Marine plant dispersal and connectivity measures differ in their sensitivity to biophysical model parameters

2022 ◽  
pp. 105313
Author(s):  
Jodie Schlaefer ◽  
Alex Carter ◽  
Severine Choukroun ◽  
Robert Coles ◽  
Kay Critchell ◽  
...  
Keyword(s):  
Biophysical Model ◽  
Model Parameters ◽  
Plant Dispersal ◽  
Marine Plant

scholarly journals The Energetics of Molecular Adaptation in Transcriptional Regulation

2019 ◽  
Author(s):  
Griffin Chure ◽  
Manuel Razo-Mejia ◽  
Nathan M. Belliveau ◽  
Tal Einav ◽  
Zofii A. Kaczmarek ◽  
...  
Keyword(s):  
Free Energy ◽  
Transcriptional Regulation ◽  
Dna Binding ◽  
Coarse Graining ◽  
Binding Domain ◽  
Biophysical Model ◽  
Model Parameters ◽  
Biophysical Parameters ◽  
Binding Domains ◽  
Laci Repressor

Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. Here, we present a framework centered on measuring changes in the free energy of the system to link individual mutations in an allosteric transcriptional repressor to the parameters which govern its response. We find the energetic effects of the mutations can be categorized into several classes which have characteristic curves as a function of the inducer concentration. We experimentally test these diagnostic predictions using the well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. We find that the change in gene expression due to a point mutation can be captured by modifying only a subset of the model parameters that describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, we show that the induction profiles and resulting free energies associated with pairwise double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions.SummaryWe present a biophysical model of allosteric transcriptional regulation that directly links the location of a mutation within a repressor to the biophysical parameters that describe its behavior. We explore the phenotypic space of a repressor with mutations in either the inducer binding or DNA binding domains. Using the LacI repressor in E. coli, we make sharp, falsifiable predictions and use this framework to generate a null hypothesis for how double mutants behave given knowledge of the single mutants. Linking mutations to the parameters which govern the system allows for quantitative predictions of how the free energy of the system changes as a result, permitting coarse graining of high-dimensional data into a single-parameter description of the mutational consequences.

scholarly journals Estimating Ectopic Beat Probability with Simplified Statistical Models that Account for Experimental Uncertainty

2021 ◽  
Author(s):  
Qingchu Jin ◽  
Joseph L. Greenstein ◽  
Raimond L. Winslow
Keyword(s):  
Sensitivity Analysis ◽  
Cardiac Myocyte ◽  
Special Property ◽  
Biophysical Model ◽  
Ectopic Beat ◽  
Model Parameters ◽  
Computational Burden ◽  
Simplified Approach ◽  
Ectopic Beats ◽  
The Relationship

AbstractEctopic beats (EBs) are cellular arrhythmias that can trigger lethal arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses can pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EBs (P(EB)). As an example, in this study, we build an LRM for P(EB) as a function of diastolic cytosolic Ca2+ concentration ([Ca2+]i), sarcoplasmic reticulum (SR) Ca2+ load, and kinetic parameters of the inward rectifier K+ current (IK1) and ryanodine receptor (RyR). This approach, which we refer to as arrhythmia sensitivity analysis, allows for evaluation of the relationship between these arrhythmic event probabilities and their associated parameters. This LRM is also used to demonstrate how uncertainties in experimentally measured values determine the uncertainty in P(EB). In a study of the role of [Ca2+]SR uncertainty, we show a special property of the uncertainty in P(EB), where with increasing [Ca2+]SR uncertainty, P(EB) uncertainty first increases and then decreases. Lastly, we demonstrate that IK1 suppression, at the level that occurs in heart failure myocytes, increases P(EB).Author summaryAn ectopic beat is an abnormal cellular electrical event which can trigger dangerous arrhythmias in the heart. Complex biophysical models of the cardiac myocyte can be used to reveal how cell properties affect the probability of ectopic beats. However, such analyses can pose a huge computational burden. We develop a simplified approach that enables a highly complex biophysical model to be reduced to a rather simple statistical model from which the functional relationship between myocyte model parameters and the probability of an ectopic beat is determined. We refer to this approach as arrhythmia sensitivity analysis. Given the efficiency of our approach, we also use it to demonstrate how uncertainties in experimentally measured myocyte model parameters determine the uncertainty in ectopic beat probability. We find that, with increasing model parameter uncertainty, the uncertainty in probability of ectopic beat first increases and then decreases. In general, our approach can efficiently analyze the relationship between cardiac myocyte parameters and the probability of ectopic beats and can be used to study how uncertainty of these cardiac myocyte parameters influences the ectopic beat probability.

scholarly journals Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

2019 ◽  
Author(s):  
N Barros-Zulaica ◽  
J Rahmon ◽  
G Chindemi ◽  
R Perin ◽  
H Markram ◽  
...  
Keyword(s):  
Release Site ◽  
Data Driven ◽  
Biophysical Model ◽  
Model Parameters ◽  
Synaptic Connections ◽  
Readily Releasable Pool ◽  
Data Driven Approach ◽  
Using Data ◽  
Reported Data

AbstractPrevious studies based on the ‘Quantal Model’ for synaptic transmission suggested that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily-releasable pool (NRRP), from paired whole-cell recordings of layer 5 thick tufted pyramidal cell (L5_TTPC) connections in the somatosensory neocortex. Our approach extends the work of Loebel and colleagues to take advantage of a recently reported data-driven biophysical model of neocortical tissue. Using this approach, we estimated NRRP to be between two to three for connections between L5-TTPC. To constrain NRRP values for other connections in the microcircuit, we developed and validated a generalization approach using data on post-synaptic potential (PSP) coefficient of variations (CVs) from literature and matching to in silico experiments. Our study shows that synaptic connections in the neocortex generally are mediated by MVR and provides a data-driven approach to constrain the MVR model parameters of the microcircuit.

scholarly journals Designing the Next Generation of Condition Tracking and Early Warning Systems for Shellfish Aquaculture

2021 ◽  
Vol 9 (10) ◽  
pp. 1084
Author(s):  
Jean-Marc Guarini ◽  
Shawn Hinz ◽  
Jennifer Coston-Guarini
Keyword(s):  
Early Warning ◽  
Null Distribution ◽  
Warning System ◽  
Early Warning Systems ◽  
Adductor Muscle ◽  
Biophysical Model ◽  
Data Series ◽  
Model Parameters ◽  
Discrete Events ◽  
Functional Responses

Early detection of environmental disturbances affecting shellfish stock condition is highly desirable for aquaculture activities. In this article, a new biophysical model-based early warning system (EWS) is described, that assesses bivalve stock condition by diagnosing signs of persistent physiological dysfunctioning. The biophysical model represents valve gape dynamics, controlled by active contractions of the adductor muscle countering the passive action of the hinge ligament; the dynamics combine continuous convergence to a steady-state interspersed with discrete closing events. A null simulation was introduced to describe undisturbed conditions. The diagnostic compares valve gape measurements and simulations. Indicators are inferred from the model parameters, and disturbances are assessed when their estimates deviate from their null distribution. Instead of focusing only on discrete events, our EWS exploits the complete observed dynamics within successive time intervals defined by the variation scales. When applied to a valvometry data series, collected in controlled conditions from scallops (Pecten maximus), the EWS indicated that one among four individuals exhibited signs its physiological condition was degrading. This was detected neither during experiments nor during the initial data analysis, suggesting the utility of an approach that quantifies physiological mechanisms underlying functional responses. Practical implementations of biological-EWS at farming sites are then discussed.

scholarly journals Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets

2020 ◽  
Author(s):  
Brian T. Castle ◽  
Carissa Dock ◽  
Mahya Hemmat ◽  
Susan Kline ◽  
Christopher Tignanelli ◽  
...  
Keyword(s):  
Viral Entry ◽  
Empirical Process ◽  
Biophysical Model ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Viral Production ◽  
Virion Production ◽  
Highly Sensitive ◽  
Antiviral Targets ◽  
Enormous Number

AbstractEffective therapies for COVID-19 are urgently needed. Presently there are more than 800 COVID-19 clinical trials globally, many with drug combinations, resulting in an empirical process with an enormous number of possible combinations. To identify the most promising potential therapies, we developed a biophysical model for the SARS-CoV-2 viral cycle and performed a sensitivity analysis for individual model parameters and all possible pairwise parameter changes (162 = 256 possibilities). We found that model-predicted virion production is fairly insensitive to changes in most viral entry, assembly, and release parameters, but highly sensitive to some viral transcription and translation parameters. Furthermore, we found a cooperative benefit to pairwise targeting of transcription and translation, predicting that combined targeting of these processes will be especially effective in inhibiting viral production.

scholarly journals Mathematical modelling of human P2X-mediated plasma membrane electrophysiology and calcium dynamics in microglia

2021 ◽  
Vol 17 (11) ◽  
pp. e1009520
Author(s):  
Alireza Poshtkohi ◽  
John Wade ◽  
Liam McDaid ◽  
Junxiu Liu ◽  
Mark Dallas ◽  
...  
Keyword(s):  
Plasma Membrane ◽  
Purinergic Receptors ◽  
Signal Transduction Pathway ◽  
Cytosolic Calcium ◽  
Biophysical Model ◽  
Model Parameters ◽  
State Variables ◽  
Human Microglia ◽  
Whole Cell Patch Clamp ◽  
Intracellular Ca

Regulation of cytosolic calcium (Ca2+) dynamics is fundamental to microglial function. Temporal and spatial Ca2+ fluxes are induced from a complicated signal transduction pathway linked to brain ionic homeostasis. In this paper, we develop a novel biophysical model of Ca2+ and sodium (Na+) dynamics in human microglia and evaluate the contribution of purinergic receptors (P2XRs) to both intracellular Ca2+ and Na+ levels in response to agonist/ATP binding. This is the first comprehensive model that integrates P2XRs to predict intricate Ca2+ and Na+ transient responses in microglia. Specifically, a novel compact biophysical model is proposed for the capture of whole-cell patch-clamp currents associated with P2X4 and P2X7 receptors, which is composed of only four state variables. The entire model shows that intricate intracellular ion dynamics arise from the coupled interaction between P2X4 and P2X7 receptors, the Na+/Ca2+ exchanger (NCX), Ca2+ extrusion by the plasma membrane Ca2+ ATPase (PMCA), and Ca2+ and Na+ leak channels. Both P2XRs are modelled as two separate adenosine triphosphate (ATP) gated Ca2+ and Na+ conductance channels, where the stoichiometry is the removal of one Ca2+ for the hydrolysis of one ATP molecule. Two unique sets of model parameters were determined using an evolutionary algorithm to optimise fitting to experimental data for each of the receptors. This allows the proposed model to capture both human P2X7 and P2X4 data (hP2X7 and hP2X4). The model architecture enables a high degree of simplicity, accuracy and predictability of Ca2+ and Na+ dynamics thus providing quantitative insights into different behaviours of intracellular Na+ and Ca2+ which will guide future experimental research. Understanding the interactions between these receptors and other membrane-bound transporters provides a step forward in resolving the qualitative link between purinergic receptors and microglial physiology and their contribution to brain pathology.

scholarly journals Scaling-Up Conservation Agriculture Production System with Drip Irrigation by Integrating MCE Technique and the APEX Model

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2007 ◽  
Author(s):  
Tewodros Assefa ◽  
Manoj Jha ◽  
Abeyou W. Worqlul ◽  
Manuel Reyes ◽  
Seifu Tilahun
Keyword(s):  
Crop Yield ◽  
Drip Irrigation ◽  
Production System ◽  
Conservation Agriculture ◽  
Scaling Up ◽  
Crop Productivity ◽  
Groundwater Potential ◽  
Crop Model ◽  
Biophysical Model ◽  
Model Parameters

The conservation agriculture production system (CAPS) approach with drip irrigation has proven to have the potential to improve water management and food production in Ethiopia. A method of scaling-up crop yield under CAPS with drip irrigation is developed by integrating a biophysical model: APEX (agricultural policy environmental eXtender), and a Geographic Information System (GIS)-based multi-criteria evaluation (MCE) technique. Topography, land use, proximity to road networks, and population density were considered in identifying potentially irrigable land. Weather and soil texture data were used to delineate unique climate zones with similar soil properties for crop yield simulation using well-calibrated crop model parameters. Crops water demand for the cropping periods was used to determine groundwater potential for irrigation. The calibrated APEX crop model was then used to predict crop yield across the different climatic and soil zones. The MCE technique identified about 18.7 Mha of land (16.7% of the total landmass) as irrigable land in Ethiopia. Oromia has the highest irrigable land in the nation (35.4% of the irrigable land) when compared to other regional states. Groundwater could supply a significant amount of the irrigable land for dry season production under CAPS with drip irrigation for the various vegetables tested at the experimental sites with about 2.3 Mha, 3.5 Mha, 1.6 Mha, and 1.4 Mha of the irrigable land available to produce garlic, onion, cabbage, and tomato, respectively. When comparing regional states, Oromia had the highest groundwater potential (40.9% of total potential) followed by Amhara (20%) and Southern Nations, Nationalities, and Peoples (16%). CAPS with drip irrigation significantly increased groundwater potential for irrigation when compared to CTPS (conventional tillage production system) with traditional irrigation practice (i.e., 0.6 Mha under CTPS versus 2.2 Mha under CAPS on average). Similarly, CAPS with drip irrigation depicted significant improvement in crop productivity when compared to CTPS. APEX simulation of the average fresh vegetable yield on the irrigable land under CAPS with drip irrigation ranged from 1.8–2.8 t/ha, 1.4–2.2 t/ha, 5.5–15.7 t/ha, and 8.3–12.9 t/ha for garlic, onion, tomato, and cabbage, respectively. CAPS with drip irrigation technology could improve groundwater potential for irrigation up to five folds and intensify crop productivity by up to three to four folds across the nation.

scholarly journals Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia

2011 ◽  
Vol 1 (3) ◽  
pp. 396-407 ◽  
Author(s):  
Jatin Relan ◽  
Phani Chinchapatnam ◽  
Maxime Sermesant ◽  
Kawal Rhode ◽  
Matt Ginks ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Cardiac Electrophysiology ◽  
Specific Model ◽  
Biophysical Model ◽  
Patient Specific ◽  
Model Parameters ◽  
Tissue Conductivity ◽  
Mapping Procedure ◽  
Macroscopic Models ◽  
Contact Mapping

In order to translate the important progress in cardiac electrophysiology modelling of the last decades into clinical applications, there is a requirement to make macroscopic models that can be used for the planning and performance of the clinical procedures. This requires model personalization, i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Simplified macroscopic models can allow a rapid estimation of the tissue conductivity, but are often unreliable to predict arrhythmias. Conversely, complex biophysical models are more complete and have mechanisms of arrhythmogenesis and arrhythmia sustainibility, but are computationally expensive and their predictions at the organ scale still have to be validated. We present a coupled personalization framework that combines the power of the two kinds of models while keeping the computational complexity tractable. A simple eikonal model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell–Schaeffer (MS) model. Additional parameters related to action potential duration restitution curves for the tissue are further estimated for the MS model. This framework is applied to a clinical dataset derived from a hybrid X-ray/magnetic resonance imaging and non-contact mapping procedure on a patient with heart failure. This personalized MS model is then used to perform an in silico simulation of a ventricular tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling in order to both assess the risk of VT for a given patient and also to plan a potential subsequent radio-frequency ablation strategy to treat VT.

scholarly journals Towards Biophysical Markers of Depression Vulnerability

2021 ◽  
Author(s):  
D.A. Pinotsis ◽  
S. Fitzgerald ◽  
C. See ◽  
A. Sementsova ◽  
A. S. Widge
Keyword(s):  
Feature Space ◽  
Neural Model ◽  
Biophysical Model ◽  
Model Parameters ◽  
Biophysical Parameters ◽  
Biophysical Models ◽  
Classical Statistics ◽  
Heterogeneity Variance ◽  
Brain Data ◽  
Low Dimensional

AbstractA major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. We constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Instrumental and Ideological Union Commitment: Longitudinal Assessment of Construct Validity

2001 ◽  
Vol 17 (2) ◽  
pp. 98-111 ◽  
Author(s):  
Anders Sjöberg ◽  
Magnus Sverke
Keyword(s):  
Construct Validity ◽  
Temporal Stability ◽  
Strong Support ◽  
Internal Consistency Reliability ◽  
Measurement Model ◽  
Two Dimensions ◽  
Model Parameters ◽  
Longitudinal Assessment ◽  
Union Commitment ◽  
Union Participation

Summary: Previous research has identified instrumentality and ideology as important aspects of member attachment to labor unions. The present study evaluated the construct validity of a scale designed to reflect the two dimensions of instrumental and ideological union commitment using a sample of 1170 Swedish blue-collar union members. Longitudinal data were used to test seven propositions referring to the dimensionality, internal consistency reliability, and temporal stability of the scale as well as postulated group differences in union participation to which the scale should be sensitive. Support for the hypothesized factor structure of the scale and for adequate reliabilities of the dimensions was obtained and was also replicated 18 months later. Tests for equality of measurement model parameters and test-retest correlations indicated support for the temporal stability of the scale. In addition, the results were consistent with most of the predicted differences between groups characterized by different patterns of change/stability in union participation status. The study provides strong support for the construct validity of the scale and indicates that it can be used in future theory testing on instrumental and ideological union commitment.

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