scholarly journals Modeling the ballistic-to-diffusive transition in nematode motility reveals variation in exploratory behavior across species

2019 ◽  
Author(s):  
Stephen J. Helms ◽  
W. Mathijs Rozemuller ◽  
Antonio Carlos Costa ◽  
Leon Avery ◽  
Greg J. Stephens ◽  
...  
Keyword(s):  
Behavioral Control ◽  
Translational Motion ◽  
Model Parameters ◽  
Behavioral Repertoire ◽  
C Elegans ◽  
Hedging Strategies ◽  
Wild Strains ◽  
Quantitative Understanding ◽  
Trajectory Dynamics ◽  
Low Dimensional

AbstractA quantitative understanding of organism-level behavior requires predictive models that can capture the richness of behavioral phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here we investigate the motile behavior of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioral repertoire by measuring motile trajectories of the canonical lab strainC. elegansN2 as well as wild strains and distant species. We focus on trajectory dynamics over timescales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioral control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting “bet-hedging” strategies for foraging.

scholarly journals Modelling the ballistic-to-diffusive transition in nematode motility reveals variation in exploratory behaviour across species

2019 ◽  
Vol 16 (157) ◽  
pp. 20190174
Author(s):  
Stephen J. Helms ◽  
W. Mathijs Rozemuller ◽  
Antonio Carlos Costa ◽  
Leon Avery ◽  
Greg J. Stephens ◽  
...  
Keyword(s):  
Translational Motion ◽  
Laboratory Strain ◽  
Model Parameters ◽  
Hedging Strategies ◽  
Wild Strains ◽  
Quantitative Understanding ◽  
Behavioural Phenotypes ◽  
The Mean ◽  
Trajectory Dynamics ◽  
Low Dimensional

A quantitative understanding of organism-level behaviour requires predictive models that can capture the richness of behavioural phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here, we investigate the motile behaviour of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioural repertoire by measuring motile trajectories of the canonical laboratory strain Caenorhabditis elegans N2 as well as wild strains and distant species. We focus on trajectory dynamics over time scales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioural control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting ‘bet-hedging’ strategies for foraging.

Modeling Cyclic Variability in Spark-Assisted HCCI

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
C. Stuart Daw ◽  
K. Dean Edwards ◽  
Robert M. Wagner ◽  
Johney B. Green
Keyword(s):  
Control Strategies ◽  
Experimental Studies ◽  
Mode Switching ◽  
Load Range ◽  
Gasoline Engines ◽  
Cyclic Variability ◽  
Hcci Combustion ◽  
Quantitative Understanding ◽  
Low Dimensional ◽  
High Degree

Spark assist appears to offer considerable potential for increasing the speed and load range over which homogeneous charge compression ignition (HCCI) is possible in gasoline engines. Numerous experimental studies of the transition between conventional spark-ignited (SI) propagating-flame combustion and HCCI combustion in gasoline engines with spark assist have demonstrated a high degree of deterministic coupling between successive combustion events. Analysis of this coupling suggests that the transition between SI and HCCI can be described as a sequence of bifurcations in a low-dimensional dynamic map. In this paper, we describe methods for utilizing the deterministic relationship between cycles to extract global kinetic rate parameters that can be used to discriminate multiple distinct combustion states and develop a more quantitative understanding of the SI-HCCI transition. We demonstrate the application of these methods for indolene-containing fuels and point out an apparent HCCI mode switching not previously reported. Our results have specific implications for developing dynamic combustion models and feedback control strategies that utilize spark assist to expand the operating range of HCCI combustion.

Reduced-order modelling of the flow around a high-lift configuration with unsteady Coanda blowing

2016 ◽  
Vol 800 ◽  
pp. 72-110 ◽  
Author(s):  
Richard Semaan ◽  
Pradeep Kumar ◽  
Marco Burnazzi ◽  
Gilles Tissot ◽  
Laurent Cordier ◽  
...  
Keyword(s):  
Transient Flow ◽  
Stokes Equations ◽  
Lift Coefficient ◽  
Mean Field ◽  
Galerkin Projection ◽  
Validation Dataset ◽  
Model Parameters ◽  
High Lift ◽  
Modal Expansion ◽  
Low Dimensional

We propose a hierarchy of low-dimensional proper orthogonal decomposition (POD) models for the transient and post-transient flow around a high-lift airfoil with unsteady Coanda blowing over the trailing edge. The modal expansion comprises actuation modes as a lifting method for wall actuation following Graham et al. (Intl J. Numer. Meth. Engng, vol. 44 (7), 1999, pp. 945–972) and Kasnakoğlu et al. (Intl J. Control, vol. 81 (9), 2008, pp. 1475–1492). A novel element is separate actuation modes for different frequencies. The structure of the dynamic model rests on a Galerkin projection using the Navier–Stokes equations, simplifying mean-field considerations, and a stochastic term representing the background turbulence. The model parameters are identified with a data assimilation (4D-Var) method. We propose a model hierarchy from a linear oscillator explaining the suppression of vortex shedding by blowing to a fully nonlinear model resolving unactuated and actuated transients with steady and high-frequency modulation of blowing. The models’ accuracy is assessed through the mode amplitudes and an estimator for the lift coefficient. The robustness of the model is physically justified, and then observed for the training and the validation dataset.

scholarly journals Inference of a Mesoscopic Population Model from Population Spike Trains

2020 ◽  
Vol 32 (8) ◽  
pp. 1448-1498 ◽  
Author(s):  
Alexandre René ◽  
André Longtin ◽  
Jakob H. Macke
Keyword(s):  
Population Model ◽  
Single Neuron ◽  
Simulated Data ◽  
Population Data ◽  
Model Parameters ◽  
Population Activity ◽  
Mcmc Sampling ◽  
Wide Range ◽  
Low Dimensional ◽  
Aggregate Population

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

scholarly journals A Nonparametric Approach to Bond Portfolio Immunization

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1121
Author(s):  
Victor Lapshin
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Best Practice ◽  
Yield Curve ◽  
Model Parameters ◽  
Dimensional Manifold ◽  
Nonparametric Approach ◽  
Hedging Portfolio ◽  
Practice Approach ◽  
Low Dimensional

We consider the problem of short term immunization of a bond-like obligation with respect to changes in interest rates using a portfolio of bonds. In the case that the zero-coupon yield curve belongs to a fixed low-dimensional manifold, the problem is widely known as parametric immunization. Parametric immunization seeks to make the sensitivities of the hedged portfolio price with respect to all model parameters equal to zero. However, within a popular approach of nonparametric (smoothing spline) term structure estimation, parametric hedging is not applicable right away. We present a nonparametric approach to hedging a bond-like obligation allowing for a general form of the term structure estimator with possible smoothing. We show that our approach yields the standard duration based immunization in the limit when the amount of smoothing goes to infinity. We also recover the industry best practice approach of hedging based on key rate durations as another particular case. The hedging portfolio is straightforward to calculate using only basic linear algebra operations.

Identification of Certain Polynomial Nonlinear Structures by Adaptive Selectively-Sensitive Excitation

1993 ◽  
Vol 115 (3) ◽  
pp. 246-255 ◽  
Author(s):  
Y. Ben-Haim
Keyword(s):  
Vibrational Excitation ◽  
Model Identification ◽  
Elastic Beam ◽  
Estimation Procedure ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Nonlinear Part ◽  
Precise Knowledge ◽  
Low Dimensional ◽  
Selective Sensitivity

This paper presents a method for identification of certain polynomial nonlinear dynamic systems by adaptive vibrational excitation. The identification is based on the concept of selective sensitivity and is implemented by an adaptive multihypothesis estimation algorithm. The central problem addressed by this method is reduction of the dimensionality of the space in which the model identification is performed. The method of selective sensitivity allows one to design an excitation which causes the response to be selectively sensitive to a small set of model parameters and insensitive to all the remaining model parameters. The identification of the entire system thus becomes a sequence of low-dimensional estimation problems. The dynamical system is modelled as containing both a linear and a nonlinear part. The estimation procedure presumes precise knowledge of the linear model and knowledge of the structure, though not the parameter values, of the nonlinear part of the model. The theory is developed for three different polynomial forms of the nonlinear model: quadratic, cubic and hybrid polynomial nonlinearities. The estimation procedure is illustrated through simulated identification of quadratic nonlinearities in the small-angle vibrations of a uniform elastic beam.

scholarly journals Estimation and inference with a (nearly) singular Jacobian

10.3982/qe989 ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 1019-1068 ◽  
Author(s):  
Sukjin Han ◽  
Adam McCloskey
Keyword(s):  
Nonlinear Models ◽  
Sample Selection ◽  
Model Parameters ◽  
Asymptotic Results ◽  
Household Expenditures ◽  
One Dimensional ◽  
Asymptotic Size ◽  
Threshold Crossing ◽  
Low Dimensional ◽  
Singular Jacobian

This paper develops extremum estimation and inference results for nonlinear models with very general forms of potential identification failure when the source of this identification failure is known. We examine models that may have a general deficient rank Jacobian in certain parts of the parameter space. When identification fails in one of these models, it becomes underidentified and the identification status of individual parameters is not generally straightforward to characterize. We provide a systematic reparameterization procedure that leads to a reparametrized model with straightforward identification status. Using this reparameterization, we determine the asymptotic behavior of standard extremum estimators and Wald statistics under a comprehensive class of parameter sequences characterizing the strength of identification of the model parameters, ranging from nonidentification to strong identification. Using the asymptotic results, we propose hypothesis testing methods that make use of a standard Wald statistic and data‐dependent critical values, leading to tests with correct asymptotic size regardless of identification strength and good power properties. Importantly, this allows one to directly conduct uniform inference on low‐dimensional functions of the model parameters, including one‐dimensional subvectors. The paper illustrates these results in three examples: a sample selection model, a triangular threshold crossing model, and a collective model for household expenditures.

scholarly journals Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature

2021 ◽  
Author(s):  
Adam Gosztolai ◽  
Alexis Arnaudon
Keyword(s):  
Multiple Scales ◽  
Homeobox Gene ◽  
Clustering Methods ◽  
Dynamical Network ◽  
C Elegans ◽  
Gene Regulatory ◽  
Information Spreading ◽  
Curvature Distribution ◽  
Low Dimensional ◽  
Efficient Learning

Abstract Defining the geometry of networks is typically associated with embedding in low-dimensional spaces such as manifolds. This approach has helped design efficient learning algorithms, unveil network symmetries and study dynamical network processes. However, the choice of embedding space is network-specific, and incompatible spaces can result in information loss. Here, we define a dynamic edge curvature for the study of arbitrary networks measuring the similarity between pairs of dynamical network processes seeded at nearby nodes. We show that the evolution of the curvature distribution exhibits gaps at characteristic timescales indicating bottleneck-edges that limit information spreading. Importantly, curvature gaps robustly encode communities until the phase transition of detectability, where spectral clustering methods fail. We use this insight to derive geometric modularity optimisation and demonstrate it on the European power grid and the C. elegans homeobox gene regulatory network finding previously unidentified communities on multiple scales. Our work suggests using network geometry for studying and controlling the structure of and information spreading on networks.

scholarly journals Neuro-genetic plasticity ofCaenorhabditis elegansbehavioral thermal tolerance

2019 ◽  
Author(s):  
Gregory W. Stegeman ◽  
Denise Medina ◽  
Asher D. Cutter ◽  
William S. Ryu
Keyword(s):  
Nervous System ◽  
Temperature Variation ◽  
Thermal Tolerance ◽  
Transient Receptor Potential ◽  
Behavioral Control ◽  
Receptor Potential ◽  
Adaptive Plasticity ◽  
C Elegans ◽  
Physiological Limits ◽  
Transient Receptor

AbstractBackgroundAnimal responses to thermal stimuli involve intricate contributions of genetics, neurobiology and physiology, with temperature variation providing a pervasive environmental factor for natural selection. Thermal behavior thus exemplifies a dynamic trait that requires non-trivial phenotypic summaries to appropriately capture the trait in response to a changing environment. To characterize the deterministic and plastic components of thermal responses, we developed a novel micro-droplet assay of nematode behavior that permits information-dense summaries of dynamic behavioral phenotypes as reaction norms in response to increasing temperature (thermal tolerance curves, TTC).ResultsWe found thatC. elegansTTCs shift predictably with rearing conditions and developmental stage, with significant differences between distinct wildtype genetic backgrounds. Moreover, after screening TTCs for 58C. elegansgenetic mutant strains, we determined that genes affecting thermosensation, includingcmk-1andtax-4, potentially play important roles in the behavioral control of locomotion at high temperature, implicating neural decision-making in TTC shape rather than just generalized physiological limits. However, expression of the transient receptor potential ion channel TRPA-1 in the nervous system is not sufficient to rescue rearing-dependent plasticity in TTCs conferred by normal expression of this gene, indicating instead a role for intestinal signaling involving TRPA-1 in the adaptive plasticity of thermal performance.ConclusionsThese results implicate nervous system and non-nervous system contributions to behavior, in addition to basic cellular physiology, as key mediators of evolutionary responses to selection from temperature variation in nature.

scholarly journals Metastable regimes and tipping points of biochemical networks with potential applications in precision medicine

2018 ◽  
Author(s):  
Satya Swarup Samal ◽  
Jeyashree Krishnan ◽  
Ali Hadizadeh Esfahani ◽  
Christoph Lüders ◽  
Andreas Weber ◽  
...  
Keyword(s):  
Invariant Manifolds ◽  
Tropical Geometry ◽  
Cell Types ◽  
Biochemical Networks ◽  
Model Parameters ◽  
Tipping Points ◽  
Cancer Disease ◽  
Cell Alterations ◽  
Low Dimensional ◽  
Sensitive Parameters

AbstractThe concept of attractor of dynamic biochemical networks has been used to explain cell types and cell alterations in health and disease. We have recently proposed an extension of the notion of attractor to take into account metastable regimes, defined as long lived dynamical states of the network. These regimes correspond to slow dynamics on low dimensional invariant manifolds of the biochemical networks. Methods based on tropical geometry allow to compute the metastable regimes and represent them as polyhedra in the space of logarithms of the species concentrations. We are looking for sensitive parameters and tipping points of the networks by analyzing how these polyhedra depend on the model parameters. Using the coupled MAPK and PI3K/Akt signaling networks as an example, we test the idea that large changes of the metastable states can be associated to cancer disease specific alterations of the network. In particular, we show that for model parameters representing protein concentrations, the protein differential level between tumors of different types is reasonably reflected in the sensitivity scores, with sensitive parameters corresponding to differential proteins.

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