Dynamic Road Pricing: General Network Models

With the advancement of “proteomics” data and systems biology, new techniques are needed to meet the new era of drug discovery. Network theory is increasingly applied to describe complex biological systems, thus implying its essential roles in system-based drug design. In this review, we first summarized general network parameters used in describing biological systems, and then gave some recent applications of these network parameters as topological indices in drug design in terms of Protein Structure Networks (PSNs), Protein-Protein Interaction Networks (PPINs) including related structural PPINs, and Elastic Network Models (ENMs). These network models have enabled the development of new drugs relying on allosteric effects, describing anti-cancer targets, targeting hot spots and key proteins at the protein-protein interfaces and PPINs, and helped drug design by modulating conformational flexibility. Accordingly, we highlighted the integration of network models bringing new paradigms into the next-generation target-based drug discovery.

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Firing rate models for gamma oscillations

Journal of Neurophysiology ◽

10.1152/jn.00741.2018 ◽

2019 ◽

Vol 121 (6) ◽

pp. 2181-2190 ◽

Cited By ~ 8

Author(s):

Stephen Keeley ◽

Áine Byrne ◽

André Fenton ◽

John Rinzel

Keyword(s):

Firing Rate ◽

Computational Models ◽

Network Models ◽

Gamma Oscillations ◽

Brain Regions ◽

Dynamic Feature ◽

Gamma Rhythm ◽

Firing Rate Models ◽

General Network ◽

Dynamic Variables

Gamma oscillations are readily observed in a variety of brain regions during both waking and sleeping states. Computational models of gamma oscillations typically involve simulations of large networks of synaptically coupled spiking units. These networks can exhibit strongly synchronized gamma behavior, whereby neurons fire in near synchrony on every cycle, or weakly modulated gamma behavior, corresponding to stochastic, sparse firing of the individual units on each cycle of the population gamma rhythm. These spiking models offer valuable biophysical descriptions of gamma oscillations; however, because they involve many individual neuronal units they are limited in their ability to communicate general network-level dynamics. Here we demonstrate that few-variable firing rate models with established synaptic timescales can account for both strongly synchronized and weakly modulated gamma oscillations. These models go beyond the classical formulations of rate models by including at least two dynamic variables per population: firing rate and synaptic activation. The models’ flexibility to capture the broad range of gamma behavior depends directly on the timescales that represent recruitment of the excitatory and inhibitory firing rates. In particular, we find that weakly modulated gamma oscillations occur robustly when the recruitment timescale of inhibition is faster than that of excitation. We present our findings by using an extended Wilson-Cowan model and a rate model derived from a network of quadratic integrate-and-fire neurons. These biophysical rate models capture the range of weakly modulated and coherent gamma oscillations observed in spiking network models, while additionally allowing for greater tractability and systems analysis. NEW & NOTEWORTHY Here we develop simple and tractable models of gamma oscillations, a dynamic feature observed throughout much of the brain with significant correlates to behavior and cognitive performance in a variety of experimental contexts. Our models depend on only a few dynamic variables per population, but despite this they qualitatively capture features observed in previous biophysical models of gamma oscillations that involve many individual spiking units.

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Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management

Phytopathology ◽

10.1094/phyto-02-16-0058-fi ◽

2016 ◽

Vol 106 (10) ◽

pp. 1083-1096 ◽

Cited By ~ 105

Author(s):

R. Poudel ◽

A. Jumpponen ◽

D. C. Schlatter ◽

T. C. Paulitz ◽

B. B. McSpadden Gardener ◽

...

Keyword(s):

Network Analysis ◽

Disease Management ◽

A Priori ◽

Network Models ◽

Disease Suppression ◽

Plant Host ◽

Network Analyses ◽

General Network ◽

Positive Direct ◽

Systems Framework

Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. “General network analysis” identifies candidate taxa for maintaining an existing microbial community. “Host-focused analysis” includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. “Pathogen-focused analysis” identifies taxa with direct or indirect associations with taxa known a priori as pathogens. “Disease-focused analysis” identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

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Learning Feedback Molecular Network Models Using Integer Linear Programming

10.1101/2021.11.08.467837 ◽

2021 ◽

Author(s):

Mustafa Ozen ◽

Ali Abdi ◽

Effat S. Emamian

Keyword(s):

Linear Programming ◽

Integer Linear Programming ◽

Computational Models ◽

Network Models ◽

Molecular Networks ◽

Network Data ◽

Learning Approach ◽

Conflicting Information ◽

General Network ◽

Linear Programming Formulation

Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network models constructed using literature and pathway databases may not accurately predict and reproduce experimental network data. This can be due to the incompleteness of literature on molecular pathways, the resources used to construct the networks, or some conflicting information in the resources. In this paper, we propose a network learning approach via an integer linear programming formulation that can efficiently incorporate biological dynamics and regulatory mechanisms of molecular networks in the learning process. Moreover, we present a method to properly take into account the feedback paths, while learning the network from data. Examples are also provided to show how one can apply the proposed learning approach to a network of interest. Overall, the proposed methods are useful for reducing the gap between the curated networks and experimental network data, and result in calibrated networks that are more reliable for making biologically meaningful predictions.

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Recoil from elongation using general network models

Rheologica Acta ◽

10.1007/s00397-005-0433-8 ◽

2005 ◽

Vol 44 (5) ◽

pp. 513-520 ◽

Cited By ~ 7

Author(s):

Roger I. Tanner ◽

Anthony M. Zdilar ◽

Simin Nasseri

Keyword(s):

Network Models ◽

General Network

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What's in a model? Network models as tools instead of representations of what psychiatric disorders really are

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001206 ◽

2019 ◽

Vol 42 ◽

Cited By ~ 2

Author(s):

Hanna M. van Loo ◽

Jan-Willem Romeijn

Keyword(s):

Psychiatric Disorders ◽

Network Models ◽

Model Network

AbstractNetwork models block reductionism about psychiatric disorders only if models are interpreted in a realist manner – that is, taken to represent “what psychiatric disorders really are.” A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

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Special, radical, failure of reduction in psychiatry

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001164 ◽

2019 ◽

Vol 42 ◽

Author(s):

Don Ross

Keyword(s):

Mental Disorder ◽

Brain Structure ◽

Causal Structure ◽

Network Models ◽

Gambling Addiction ◽

Brain Disorder ◽

Mental Processes ◽

Special Radical ◽

Intentional Relationships

AbstractUse of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.

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Application of Lattice Imaging Techniques to Amorphous Films

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100113664 ◽

1975 ◽

Vol 33 ◽

pp. 8-9

Author(s):

S. R. Herd ◽

P. Chaudhari

Keyword(s):

Nearest Neighbor ◽

Random Network ◽

Imaging Techniques ◽

Network Models ◽

Accurate Method ◽

Direct Transmission ◽

Lattice Imaging ◽

Transmission Electron ◽

Lattice Planes ◽

Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

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Network Models for HIV Outreach and Prevention Programs for Drug Users

PsycEXTRA Dataset ◽

10.1037/e495782006-010 ◽

1995 ◽

Author(s):

Robert T. Trotter ◽

Anne M. Bowen ◽

James M. Potter

Keyword(s):

Drug Users ◽

Network Models ◽

Prevention Programs

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