scholarly journals Parameterizing and operationalizing zooplankton population dynamic and trophic interaction models

2014 ◽  
Vol 71 (2) ◽  
pp. 234-235
Author(s):  
Stéphane Plourde ◽  
Howard I. Browman
Keyword(s):  
Population Dynamic ◽  
Simulation Models ◽  
Specific Model ◽  
Trophic Interaction ◽  
Interaction Models ◽  
Research Fields ◽  
Physiological Processes ◽  
Model Parameterization ◽  
Zooplankton Population ◽  
Species Specific

Abstract Plourde, S., and Browman, H. I. 2014. Parameterizing and operationalizing zooplankton population dynamic and trophic interaction models. – ICES Journal of Marine Science, 71: 234–235. This themed set (TS) of articles was motivated by the need for modellers and biologists–ecologists to work more closely together to produce more realistic simulation models of zooplankton population dynamics and trophic interactions. The TS was intended to cover a broad range of subjects and potential approaches, including identifying crucial gaps in our knowledge and parameterization of biological/physiological processes, experimental/fieldwork aimed at quantifying the response of key physiological and behavioural processes to variations in the environment, identifying novel modelling approaches that would enable the development of simulation models that would minimize the need for species-specific (and stage-specific) model parameterization, etc. Five articles were accepted for inclusion in the TS. They cover the majority of these themes. TSs are intended to be instrumental in focusing attention, triggering opinions, and stimulating ideas, discussion and activity in specific research fields. We hope that this TS has achieved that.

Unknown Parameter Excitation and Estimation for Complex Systems With Dynamic Performances

2021 ◽  
Vol 143 (9) ◽  
Author(s):  
Yi-Ping Chen ◽  
Kuei-Yuan Chan
Keyword(s):  
Complex Systems ◽  
Simulation Models ◽  
Credible Interval ◽  
Specific Model ◽  
Model Parameters ◽  
Real Model ◽  
Dynamic Case ◽  
Dynamic Performances ◽  
Validation Stage ◽  
Setting Parameters

Abstract Simulation models play crucial roles in efficient product development cycles, therefore many studies aim to improve the confidence of a model during the validation stage. In this research, we proposed a dynamic model validation to provide accurate parameter settings for minimal output errors between simulation models and real model experiments. The optimal operations for setting parameters are developed to maximize the effects by specific model parameters while minimizing interactions. To manage the excessive costs associated with simulations of complex systems, we propose a procedure with three main features: the optimal excitation based on global sensitivity analysis (GSA) is done via metamodel techniques, for estimating parameters with the polynomial chaos-based Kalman filter, and validating the updated model based on hypothesis testing. An illustrative mathematical model was used to demonstrate the detail processes in our proposed method. We also apply our method on a vehicle dynamic case with a composite maneuver for exciting unknown model parameters such as inertial and coefficients of the tire model; the unknown model parameters were successfully estimated within a 95% credible interval. The contributions of this research are also underscored through multiple cases.

A Patient-specific in vivo Tumor and Normal Tissue Model for Prediction of the Response to Radiotherapy

2007 ◽  
Vol 46 (03) ◽  
pp. 367-375 ◽  
Author(s):  
V. P. Antipas ◽  
N. K. Uzunoglu ◽  
G. S. Stamatakos
Keyword(s):  
Cancer Biology ◽  
Normal Tissue ◽  
Region Of Interest ◽  
Neural Tissue ◽  
Simulation Models ◽  
Specific Model ◽  
Tissue Response ◽  
Patient Specific ◽  
Treatment Optimization

Summary Objectives: Integration of multiscale experimental cancer biology through the development of computer simulation models seems to be a necessary step towards the better understanding of cancer and patient-individualized treatment optimization. The integration of a four-dimensional patient-specific model of in vivo tumor response to radiotherapy developed by our group with a model of slowly responding normal tissue based on W. Duechting’s approach is presented in this paper. The case of glioblastoma multiforme and its surrounding neural tissue is addressed as a modeling paradigm. Methods: A cubic discretizing mesh is superimposed upon the anatomic region of interest as is reconstructed from pertinent imaging (e.g. MRI) data. On each geometrical cell of the mesh the most crucial biological “laws” e.g. metabolism, cell cycling, tumor geometry changes, cell kill following irradiation etc. are applied. Slowly responding normal neural tissue is modeled by a functional compartment containing indivisible cells and a divisible compartment containing glial cells. Results: The model code has been executed for a simulated period normally covering the radiotherapy course duration and extending a few days after its completion. The following schemes have been simulated: standard fractionation, hyperfractionation, accelerated fractionation, accelerated hyperfractionation and hypofractionation. The predictions are in agreement with the outcome of the RTOG 83-02 phase I/II trial, the retrospective study conducted by Sugawara et al. and the theoretical predictions of Duechting et al. Conclusions: The presented model, although oversimplified, may serve as a basis for a refined simulation of the biological mechanisms involved in tumor and normal tissue response to radiotherapy.

scholarly journals A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Song Guo ◽  
Chunhua Liu ◽  
Peng Zhou ◽  
Yanling Li
Keyword(s):  
Feature Selection ◽  
Posttranslational Modifications ◽  
Optimization Method ◽  
Tyrosine Sulfation ◽  
Selection Scheme ◽  
Protein Tyrosine ◽  
Research Fields ◽  
Physiological Processes ◽  
Novel Method ◽  
Protein Posttranslational Modifications

Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields.

scholarly journals The transcriptional regulating protein of 132 kDa (TReP-132) differentially influences steroidogenic pathways in human adrenal NCI-H295 cells

2004 ◽  
Vol 32 (2) ◽  
pp. 557-569 ◽  
Author(s):  
F Gizard ◽  
E Teissier ◽  
I Dufort ◽  
G Luc ◽  
V Luu-The ◽  
...  
Keyword(s):  
Mrna Levels ◽  
Steroid Biosynthesis ◽  
Steroidogenic Factor 1 ◽  
Transcript Levels ◽  
Adrenal Steroid ◽  
Physiological Processes ◽  
Estrogen Production ◽  
Coordinated Expression ◽  
Species Specific ◽  
Adrenal Glucocorticoid

Steroid hormones synthesized from cholesterol in the adrenal gland are important regulators of many physiological processes. It is now well documented that the expression of many genes required for steroid biosynthesis is dependent on the coordinated expression of the nuclear receptor steroidogenic factor-1 (SF-1). However, transcriptional mechanisms underlying the species-specific, developmentally programmed and hormone-dependent modulation of the adrenal steroid pathways remain to be elucidated. Recently, we demonstrated that the transcriptional regulating protein of 132 kDa (TReP-132) acts as a coactivator of SF-1 to regulate human P450scc gene transcription in human adrenal NCI-H295 cells. The present study shows that overexpression of TReP-132 increases the level of active steroids produced in NCI-H295 cells. The conversion of pregnenolone to downstream steroids following TReP-132 expression showed increased levels of glucocorticoids, C(19) steroids and estrogens. Correlating with these data, TReP-132 increases P450c17 activities via the induction of transcript levels and promoter activity of the P450c17 gene, an effect that is enhanced in the presence of cAMP or SF-1. In addition, P450aro activity and mRNA levels are highly induced by TReP-132, whereas 3beta-hydroxysteroid dehydrogenase type II and P450c11aldo transcript levels are only slightly modulated. Taken together, these results demonstrate that TReP-132 is a trans-acting factor of genes involved in adrenal glucocorticoid, C(19) steroid and estrogen production.

Feature Model Approach for Managing Variablility of Dynamic Behavior Models in Mechatronic Systems

2015 ◽  
Author(s):  
Farisoroosh Abrishamchian ◽  
Felix Oestersötebier ◽  
Ansgar Trächtler
Keyword(s):  
Dynamic Behavior ◽  
Simulation Models ◽  
Specific Model ◽  
Product Line ◽  
Feature Model ◽  
Mechatronic Systems ◽  
Modeling Tools ◽  
Levels Of Detail ◽  
Complex Interactions ◽  
Behavior Models

The design of mechatronic products requires cooperation and coordination of the involved disciplines. To analyze the dynamic behavior of the product’s subsystems and their components, multiple dynamic behavior models (DBM) are developed in different levels of detail (modeling depths) and domains. However, in order to simulate the complex interactions and dependencies between them, models of the whole system are needed, which fit the varying modeling objectives and analysis goals. These comprehensive models are often extensive and the manual construction presupposes deep insight in the specific model approaches and modeling tools. Furthermore, consistency needs to be ensured. The paper describes a way to automatically configure simulation models of the system adopting a Software Product Line (SPL) approach. With the use of feature models, SPL approach provides a structured method for managing variability. The particular focus of this paper is on handling of components in different tools with more than one level of detail through deployment of feature modeling. Also, it presents the concept of a multifunctional model client (MMC), which facilitates integration of solution and system knowledge.

scholarly journals Species–specific crab predation on the hydrozoan clinging jellyfish Gonionemus sp. (Cnidaria, Hydrozoa), subsequent crab mortality, and possible ecological consequences

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3966 ◽  
Author(s):  
Mary R. Carman ◽  
David W. Grunden ◽  
Annette F. Govindarajan
Keyword(s):  
Resource Use ◽  
Callinectes Sapidus ◽  
Trophic Interaction ◽  
Green Crab ◽  
Spider Crab ◽  
Crab Species ◽  
Negative Effect ◽  
Species Specific ◽  
Crab Predation ◽  
Density Treatment

Here we report a unique trophic interaction between the cryptogenic and sometimes highly toxic hydrozoan clinging jellyfish Gonionemus sp. and the spider crab Libinia dubia. We assessed species–specific predation on the Gonionemus medusae by crabs found in eelgrass meadows in Massachusetts, USA. The native spider crab species L. dubia consumed Gonionemus medusae, often enthusiastically, but the invasive green crab Carcinus maenus avoided consumption in all trials. One out of two blue crabs (Callinectes sapidus) also consumed Gonionemus, but this species was too rare in our study system to evaluate further. Libinia crabs could consume up to 30 jellyfish, which was the maximum jellyfish density treatment in our experiments, over a 24-hour period. Gonionemus consumption was associated with Libinia mortality. Spider crab mortality increased with Gonionemus consumption, and 100% of spider crabs tested died within 24 h of consuming jellyfish in our maximum jellyfish density containers. As the numbers of Gonionemus medusae used in our experiments likely underestimate the number of medusae that could be encountered by spider crabs over a 24-hour period in the field, we expect that Gonionemus may be having a negative effect on natural Libinia populations. Furthermore, given that Libinia overlaps in habitat and resource use with Carcinus, which avoids Gonionemus consumption, Carcinus populations could be indirectly benefiting from this unusual crab–jellyfish trophic relationship.

Validations of Particle/Fluid Interaction Models

2004 ◽  
Author(s):  
Z. Charlie Zheng ◽  
N. Zhang ◽  
S. Eckels
Keyword(s):  
Turbulence Model ◽  
Gravitational Force ◽  
Simulation Models ◽  
Fluid Simulation ◽  
Particle Collision ◽  
Type Model ◽  
Interaction Models ◽  
Fluid Interaction ◽  
Good Agreement

Several particle/fluid simulation models have been tested, including Euler-Lagrange type and Euler type models. Other effects, such as gravitational force, turbulence model and particle collision, are also discussed. Comparisons with literature data have shown good agreement by using the Euler type model.

scholarly journals The Importance of GLWamide Neuropeptides in Cnidarian Development and Physiology

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Toshio Takahashi ◽  
Masayuki Hatta
Keyword(s):  
Basic Structure ◽  
Signaling Molecules ◽  
Structure And Function ◽  
Amino Acid Residues ◽  
Peptide Signaling ◽  
Physiological Processes ◽  
History Of ◽  
Species Specific ◽  
And Function ◽  
Potential Use

The peptide-signaling molecules (<50 amino acid residues) occur in a wide variety of invertebrate and vertebrate organisms, playing pivotal roles in physiological, endocrine, and developmental processes. While some of these peptides display similar structures in mammals and invertebrates, others differ with respect to their structure and function in a species-specific manner. Such a conservation of basic structure and function implies that many peptide-signaling molecules arose very early in the evolutionary history of some taxa, while species-specific characteristics led us to suggest that they also acquire the ability to evolve in response to specific environmental conditions. In this paper, we describe GLWamide-family peptides that function as signaling molecules in the process of muscle contraction, metamorphosis, and settlement in cnidarians. The peptides are produced by neurons and are therefore referred to as neuropeptides. We discuss the importance of the neuropeptides in both developmental and physiological processes in a subset of hydrozoans, as well as the potential use as a seed compound in drug development and aspects related to the protection of corals.

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