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 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.

Global long-term sub-daily reanalysis of fluvial floods through high-resolution modeling

2020 ◽  
Author(s):  
Yuan Yang ◽  
Ming Pan ◽  
Peirong Lin ◽  
Hylke Beck ◽  
Dai Yamazaki ◽  
...  
Keyword(s):  
High Resolution ◽  
Performance Metrics ◽  
Probability Of Detection ◽  
Infiltration Capacity ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Discharge Performance ◽  
Environmental Consequences ◽  
Learning Skill ◽  
Level Model

<p>Flood is one of the most devastating natural disasters of severe societal, economic, and environmental consequences. Understanding the characteristics of floods, especially at fine spatial and short temporal scales, can be critical for improving forecast and risk management efforts. Due to the limited availability, in-situ observations have been inadequate for meeting the challenges at global extent. Existing global flood modeling efforts also lack the sufficient spatial/temporal resolutions for capturing rapid/local flood events, e.g., those developed in less than a day. Here we implement a carefully-designed modeling framework to reconstruct global river discharge at very high resolution (5-km and 3-hourly for runoff calculation and ~2.94 million river reaches derived from 90-m DEM for river routing) for 40 years (1979-2018). The Variable Infiltration Capacity (VIC) model with calibrated parameters, is coupled with the Routing Application for Parallel computation of Discharge (RAPID), serving as the core of the modeling framework. The state-of-the-art merged precipitation product, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and flowlines vectorized from the MERIT Hydro are used. Pixel-level model calibration and distributional bias correction are performed against global runoff characteristics derived from observations and machine learning. Skill assessments are carried out both globally at daily sale and over contiguous U.S. (CONUS) at 3-hourly scale, using both general discharge performance metrics (Kling-Gupta Efficiency and it three components) and sub-daily flood-specific metrics (probability of detection, false alarm rate, flood volume error, peak magnitude error, timing error, etc.). The work here aims to provide some first-time understanding of local scale rapid flooding over the global domain. We also expect to learn more about the modeling tools developed for analyzing/monitoring fine scale flooding globally – their efficacy and lack thereof, why, and where to improve.</p>

Modeling mercury cycling in the marine environment

2020 ◽  
Author(s):  
Johannes Bieser ◽  
Ute Daewel ◽  
Corinna Schrum
Keyword(s):  
Numerical Models ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Trophic Levels ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Large Variability ◽  
Marine Ecosystem Model ◽  
Set Up ◽  
Fully Coupled

<p>Five decades of Hg science have shown the <strong>tremendous complexity of the global Hg cycle</strong>. Yet, the pathways that lead from anthropogenic Hg emissions to MeHg exposure through sea food are not fully comprehended. Moreover, the observed amount of MeHg in fish exhibits a large temporal and spatial variability that we cannot predict yet. A key issue is that fully speciated Hg measurements in the ocean are difficult to perform and thus we will never be able to achieve a comprehensive spatial and temporal coverage.</p><p>Therefore, we need complex modeling tools that allow us to fill the gaps in the observations and to predict future changes in the system under changing external drivers (emissions, climate change, ecosystem changes). Numerical models have a long history in Hg research, but so far have virtually only addressed inorganic Hg cycling in atmosphere and oceans.</p><p>Here we present a novel 3d-hydrodynamic mercury modeling framework based on fully coupled compartmental models including atmosphere, ocean, and ecosystem. The generalized high resolution model has been set up for European shelf seas and was used to model the transition zone from estuaries to the open ocean. Based on this model we present our findings on intra- and inter-annual dynamics and variability of mercury speciation and distribution in a coastal ocean. Moreover, we present the first results on the dynamics of mercury bio-accumulation from a fully coupled marine ecosystem model. Most importantly, the model is able to reproduce the large variability in methylmercury accumulation in higher trophic levels.</p>

Use of Multiphoton Intravital Imaging and a Multiscale Computational Model of Thrombus Development to Study the Role of FVII in Thrombogenesis

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 391-391
Author(s):  
Malgorzata Kamocka ◽  
Zhiliang Xu ◽  
Nan Chen ◽  
Mark Alber ◽  
Elliot D. Rosen
Keyword(s):  
High Resolution ◽  
Computational Model ◽  
Imaging System ◽  
Structural Information ◽  
Cellular Interactions ◽  
Wild Type ◽  
Cellular Potts Model ◽  
Modeling Framework ◽  
Deficient Mice

Abstract Using 2-photon intravital microscopy we have generated high resolution, near real-time 3-dimensional images of a developing thrombus. Following Titanium - Sapphire laser-induced injuries in mouse mesenteric vessels, the developing thrombus was monitored by collecting stacks of confocal images through the developing thrombus. Data were collected in 3 channels for fluorescently labeled platelets, fibrinogen and 70,000 MW dextran. By including fluorescently labeled dextran in the blood we were able to monitor flow (plasma) and not labeled cells (leukocytes and erythrocytes) forming black silhouettes in the plasma. Since each 3-D reconstruction involves a series of scans, we were able to generate approximately 2–3 reconstructions per minute. Thus, the system sacrifices temporal resolution for high resolution structural information revealing the changing, heterogeneous sub-domain structure of the developing thrombus. The imaging system has been used to study the consequences of FVII-deficiency. Following injury, of the luminal surface of the vessel, a thin layer of platelets and fibrin accumulated at the injury site. Unlike injuries in wild-type mice where the thrombus continues to grow, the injuries in FVII deficient mice failed to grow as a result of frequent embolization from the developing structure. Interestingly, in a model of ferric chloride induced injury of the carotid artery, thrombi in FVII deficient mice form large structures capable of reducing flow although, unlike wild type mice, the FVII deficient animals fail to form stable occlusive clots. In parallel with the modified experimental vascular injury model, we have begun development of a computational model of thrombus development. The modeling framework consists of a stochastic and discrete Cellular Potts Model (CPM) to describe platelet and cellular interactions and continuous submodels to describe hydrodynamic and biochemical reactions. Our multiscale model includes the vessel wall, platelets (in resting and activated states), blood cells, coagulation reactions, fibrin formation, and hydrodynamic parameters as components. By comparing the in vivo experimental results with those of simulations varying the concentration of FVII we are able to refine and validate the computational model of thrombogenesis.

scholarly journals A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems

2015 ◽  
Vol 6 ◽  
Author(s):  
Cristiana Gomes de Oliveira Dal'Molin ◽  
Lake-Ee Quek ◽  
Pedro A. Saa ◽  
Lars K. Nielsen
Keyword(s):  
Metabolic Modeling ◽  
Modeling Framework ◽  
Whole Plant ◽  
Genome Scale

scholarly journals Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

2013 ◽  
Vol 163 (2) ◽  
pp. 637-647 ◽  
Author(s):  
E. Grafahrend-Belau ◽  
A. Junker ◽  
A. Eschenroder ◽  
J. Muller ◽  
F. Schreiber ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Metabolic Modeling ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Whole Plant ◽  
Balance Analysis

scholarly journals Engineering Design Tools for Shape Memory Alloy Actuators: CASMART Collaborative Best Practices and Case Studies

2016 ◽  
Author(s):  
Robert W. Wheeler ◽  
Othmane Benafan ◽  
Xiujie Gao ◽  
Frederick T. Calkins ◽  
Zahra Ghanbari ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Best Practices ◽  
Shape Memory ◽  
Case Studies ◽  
Design Parameters ◽  
Solar Array ◽  
Design Tools ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Actuation System

The primary goal of the Consortium for the Advancement of Shape Memory Alloy Research and Technology (CASMART) is to enable the design of revolutionary applications based on shape memory alloy (SMA) technology. In order to help realize this goal and reduce the development time and required experience for the fabrication of SMA actuation systems, several modeling tools have been developed for common actuator types and are discussed herein along with case studies, which highlight the capabilities and limitations of these tools. Due to their ability to sustain high stresses and recover large deformations, SMAs have many potential applications as reliable, lightweight, solid-state actuators. Their advantage over classical actuators can also be further improved when the actuator geometry is modified to fit the specific application. In this paper, three common actuator designs are studied: wires, which are lightweight, low-profile, and easily implemented; springs, which offer actuation strokes upwards of 200% at reduced mechanical loads; and torque tubes, which can provide large actuation forces in small volumes and develop a repeatable zero-load actuation response (known as the two-way shape memory effect). The modeling frameworks, which have been implemented in the design tools, are developed for each of these frequently used SMA actuator types. In order to demonstrate the versatility and flexibility of the presented design tools, as well as validate their modeling framework, several design challenges were completed. These case studies include the design and development of an active hinge for the deployment of a solar array or foldable space structure, an adaptive solar array deployment and positioning system, a passive air temperature controller for the regulation of flow temperatures inside of a jet engine, and a redesign of the Corvette active hatch, which allows for pressure equalization of the car interior. For each of the presented case studies, a prototype or proof-of-concept was fabricated and the experimental results and lessons learned are discussed. This analysis presents a collection of CASMART collaborative best practices in order to allow readers to utilize the available design tools and understand their modeling principles. These design tools, which are based on engineering models, can provide first-order optimal designs and are a basic and efficient method for either demonstrating design feasibility or refining design parameters. Although the design and integration of an SMA-based actuation system always requires application- and environment-specific engineering considerations, common modeling tools can significantly reduce the investment required for actuation system development and provide valuable engineering insight.

scholarly journals Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model ofEscherichia coliDiauxic Growth

mSystems ◽  
2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Antonella Succurro ◽  
Daniel Segrè ◽  
Oliver Ebenhöh
Keyword(s):  
Metabolic Modeling ◽  
Metabolic Model ◽  
Published Data ◽  
Diauxic Shift ◽  
Source Modeling ◽  
Cell Level ◽  
Modeling Framework ◽  
Content Type ◽  
E Coli ◽  
New Perspective

ABSTRACTMicrobes have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. A classical and yet still actively studied example of adaptation to dynamic environments is the diauxic shift ofEscherichia coli, in which cells grow on glucose until its exhaustion and then transition to using previously secreted acetate. Here we tested different hypotheses concerning the nature of this transition by using dynamic metabolic modeling. To reach this goal, we developed an open source modeling framework integrating dynamic models (ordinary differential equation systems) with structural models (metabolic networks) which can take into account the behavior of multiple subpopulations and smooth flux transitions between time points. We used this framework to model the diauxic shift, first with a singleE. colimodel whose metabolic state represents the overall population average and then with a community of two subpopulations, each growing exclusively on one carbon source (glucose or acetate). After introduction of an environment-dependent transition function that determined the balance between subpopulations, our model generated predictions that are in strong agreement with published data. Our results thus support recent experimental evidence that diauxie, rather than a coordinated metabolic shift, would be the emergent pattern of individual cells differentiating for optimal growth on different substrates. This work offers a new perspective on the use of dynamic metabolic modeling to investigate population heterogeneity dynamics. The proposed approach can easily be applied to other biological systems composed of metabolically distinct, interconverting subpopulations and could be extended to include single-cell-level stochasticity.IMPORTANCEEscherichia colidiauxie is a fundamental example of metabolic adaptation, a phenomenon that is not yet completely understood. Further insight into this process can be achieved by integrating experimental and computational modeling methods. We present a dynamic metabolic modeling approach that captures diauxie as an emergent property of subpopulation dynamics inE. colimonocultures. Without fine-tuning the parameters of theE. colicore metabolic model, we achieved good agreement with published data. Our results suggest that single-organism metabolic models can only approximate the average metabolic state of a population, therefore offering a new perspective on the use of such modeling approaches. The open source modeling framework that we provide can be applied to model general subpopulation systems in more-complex environments and can be extended to include single-cell-level stochasticity.

scholarly journals Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2876
Author(s):  
Martha A. Zaidan ◽  
Ola Surakhi ◽  
Pak Lun Fung ◽  
Tareq Hussein
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Meteorological Parameters ◽  
Quality Measurement ◽  
Air Pollutant ◽  
Feed Forward Neural Network ◽  
Modeling Framework ◽  
Concentration Data ◽  
Modeling Tools ◽  
Diurnal Cycles

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.

Mechanism of pyridoxine 5'-phosphate accumulation in PLPBP protein-deficiency

2022 ◽  
Author(s):  
Tomokazu Ito ◽  
Honoka Ogawa ◽  
Hisashi Hemmi ◽  
Diana M. Downs ◽  
Tohru Yoshimura
Keyword(s):  
De Novo ◽  
Binding Protein ◽  
Vitamin B ◽  
Wild Type ◽  
Salvage Pathway ◽  
Intracellular Accumulation ◽  
Pyridoxal Kinase ◽  
Genetic Studies ◽  
E Coli ◽  
Metabolic Systems

The pyridoxal 5'-phosphate (PLP)-binding protein (PLPBP) plays an important role in vitamin B 6 homeostasis. Loss of this protein in organisms such as Escherichia coli and humans disrupts the vitamin B 6 pool and induces intracellular accumulation of pyridoxine 5'-phosphate (PNP), which is normally undetectable in wild-type cells. The accumulated PNP could affect diverse metabolic systems through inhibition of some PLP-dependent enzymes. In this study, we investigated the as yet unclear mechanism of intracellular accumulation of PNP by the loss of PLPBP protein encoded by yggS in E. coli . Genetic studies using several PLPBP-deficient strains of E. coli lacking known enzyme(s) in the de novo or salvage pathway of vitamin B 6 , which includes pyridoxine (amine) 5'-phosphate oxidase (PNPO), PNP synthase, pyridoxal kinase, and pyridoxal reductase, demonstrated that neither the flux from the de novo pathway nor the salvage pathway solely contributed to the PNP accumulation caused by the PLPBP mutation. Studies with the strains lacking both PLPBP and PNPO suggested that PNP shares the same pool with PMP, and showed that PNP levels are impacted by PMP levels and vice versa . We show that disruption of PLPBP lead to perturb PMP homeostasis, which may result in PNP accumulation in the PLPBP-deficient strains. Importance A PLP-binding protein PLPBP from the conserved COG0325 family has recently been recognized as a key player in vitamin B 6 homeostasis in various organisms. Loss of PLPBP disrupts vitamin B 6 homeostasis and perturbs diverse metabolisms, including amino acid and α-keto acid metabolism. Accumulation of PNP is a characteristic phenotype of the PLPBP deficiency and is suggested to be a potential cause of the pleiotropic effects, but the mechanism of the PNP accumulation was poorly understood. In this study, we show that fluxes for PNP synthesis/metabolism are not responsible for the accumulation of PNP. Our results indicate that PLPBP is involved in the homeostasis of pyridoxamine 5'-phosphate, and its disruption may lead to the accumulation of PNP in PLPBP-deficiency.

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