributed by the structure of the network of inter-organ signalling. A network model comprising all known inter-organ signals in T2DM showed bi-stability with one insulin sensitive and one insulin resistant attractor. The bi-stability was made robust by multiple positive feedback loops suggesting an evolved allostatic system rather than a homeostatic system...
2017: PloS One
https://www.readbyqxmd.com/read/28764405/local-complexity-predicts-global-synchronization-of-hierarchically-networked-oscillators
#10
Jin Xu, Dong-Ho Park, Junghyo Jo
We study the global synchronization of hierarchically-organized Stuart-Landau oscillators, where each subsystem consists of three oscillators with activity-dependent couplings. We considered all possible coupling signs between the three oscillators, and found that they can generate different numbers of phase attractors depending on the network motif. Here, the subsystems are coupled through mean activities of total oscillators. Under weak inter-subsystem couplings, we demonstrate that the synchronization between subsystems is highly correlated with the number of attractors in uncoupled subsystems...
July 2017: Chaos
https://www.readbyqxmd.com/read/28759958/observation-of-significant-biomarkers-in-osteosarcoma-via-integrating-module-identification-method-with-attract
#11
Jie Qi, Liang Ma, Xiaogang Wang, Ying Li, Kejun Wang
OBJECTIVE: Osteosarcoma (OS) is the most frequent type of bone malignancy, and this disease has a poor prognosis. We aimed to identify the significant genes related with OS by integrating module-identification method and attract approach. METHODS: OS-related microarray data E-GEOD-36001 were obtained from ArrayExpress database, and then protein-protein interaction (PPI) networks of normal and OS were re-weighted by means of spearman correlation coefficient (SCC)...
July 19, 2017: Cancer Biomarkers: Section A of Disease Markers
https://www.readbyqxmd.com/read/28736575/identification-of-bifurcation-transitions-in-biological-regulatory-networks-using-answer-set-programming
#12
Louis Fippo Fitime, Olivier Roux, Carito Guziolowski, Loïc Paulevé
BACKGROUND: Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour. METHODS: In the context of non-deterministic automata networks, we propose a static identification of so-called bifurcations, i...
2017: Algorithms for Molecular Biology: AMB
https://www.readbyqxmd.com/read/28733488/identification-of-optimal-strategies-for-state-transition-of-complex-biological-networks
#13
REVIEW
Meichen Yuan, Weirong Hong, Pu Li
Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor...
July 21, 2017: Biochemical Society Transactions
https://www.readbyqxmd.com/read/28726769/activity-dependent-feedback-inhibition-may-maintain-head-direction-signals-in-mouse-presubiculum
#14
Jean Simonnet, Mérie Nassar, Federico Stella, Ivan Cohen, Bertrand Mathon, Charlotte N Boccara, Richard Miles, Desdemona Fricker
Orientation in space is represented in specialized brain circuits. Persistent head direction signals are transmitted from anterior thalamus to the presubiculum, but the identity of the presubicular target neurons, their connectivity and function in local microcircuits are unknown. Here, we examine how thalamic afferents recruit presubicular principal neurons and Martinotti interneurons, and the ensuing synaptic interactions between these cells. Pyramidal neuron activation of Martinotti cells in superficial layers is strongly facilitating such that high-frequency head directional stimulation efficiently unmutes synaptic excitation...
July 20, 2017: Nature Communications
https://www.readbyqxmd.com/read/28700035/excavation-of-attractor-modules-for-nasopharyngeal-carcinoma-via-integrating-systemic-module-inference-with-attract-method
#15
T Jiang, C-Y Jiang, J-H Shu, Y-J Xu
The molecular mechanism of nasopharyngeal carcinoma (NPC) is poorly understood and effective therapeutic approaches are needed. This research aimed to excavate the attractor modules involved in the progression of NPC and provide further understanding of the underlying mechanism of NPC. Based on the gene expression data of NPC, two specific protein-protein interaction networks for NPC and control conditions were re-weighted using Pearson correlation coefficient. Then, a systematic tracking of candidate modules was conducted on the re-weighted networks via cliques algorithm, and a total of 19 and 38 modules were separately identified from NPC and control networks, respectively...
July 10, 2017: Brazilian Journal of Medical and Biological Research, Revista Brasileira de Pesquisas Médicas e Biológicas
https://www.readbyqxmd.com/read/28696758/local-dynamics-in-trained-recurrent-neural-networks
#16
Alexander Rivkind, Omri Barak
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure...
June 23, 2017: Physical Review Letters
https://www.readbyqxmd.com/read/28680506/an-investigation-of-the-dynamical-transitions-in-harmonically-driven-random-networks-of-firing-rate-neurons
#17
Kyriacos Nikiforou, Pedro A M Mediano, Murray Shanahan
Continuous-time recurrent neural networks are widely used as models of neural dynamics and also have applications in machine learning. But their dynamics are not yet well understood, especially when they are driven by external stimuli. In this article, we study the response of stable and unstable networks to different harmonically oscillating stimuli by varying a parameter ρ, the ratio between the timescale of the network and the stimulus, and use the dimensionality of the network's attractor as an estimate of the complexity of this response...
2017: Cognitive Computation
https://www.readbyqxmd.com/read/28679226/recurrence-networks-to-study-dynamical-transitions-in-a-turbulent-combustor
#18
V Godavarthi, V R Unni, E A Gopalakrishnan, R I Sujith
Thermoacoustic instability and lean blowout are the major challenges faced when a gas turbine combustor is operated under fuel lean conditions. The dynamics of thermoacoustic system is the result of complex nonlinear interactions between the subsystems-turbulent reactive flow and the acoustic field of the combustor. In order to study the transitions between the dynamical regimes in such a complex system, the time series corresponding to one of the dynamic variables is transformed to an ε-recurrence network...
June 2017: Chaos
https://www.readbyqxmd.com/read/28660531/decision-making-neural-circuits-mediating-social-behaviors-an-attractor-network-model
#19
Julián Hurtado-López, David F Ramirez-Moreno, Terrence J Sejnowski
We propose a mathematical model of a continuous attractor network that controls social behaviors. The model is examined with bifurcation analysis and computer simulations. The results show that the model exhibits stable steady states and thresholds for steady state transitions corresponding to some experimentally observed behaviors, such as aggression control. The performance of the model and the relation with experimental evidence are discussed.
June 29, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/28659783/multistability-and-long-timescale-transients-encoded-by-network-structure-in-a-model-of-c-elegans-connectome-dynamics
#20
James M Kunert-Graf, Eli Shlizerman, Andrew Walker, J Nathan Kutz
The neural dynamics of the nematode Caenorhabditis elegans are experimentally low-dimensional and may be understood as long-timescale transitions between multiple low-dimensional attractors. Previous modeling work has found that dynamic models of the worm's full neuronal network are capable of generating reasonable dynamic responses to certain inputs, even when all neurons are treated as identical save for their connectivity. This study investigates such a model of C. elegans neuronal dynamics, finding that a wide variety of multistable responses are generated in response to varied inputs...
2017: Frontiers in Computational Neuroscience
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