scholarly journals Improved constraints increase the predictivity and applicability of a linear programming-based dynamic metabolic modeling framework

2021 ◽  
Author(s):  
Justin Y Lee ◽  
Mark P Styczynski
Keyword(s):  
Experimental Data ◽  
Linear Programming ◽  
Biological Systems ◽  
Metabolic Modeling ◽  
Training Data ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Regulatory Constraints ◽  
Simplifying Assumptions ◽  
Optimal Approach

Background: Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, LK-DFBA, that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework's success are the linear kinetics and regulatory constraints imposed on the system. Here, we present improvements to these constraints to improve the predictivity of LK-DFBA models and their applicability to biological systems. Method: Three new constraint approaches were created to better model interactions between metabolites and the reactions they regulate. These new approaches (and the original LK-DFBA approach) were tested on several synthetic and biological systems to determine their performance when using both noiseless and noisy data. To validate our framework, we compared experimental data to metabolite dynamics predicted by LK-DFBA. Results: There was no single optimal type of constraints across all synthetic and biological systems; rather, any one of the four approaches could perform best for a given system. The optimal approach for fitting to wildtype data of a given model was consistently the best approach when predicting new phenotypes for that model. Furthermore, many of LK-DFBA's predictions qualitatively agreed with experimental data. Conclusions: LK-DFBA can be improved by using several kinetics constraint approaches, with the ideal one selected based on wild-type training data. LK-DFBA's ability to predict metabolic trends in experimental data further supports its potential for modeling metabolite dynamics in systems of all sizes.

scholarly journals Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Mark P. Styczynski
Keyword(s):  
Linear Programming ◽  
Metabolic Modeling ◽  
Wild Type ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Dynamic Flux Balance Analysis ◽  
Regulatory Constraints ◽  
Linear Kinetics ◽  
Metabolic Systems ◽  
Constraint Approach

AbstractCurrent metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework’s success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.

scholarly journals Ordinary Differential Equations in Cancer Biology

2016 ◽  
Author(s):  
Margaret P. Chapman ◽  
Claire J. Tomlin
Keyword(s):  
Experimental Data ◽  
Differential Equations ◽  
Qualitative Analysis ◽  
Ordinary Differential Equations ◽  
Cancer Biology ◽  
Biological Systems ◽  
Short Paper ◽  
Ode Model ◽  
Modeling Framework ◽  
Biological Insight

AbstractOrdinary differential equations (ODEs) provide a classical framework to model the dynamics of biological systems, given temporal experimental data. Qualitative analysis of the ODE model can lead to further biological insight and deeper understanding compared to traditional experiments alone. Simulation of the model under various perturbations can generate novel hypotheses and motivate the design of new experiments. This short paper will provide an overview of the ODE modeling framework, and present examples of how ODEs can be used to address problems in cancer biology.

Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

Comparison of soot models for reacting sprays in diesel engine-like conditions

2021 ◽  
pp. 095440702110154
Author(s):  
Pavan Prakash Duvvuri ◽  
Rajesh Kumar Shrivastava ◽  
Sheshadri Sreedhara
Keyword(s):  
Experimental Data ◽  
Volume Fraction ◽  
Soot Volume Fraction ◽  
Modeling Framework ◽  
Aerosol Dynamics ◽  
Significant Difference ◽  
Polycyclic Aromatic ◽  
Order Of Magnitude ◽  
Detailed Kinetics ◽  
Soot Model

Stringent emission legislations and growing health concerns have contributed to the evolution of soot modeling in diesel engines from simple empirical relations to methods involving detailed kinetics and complex aerosol dynamics. In this paper, four different soot models have been evaluated for the high temperature, high pressure combusting dodecane spray cases of engine combustion network (ECN) spray A which mimics engine-relevant conditions. The soot models considered include an empirical, a multistep, a method of moments based, and a discrete sectional method soot model. Two experimental cases with ambient oxygen volume of 21% and 15% have been modeled. A good agreement between simulations and experiments for vapor penetration and heat release rate has been obtained. Quasi-steady soot volume fraction contours for the four soot models have been compared with experiments. Contours of the species and source terms involved in soot modeling have also been compared for a better understanding of soot processes. The empirical soot model results in higher magnitude and spread of soot due to a lack of modeling framework for oxidation through OH species. Among the four models studied, the multistep soot model has been observed to provide the most promising agreement with the experimental data in terms of distribution of soot and location of peak soot volume fraction. Due to a two-way coupling of soot models, the detailed models predict an upstream location for soot as compared to the multi-step soot model which is one way coupled. A significant difference (of an order of magnitude) in the concentration of PAH (polycyclic aromatic hydrocarbons) precursor between multistep and detailed soot models has been observed because of precursor consumption due to the coupling of detailed soot models with chemical kinetics. It is recommended that kinetic schemes, especially those concerning PAH, be validated with experimental data with a kinetics-coupled soot model.

scholarly journals Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

2021 ◽  
Vol 13 (4) ◽  
pp. 631
Author(s):  
Kyle D. Woodward ◽  
Narcisa G. Pricope ◽  
Forrest R. Stevens ◽  
Andrea E. Gaughan ◽  
Nicholas E. Kolarik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Resource Use ◽  
Training Data ◽  
Systems Science ◽  
Participatory Mapping ◽  
Land Use Patterns ◽  
Modeling Framework ◽  
Use Patterns ◽  
Human Habitation

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.

scholarly journals Measuring cost and affordability of current vs. healthy diets in Argentina: an application of linear programming and the INFORMAS protocol

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gabriel Giacobone ◽  
Maria Victoria Tiscornia ◽  
Leila Guarnieri ◽  
Luciana Castronuovo ◽  
Sally Mackay ◽  
...  
Keyword(s):  
Linear Programming ◽  
Energy Intake ◽  
Current Model ◽  
Food Product ◽  
Poor Households ◽  
Average Household Income ◽  
Healthy Diets ◽  
Diet Cost ◽  
The Difference ◽  
Optimal Approach

Abstract Background Food cost and affordability is one of the main barriers to improve the nutritional quality of diets of the population. However, in Argentina, where over 60% of adults and 40% of children and adolescents are overweight or obese, little is known about the difference in cost and affordability of healthier diets compared to ordinary, less healthy ones. Methods We implemented the “optimal approach” proposed by the International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support (INFORMAS). We modelled the current diet and two types of healthy diets, one equal in energy with the current diet and one 6.3% lower in energy by linear programming. Cost estimations were performed by collecting food product prices and running a Monte Carlo simulation (10,000 iterations) to obtain a range of costs for each model diet. Affordability was measured as the percentage contribution of diet cost vs. average household income in average, poor and extremely poor households and by income deciles. Results On average, households must spend 32% more money on food to ensure equal energy intake from a healthy diet than from a current model diet. When the energy intake target was reduced by 6.3%, the difference in cost was 22%. There are no reasonably likely situations in which any of these healthy diets could cost less or the same than the current unhealthier one. Over 50% of households would be unable to afford the modelled healthy diets, while 40% could not afford the current diet. Conclusions Differential cost and affordability of healthy vs. unhealthy diets are germane to the design of effective public policies to reduce obesity and NCDs in Argentina. It is necessary to implement urgent measures to transform the obesogenic environment, making healthier products more affordable, available and desirable, and discouraging consumption of nutrient-poor, energy-rich foods.

Thermal Analysis of the Hot Dip-Coating Process

1993 ◽  
Vol 115 (2) ◽  
pp. 453-460 ◽  
Author(s):  
Hui Zhang ◽  
M. Karim Moallemi ◽  
Sunil Kumar
Keyword(s):  
Experimental Data ◽  
Thermal Analysis ◽  
Approximate Solution ◽  
Dip Coating ◽  
Solution Procedure ◽  
Heat Diffusion ◽  
Coating Process ◽  
Parametric Effects ◽  
Simplifying Assumptions ◽  
Axial Heat

In this study a thermal analysis is performed on the hot dip-coating process where solidification of metal occurs on a bar moving through a finite molten bath. A continuum model is considered that accounts for important transport mechanisms such as axial heat diffusion, buoyancy, and shear-induced melt motion in the bath. A numerical solution procedure is developed, and its predictions are compared with those of an analytical approximate solution, as well as available experimental data. The predictions of the numerical scheme are in good agreement with the experimental data. The results of the approximate solution, however, exhibit significant disagreement with the data, which is attributed to the simplifying assumptions used in its development. Parametric effects of the bath geometry, and initial and boundary temperatures and solid velocity, as characterized by the Reynolds number, Grashof number, and Stefan numbers, are presented.

scholarly journals Permeability of Sand-Clay Mixtures

2010 ◽  
Vol 56 (4) ◽  
pp. 299-320 ◽  
Author(s):  
G. Kacprzak ◽  
C. Boutin ◽  
T. Doanh
Keyword(s):  
Experimental Data ◽  
Actual State ◽  
Load Step ◽  
Qualitative And Quantitative ◽  
Experimental Data Point ◽  
Saturated Clay ◽  
Simplifying Assumptions ◽  
Data Point ◽  
Homogenization Methods

Abstract This study deals with the behavior of composite blends constituted of rigid and impervious grains included in saturated clay paste of kaolin, considered as permeable and deformable. Permeability tests performed during standard oedometr tests (before each load step) highlight the key role of the original and actual state of the clay paste, and show the existence of a threshold of sand grain concentration above which a structuring effect influences its permeability. In the light of these experiments some usual homogenization methods (with simplifying assumptions to make the problem manageable) are considered in order to model the mixture permeability. Qualitative and quantitative comparisons with experimental data point out their respective domain of interest and limitations of such approaches

scholarly journals Deep learning of stochastic contagion dynamics on complex networks

2020 ◽  
Author(s):  
Charles Murphy ◽  
Edward Laurence ◽  
Antoine Allard
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Time Series Data ◽  
Training Data ◽  
Series Data ◽  
Neural Network Architecture ◽  
Complementary Approach ◽  
Quantitative Accuracy ◽  
Simplifying Assumptions ◽  
Effective Models

Abstract Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically and/or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where the effective local mechanisms governing a dynamic are learned automatically from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using stochastic contagion dynamics of increasing complexity on static and temporal networks. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

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