scholarly journals Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data

2021 ◽  
Vol 18 (181) ◽  
pp. 20210478
Author(s):  
Peter J. Gawthrop ◽  
Michael Pan ◽  
Edmund J. Crampin
Keyword(s):  
Dynamic Models ◽  
System Development ◽  
Kinetic Modelling ◽  
Simulation Models ◽  
Bond Graph ◽  
Model Parameters ◽  
Biomolecular Systems ◽  
Genome Wide ◽  
Estimation Of Model Parameters ◽  
Genome Scale

Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.

scholarly journals Modular Dynamic Biomolecular Modelling: The Unification of Stoichiometry, Thermodynamics, Kinetics and Data

2021 ◽  
Author(s):  
Peter J. Gawthrop ◽  
Michael Pan ◽  
Edmund J. Crampin
Keyword(s):  
Dynamic Models ◽  
System Development ◽  
Kinetic Modelling ◽  
Simulation Models ◽  
Bond Graph ◽  
Model Parameters ◽  
Biomolecular Systems ◽  
Genome Wide ◽  
Estimation Of Model Parameters ◽  
Genome Scale

AbstractRenewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermo-dynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of E. coli and published experimental data.

scholarly journals Connecting mathematical models to genomes: joint estimation of model parameters and genome-wide marker effects on these parameters

2020 ◽  
Vol 36 (10) ◽  
pp. 3169-3176 ◽  
Author(s):  
Akio Onogi
Keyword(s):  
Mathematical Models ◽  
R Package ◽  
Joint Estimation ◽  
Supplementary Information ◽  
Joint Analysis ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Statistical Framework ◽  
Genome Wide ◽  
Estimation Of Model Parameters

Abstract Motivation Parameters of mathematical models used in biology may be genotype-specific and regarded as new traits. Therefore, an accurate estimation of these parameters and the association mapping on the estimated parameters can lead to important findings regarding the genetic architecture of biological processes. In this study, a statistical framework for a joint analysis (JA) of model parameters and genome-wide marker effects on these parameters was proposed and evaluated. Results In the simulation analyses based on different types of mathematical models, the JA inferred the model parameters and identified the responsible genomic regions more accurately than the independent analysis (IA). The JA of real plant data provided interesting insights into photosensitivity, which were uncovered by the IA. Availability and implementation The statistical framework is provided by the R package GenomeBasedModel available at https://github.com/Onogi/GenomeBasedModel. All R and C++ scripts used in this study are also available at the site. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome wide effects of oleic acid on cultured bovine granulosa cells: evidence for the activation of pathways favoring folliculo-luteal transition

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Vengala Rao Yenuganti ◽  
Dirk Koczan ◽  
Jens Vanselow
Keyword(s):  
Oleic Acid ◽  
Granulosa Cells ◽  
System Development ◽  
Circulatory System ◽  
Bioinformatic Analysis ◽  
Negative Energy ◽  
Tissue Remodelling ◽  
Gonadotropin Secretion ◽  
Genome Wide

Abstract Background Metabolic stress, as negative energy balance on one hand or obesity on the other hand can lead to increased levels of free fatty acids in the plasma and follicular fluid of animals and humans. In an earlier study, we showed that increased oleic acid (OA) concentrations affected the function of cultured bovine granulosa cells (GCs). Here, we focus on genome wide effects of increased OA concentrations. Results Our data showed that 413 genes were affected, of which 197 were down- and 216 up-regulated. Specifically, the expression of FSH-regulated functional key genes, CCND2, LHCGR, INHA and CYP19A1 and 17-β-estradiol (E2) production were reduced by OA treatment, whereas the expression of the fatty acid transporter CD36 was increased and the morphology of the cells was changed due to lipid droplet accumulation. Bioinformatic analysis revealed that associated pathways of the putative upstream regulators “FSH” and “Cg (choriogonadotropin)” were inhibited and activated, respectively. Down-regulated genes are over-represented in GO terms “reproductive structure/system development”, “ovulation cycle process”, and “(positive) regulation of gonadotropin secretion”, whereas up-regulated genes are involved in “circulatory system development”, “vasculature development”, “angiogenesis” or “extracellular matrix/structure organization”. Conclusions From these data we conclude that besides inhibiting GC functionality, increased OA levels seemingly promote angiogenesis and tissue remodelling, thus suggestively initiating a premature fulliculo-luteal transition. In vivo this may lead to impeded folliculogenesis and ovulation, and cause sub-fertility.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

scholarly journals Mixture of Inverse Weibull and Lognormal Distributions: Properties, Estimation, and Illustration

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
K. S. Sultan ◽  
A. S. Al-Moisheer
Keyword(s):  
Information Matrix ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Component Mixture ◽  
Data Set ◽  
Hazard Functions ◽  
Lognormal Distributions ◽  
Proposed Model ◽  
Estimation Of Model Parameters

We discuss the two-component mixture of the inverse Weibull and lognormal distributions (MIWLND) as a lifetime model. First, we discuss the properties of the proposed model including the reliability and hazard functions. Next, we discuss the estimation of model parameters by using the maximum likelihood method (MLEs). We also derive expressions for the elements of the Fisher information matrix. Next, we demonstrate the usefulness of the proposed model by fitting it to a real data set. Finally, we draw some concluding remarks.

A genome-wide screen uncovers multiple roles for mitochondrial nucleoside diphosphate kinase D in inflammasome activation

2021 ◽  
Vol 14 (694) ◽  
pp. eabe0387
Author(s):  
Orna Ernst ◽  
Jing Sun ◽  
Bin Lin ◽  
Balaji Banoth ◽  
Michael G. Dorrington ◽  
...  
Keyword(s):  
Nucleoside Diphosphate Kinase ◽  
Multiple Roles ◽  
Metabolic Reprogramming ◽  
Inflammasome Activation ◽  
Ros Production ◽  
Genome Wide ◽  
A Genome ◽  
Mouse Macrophages ◽  
Genome Scale ◽  
Mitochondrial Dna Synthesis

Noncanonical inflammasome activation by cytosolic lipopolysaccharide (LPS) is a critical component of the host response to Gram-negative bacteria. Cytosolic LPS recognition in macrophages is preceded by a Toll-like receptor (TLR) priming signal required to induce transcription of inflammasome components and facilitate the metabolic reprograming that fuels the inflammatory response. Using a genome-scale arrayed siRNA screen to find inflammasome regulators in mouse macrophages, we identified the mitochondrial enzyme nucleoside diphosphate kinase D (NDPK-D) as a regulator of both noncanonical and canonical inflammasomes. NDPK-D was required for both mitochondrial DNA synthesis and cardiolipin exposure on the mitochondrial surface in response to inflammasome priming signals mediated by TLRs, and macrophages deficient in NDPK-D had multiple defects in LPS-induced inflammasome activation. In addition, NDPK-D was required for the recruitment of TNF receptor–associated factor 6 (TRAF6) to mitochondria, which was critical for reactive oxygen species (ROS) production and the metabolic reprogramming that supported the TLR-induced gene program. NDPK-D knockout mice were protected from LPS-induced shock, consistent with decreased ROS production and attenuated glycolytic commitment during priming. Our findings suggest that, in response to microbial challenge, NDPK-D–dependent TRAF6 mitochondrial recruitment triggers an energetic fitness checkpoint required to engage and maintain the transcriptional program necessary for inflammasome activation.

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.

scholarly journals Using Simulation and Budget Models to Scale-Up Nitrogen Leaching from Field to Region in Canada

2001 ◽  
Vol 1 ◽  
pp. 699-706 ◽  
Author(s):  
E.C. Huffman ◽  
J.Y. Yang ◽  
S. Gameda ◽  
R. de Jong
Keyword(s):  
Dynamic Models ◽  
Scale Up ◽  
Simulation Models ◽  
Measured Data ◽  
Weather Data ◽  
Biophysical Modeling ◽  
Mineral N ◽  
Policy Model ◽  
Field Level ◽  
Time Of Year

Efforts are underway at Agriculture and Agri-Food Canada (AAFC) to develop an integrated, nationally applicable, socioeconomic/biophysical modeling capability in order to predict the environmental impacts of policy and program scenarios. This paper outlines our Decision Support System (DSS), which integrates the IROWCN (Indicator of the Risk of Water Contamination by Nitrogen) index with the agricultural policy model CRAM (Canadian Regional Agricultural Model) and presents an outline of our methodology to provide independent assessments of the IROWCN results through the use of nitrogen (N) simulation models in select, data-rich areas. Three field-level models — DSSAT, N_ABLE, and EPIC — were evaluated using local measured data. The results show that all three dynamic models can be used to simulate biomass, grain yield, and soil N dynamics at the field level; but the accuracy of the models differ, suggesting that models need to be calibrated using local measured data before they are used in Canada. Further simulation of IROWCN in a maize field using N_ABLE showed that soil-mineral N levels are highly affected by the amount of fertilizer N applied and the time of year, meaning that fertilizer and manure N applications and weather data are crucial for improving IROWCN. Methods of scaling-up simulated IROWCN from field-level to soil-landscape polygons and CRAM regions are discussed.

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