scholarly journals Computational modelling of atherosclerosis: developing a community resource

2018 ◽  
Author(s):  
Andrew Parton ◽  
Victoria McGilligan ◽  
Maurice O’Kane ◽  
Steven Watterson
Keyword(s):  
Cardiovascular Disease ◽  
Systems Biology ◽  
Computational Model ◽  
Network Model ◽  
Research Community ◽  
Markup Language ◽  
Standard Format ◽  
Drug Intervention ◽  
Future Studies ◽  
Plaque Growth

AbstractRationaleAtherosclerosis is a dynamical process that emerges from the interplay between lipid metabolism, inflammation and innate immunity. The arterial location of atherosclerosis makes it logistically and ethically difficult to study in vivo. To improve our understanding of the disease, we must find alternative ways to investigate its progression. There is currently no computational model of atherosclerosis openly available to the research community for use in future studies and for refinement and development.ObjectiveHere we develop the first predictive computational model to be made openly available and demonstrate its use for therapeutic hypothesis generation.Methods and ResultsWe compiled a dataset of relevant interactions from the literature along with available parameters. These were used to build a network model describing atherosclerotic plaque development. A visual map of the network model was produced using the Systems Biology Graphical Notation (SBGN) and a dynamic mathematical description of the network model that enables us to simulate plaque growth was developed and is made available using the Systems Biology Markup Language (SBML). We used this model to investigate whether multi-drug therapeutic interventions could be identified that stimulate plaque regression. The model produced comprised 20 cell types and 41 proteins with 89 species in total. The visual map is available for reuse and refinement using the SBGN Markup Language standard format and the mathematical model is available using the SBML standard format. We used a genetic algorithm to identify a multi-drug intervention hypothesis comprising five drugs that comprehensively reverse plaque growth within the model.ConclusionsWe have produced the first predictive mathematical and computational model of atherosclerosis that can be reused and refined by the cardiovascular research community. We demonstrated its potential as a tool for future studies of cardiovascular disease by using it to identify multi-drug intervention hypotheses.Subject CodesAtherosclerosis, Computational Biology, Lipids and Cholesterol, Cell Signaling/Signal Transduction, Cardiovascular Disease

scholarly journals MEASUREMENT-BASED QUANTUM COMPUTATION WITH CLUSTER STATES

2009 ◽  
Vol 07 (06) ◽  
pp. 1053-1203 ◽  
Author(s):  
ROBERT RAUßENDORF
Keyword(s):  
Computational Model ◽  
Quantum Computation ◽  
Quantum Logic ◽  
Network Model ◽  
Quantum Computer ◽  
Fault Tolerant ◽  
Cluster State ◽  
Building Blocks ◽  
Network Simulator ◽  
Classical Level

In this thesis, we describe the one-way quantum computer [Formula: see text], a scheme of universal quantum computation that consists entirely of one-qubit measurements on a highly entangled multiparticle state, i.e. the cluster state. We prove the universality of the [Formula: see text], describe the underlying computational model and demonstrate that the [Formula: see text] can be operated fault-tolerantly. In Sec. 2, we show that the [Formula: see text] can be regarded as a simulator of quantum logic networks. In this way, we prove the universality and establish the link to the network model — the common model of quantum computation. We also indicate that the description of the [Formula: see text] as a network simulator is not adequate in every respect. In Sec. 3, we derive the computational model underlying the [Formula: see text], which is very different from the quantum logic network model. The [Formula: see text] has no quantum input, no quantum output and no quantum register, and the unitary gates from some universal set are not the elementary building blocks of [Formula: see text] quantum algorithms. Further, all information that is processed with the [Formula: see text] is the outcomes of one-qubit measurements and thus processing of information exists only at the classical level. The [Formula: see text] is nevertheless quantum-mechanical, as it uses a highly entangled cluster state as the central physical resource. In Sec. 4, we show that there exist nonzero error thresholds for fault-tolerant quantum computation with the [Formula: see text]. Further, we outline the concept of checksums in the context of the [Formula: see text], which may become an element in future practical and adequate methods for fault-tolerant [Formula: see text] computation.

Systems biology markup language: Level 2 and beyond

2003 ◽  
Vol 31 (6) ◽  
pp. 1472-1473 ◽  
Author(s):  
A. Finney ◽  
M. Hucka
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Chemical Species ◽  
Biochemical Networks ◽  
Markup Language ◽  
Exchange Format ◽  
Systems Biology Markup Language ◽  
Level 3 ◽  
Level 1 ◽  
Level 2

The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with the modelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.

scholarly journals A standard-enabled workflow for synthetic biology

2017 ◽  
Vol 45 (3) ◽  
pp. 793-803 ◽  
Author(s):  
Chris J. Myers ◽  
Jacob Beal ◽  
Thomas E. Gorochowski ◽  
Hiroyuki Kuwahara ◽  
Curtis Madsen ◽  
...  
Keyword(s):  
Systems Biology ◽  
Synthetic Biology ◽  
Markup Language ◽  
Design Tools ◽  
Data Standards ◽  
Data Repositories ◽  
Simulation Tools ◽  
Systems Biology Markup Language ◽  
Visualization Tools ◽  
Synthetic Biology Open Language

A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications.

How to Compare Two Quantities? A Computational Model of Flutter Discrimination

2007 ◽  
Vol 19 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Tom Verguts
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Network Model ◽  
Neural Network Model ◽  
Assignment Problem ◽  
Time Window ◽  
Cognitive Domains ◽  
Supervised Training ◽  
Comparison Problem ◽  
Stimulus Time

A task that has been intensively studied at the neural level is f lutter discrimination. I argue that f lutter discrimination entails a combination of a temporal assignment problem and a quantity comparison problem, and propose a neural network model of how these problems are solved. The network combines unsupervised and one-layer supervised training. The unsupervised part clusters input features (stimulus + time window) and the supervised part categorizes the resulting clusters. After training, the model shows a good fit with both neural and behavioral properties. New predictions are outlined and links with other cognitive domains are pointed out.

scholarly journals A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders

2020 ◽  
Vol 16 (12) ◽  
pp. e1008484
Author(s):  
Ryan Smith ◽  
Rayus Kuplicki ◽  
Justin Feinstein ◽  
Katherine L. Forthman ◽  
Jennifer L. Stewart ◽  
...  
Keyword(s):  
Substance Use ◽  
Computational Model ◽  
Substance Use Disorders ◽  
The Body ◽  
Breath Holding ◽  
Healthy Individuals ◽  
Prior Beliefs ◽  
Future Studies ◽  
Psychiatric Conditions ◽  
Processing Mechanisms

Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)–who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.

scholarly journals A Computational Model of Non-optimal Suspiciousness in the Minnesota Trust Game

2021 ◽  
Author(s):  
Rebecca Kazinka ◽  
Iris Vilares ◽  
Angus MacDonald
Keyword(s):  
Decision Making ◽  
Risk Aversion ◽  
Computational Model ◽  
Inequity Aversion ◽  
Trust Game ◽  
Social Decision ◽  
Future Studies ◽  
Two Samples ◽  
Highly Correlated ◽  
Social Decision Making

This study modeled spite sensitivity (the worry that others are willing to incur a loss to hurt you), which is thought to undergird suspiciousness and persecutory ideation. Two samples performed a parametric, non-iterative trust game known as the Minnesota Trust Game (MTG). The MTG is designed to distinguish suspicious decision-making from otherwise rational mistrust by incentivizing the player to trust in certain situations. Individuals who do not trust even under these circumstances are particularly suspicious of their potential partner’s intentions. In Sample 1, 243 undergraduates who completed the MTG showed less trust as the amount of money they could lose increased. However, for choices where partners had a financial disincentive to betray the player, variation in the willingness to trust the partner was associated with suspicious beliefs. To further examine spite sensitivity, we modified the Fehr-Schmidt (1999) inequity aversion model, which compares unequal outcomes in social decision-making tasks, to include the possibility for spite sensitivity. In this case, an anticipated partner’s dislike of advantageous inequity (i.e., guilt) parameter could take on negative values, with negative guilt indicating spite. We hypothesized that the anticipated guilt parameter would be strongly related to suspicious beliefs. Our modification of the Fehr-Schmidt model improved estimation of MTG behavior. We isolated the estimation of partner’s spite-guilt, which was highly correlated with choices most associated with persecutory ideation. We replicated our findings in a second sample, where the estimated spite-guilt parameter correlated with self-reported suspiciousness. The “Suspiciousness” condition, unique to the MTG, can be modeled to isolate spite sensitivity, suggesting that spite sensitivity is separate from inequity aversion or risk aversion, and may provide a means to quantify persecution. The MTG offers promise for future studies to quantify persecutory beliefs in clinical populations.

scholarly journals Pathway-dependent regulation of sleep dynamics in a network model of the sleep-wake cycle

2019 ◽  
Author(s):  
Charlotte Héricé ◽  
Shuzo Sakata
Keyword(s):  
Computational Model ◽  
Eye Movement ◽  
Network Model ◽  
Rem Sleep ◽  
Synaptic Weight ◽  
Nrem Sleep ◽  
Cell Types ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Neural Populations

AbstractSleep is a fundamental homeostatic process within the animal kingdom. Although various brain areas and cell types are involved in the regulation of the sleep-wake cycle, it is still unclear how different pathways between neural populations contribute to its regulation. Here we address this issue by investigating the behavior of a simplified network model upon synaptic weight manipulations. Our model consists of three neural populations connected by excitatory and inhibitory synapses. Activity in each population is described by a firing-rate model, which determines the state of the network. Namely wakefulness, rapid eye movement (REM) sleep or non-REM (NREM) sleep. By systematically manipulating the synaptic weight of every pathway, we show that even this simplified model exhibits non-trivial behaviors: for example, the wake-promoting population contributes not just to the induction and maintenance of wakefulness, but also to sleep induction. Although a recurrent excitatory connection of the REM-promoting population is essential for REM sleep genesis, this recurrent connection does not necessarily contribute to the maintenance of REM sleep. The duration of NREM sleep can be shortened or extended by changes in the synaptic strength of the pathways from the NREM-promoting population. In some cases, there is an optimal range of synaptic strengths that affect a particular state, implying that the amount of manipulations, not just direction (i.e., activation or inactivation), needs to be taken into account. These results demonstrate pathway-dependent regulation of sleep dynamics and highlight the importance of systems-level quantitative approaches for sleep-wake regulatory circuits.Author SummarySleep is essential and ubiquitous across animal species. Over the past half-century, various brain areas, cell types, neurotransmitters, and neuropeptides have been identified as part of a sleep-wake regulating circuitry in the brain. However, it is less explored how individual neural pathways contribute to the sleep-wake cycle. In the present study, we investigate the behavior of a computational model by altering the strength of connections between neuronal populations. This computational model is comprised of a simple network where three neuronal populations are connected together, and the activity of each population determines the current state of the model, that is, wakefulness, rapid-eye-movement (REM) sleep or non-REM (NREM) sleep. When we alter the connection strength of each pathway, we observe that the effect of such alterations on the sleep-wake cycle is highly pathway-dependent. Our results provide further insights into the mechanisms of sleep-wake regulation, and our computational approach can complement future biological experiments.

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