15,050 research outputs found
Setting Parameters for Biological Models With ANIMO
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for
modeling biological networks, such as e.g. signaling, metabolic or gene
networks. An ANIMO model is essentially the sum of a network topology and a
number of interaction parameters. The topology describes the interactions
between biological entities in form of a graph, while the parameters determine
the speed of occurrence of such interactions. When a mismatch is observed
between the behavior of an ANIMO model and experimental data, we want to update
the model so that it explains the new data. In general, the topology of a model
can be expanded with new (known or hypothetical) nodes, and enables it to match
experimental data. However, the unrestrained addition of new parts to a model
causes two problems: models can become too complex too fast, to the point of
being intractable, and too many parts marked as "hypothetical" or "not known"
make a model unrealistic. Even if changing the topology is normally the easier
task, these problems push us to try a better parameter fit as a first step, and
resort to modifying the model topology only as a last resource. In this paper
we show the support added in ANIMO to ease the task of expanding the knowledge
on biological networks, concentrating in particular on the parameter settings
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Variable grouping in multivariate time series via correlation
The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho
Neural Dynamics of Autistic Behaviors: Cognitive, Emotional, and Timing Substrates
What brain mechanisms underlie autism and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the iSTART model, which proposes how cognitive, emotional, timing, and motor processes may interact together to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes.Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624
Switchable Genetic Oscillator Operating in Quasi-Stable Mode
Ring topologies of repressing genes have qualitatively different long-term
dynamics if the number of genes is odd (they oscillate) or even (they exhibit
bistability). However, these attractors may not fully explain the observed
behavior in transient and stochastic environments such as the cell. We show
here that even repressilators possess quasi-stable, travelling-wave periodic
solutions that are reachable, long-lived and robust to parameter changes. These
solutions underlie the sustained oscillations observed in even rings in the
stochastic regime, even if these circuits are expected to behave as switches.
The existence of such solutions can also be exploited for control purposes:
operation of the system around the quasi-stable orbit allows us to turn on and
off the oscillations reliably and on demand. We illustrate these ideas with a
simple protocol based on optical interference that can induce oscillations
robustly both in the stochastic and deterministic regimes.Comment: 24 pages, 5 main figure
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Time-Restricted Feeding Improves Circadian Dysfunction as well as Motor Symptoms in the Q175 Mouse Model of Huntington's Disease.
Huntington's disease (HD) patients suffer from a progressive neurodegeneration that results in cognitive, psychiatric, cardiovascular, and motor dysfunction. Disturbances in sleep/wake cycles are common among HD patients with reports of delayed sleep onset, frequent bedtime awakenings, and fatigue during the day. The heterozygous Q175 mouse model of HD has been shown to phenocopy many HD core symptoms including circadian dysfunctions. Because circadian dysfunction manifests early in the disease in both patients and mouse models, we sought to determine if early intervention that improve circadian rhythmicity can benefit HD and delay disease progression. We determined the effects of time-restricted feeding (TRF) on the Q175 mouse model. At six months of age, the animals were divided into two groups: ad libitum (ad lib) and TRF. The TRF-treated Q175 mice were exposed to a 6-h feeding/18-h fasting regimen that was designed to be aligned with the middle of the time when mice are normally active. After three months of treatment (when mice reached the early disease stage), the TRF-treated Q175 mice showed improvements in their locomotor activity rhythm and sleep awakening time. Furthermore, we found improved heart rate variability (HRV), suggesting that their autonomic nervous system dysfunction was improved. Importantly, treated Q175 mice exhibited improved motor performance compared to untreated Q175 controls, and the motor improvements were correlated with improved circadian output. Finally, we found that the expression of several HD-relevant markers was restored to WT levels in the striatum of the treated mice using NanoString gene expression assays
Petri nets for systems and synthetic biology
We give a description of a Petri net-based framework for
modelling and analysing biochemical pathways, which uni¯es the qualita-
tive, stochastic and continuous paradigms. Each perspective adds its con-
tribution to the understanding of the system, thus the three approaches
do not compete, but complement each other. We illustrate our approach
by applying it to an extended model of the three stage cascade, which
forms the core of the ERK signal transduction pathway. Consequently
our focus is on transient behaviour analysis. We demonstrate how quali-
tative descriptions are abstractions over stochastic or continuous descrip-
tions, and show that the stochastic and continuous models approximate
each other. Although our framework is based on Petri nets, it can be
applied more widely to other formalisms which are used to model and
analyse biochemical networks
Reachability in Biochemical Dynamical Systems by Quantitative Discrete Approximation (extended abstract)
In this paper, a novel computational technique for finite discrete
approximation of continuous dynamical systems suitable for a significant class
of biochemical dynamical systems is introduced. The method is parameterized in
order to affect the imposed level of approximation provided that with
increasing parameter value the approximation converges to the original
continuous system. By employing this approximation technique, we present
algorithms solving the reachability problem for biochemical dynamical systems.
The presented method and algorithms are evaluated on several exemplary
biological models and on a real case study.Comment: In Proceedings CompMod 2011, arXiv:1109.104
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