Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk

Abstract Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

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Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

Genes ◽

10.3390/genes9080409 ◽

2018 ◽

Vol 9 (8) ◽

pp. 409 ◽

Cited By ~ 4

Author(s):

Ashley Teufel ◽

Andrew Ritchie ◽

Claus Wilke ◽

David Liberles

Keyword(s):

Protein Sequences ◽

Original Model ◽

Mechanistic Modeling ◽

Mathematical Framework ◽

Conditional Probabilities ◽

Modeling Framework ◽

Mutational Pressure ◽

Generative Process ◽

Different Types ◽

Selection Framework

When mutational pressure is weak, the generative process of protein evolution involves explicit probabilities of mutations of different types coupled to their conditional probabilities of fixation dependent on selection. Establishing this mechanistic modeling framework for the detection of selection has been a goal in the field of molecular evolution. Building on a mathematical framework proposed more than a decade ago, numerous methods have been introduced in an attempt to detect and measure selection on protein sequences. In this review, we discuss the structure of the original model, subsequent advances, and the series of assumptions that these models operate under.

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Mechanistic Multilayer Quantitative Model for Nonlinear Pharmacokinetics, Target Occupancy and Pharmacodynamics (PK/TO/PD) Relationship of D-Amino Acid Oxidase Inhibitor, TAK-831 in Mice

Pharmaceutical Research ◽

10.1007/s11095-020-02893-x ◽

2020 ◽

Vol 37 (8) ◽

Author(s):

Tomoki Yoneyama ◽

Sho Sato ◽

Andy Sykes ◽

Rosa Fradley ◽

Stuart Stafford ◽

...

Keyword(s):

Amino Acid ◽

Dose Range ◽

Quantitative Model ◽

Clinical Development ◽

Mechanistic Modeling ◽

Binding Kinetics ◽

Acid Oxidase ◽

Brain Distribution ◽

Modeling Framework ◽

Amino Acid Oxidase

Abstract Purpose TAK-831 is a highly selective and potent inhibitor of D-amino acid oxidase (DAAO) currently under clinical development for schizophrenia. In this study, a mechanistic multilayer quantitative model that parsimoniously connects pharmacokinetics (PK), target occupancy (TO) and D-serine concentrations as a pharmacodynamic (PD) readout was established in mice. Methods PK, TO and PD time-profiles were obtained in mice and analyzed by mechanistic binding kinetics model connected with an indirect response model in a step wise fashion. Brain distribution was investigated to elucidate a possible mechanism driving the hysteresis between PK and TO. Results The observed nonlinear PK/TO/PD relationship was well captured by mechanistic modeling framework within a wide dose range of TAK-831 in mice. Remarkably different brain distribution was observed between target and reference regions, suggesting that the target-mediated slow binding kinetics rather than slow penetration through the blood brain barrier caused the observed distinct kinetics between PK and TO. Conclusion A quantitative mechanistic model for concentration- and time-dependent nonlinear PK/TO/PD relationship was established for TAK-831 in mice with accounting for possible rate-determining process. The established mechanistic modeling framework will provide a quantitative means for multilayer biomarker-assisted clinical development in multiple central nervous system indications.

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How does the presence of neural probes affect extracellular potentials?

10.1101/318741 ◽

2018 ◽

Cited By ~ 2

Author(s):

Alessio Paolo Buccino ◽

Miroslav Kuchta ◽

Karoline Horgmo Jæger ◽

Torbjørn Vefferstad Ness ◽

Pierre Berthet ◽

...

Keyword(s):

Extracellular Space ◽

Mechanistic Modeling ◽

Accurate Estimation ◽

Extracellular Potential ◽

Cable Equation ◽

Electrode Arrays ◽

Neural Probes ◽

Modeling Framework ◽

Extracellular Recordings ◽

Extracellular Potentials

AbstractObjectiveMechanistic modeling of neurons is an essential component of computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach to simulation of extracellular neural recordings first computes transmembrane currents using the cable equation and then sums their contribution to model the extracellular potential. This two-step approach relies on the assumption that the extracellular space is an infinite and homogeneous conductive medium, while measurements are performed using neural probes. The main purpose of this paper is to assess to what extent the presence of the neural probes of varying shape and size impacts the extracellular field and how to correct for them.ApproachWe apply a detailed modeling framework allowing explicit representation of the neuron and the probe to study the effect of the probes and thereby estimate the effect of ignoring it. We use meshes with simplified neurons and different types of probe and compare the extracellular action potentials with and without the probe in the extracellular space. We then compare various solutions to account for the probes’ presence and introduce an efficient probe correction method to include the probe effect in modeling of extracellular potentials.Main resultsOur computations show that microwires hardly influence the extracellular electric field and their effect can therefore be ignored. In contrast, Multi-Electrode Arrays (MEAs) significantly affect the extracellular field by magnifying the recorded potential. While MEAs behave similarly to infinite insulated planes, we find that their effect strongly depends on the neuron-probe alignment and probe orientation.SignificanceIgnoring the probe effect might be deleterious in some applications, such as neural localization and parameterization of neural models from extracellular recordings. Moreover, the presence of the probe can improve the interpretation of extracellular recordings, by providing a more accurate estimation of the extracellular potential generated by neuronal models.

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Mechanistic Modeling Framework for Predicting Extreme Battery Response

10.2172/1365459 ◽

2017 ◽

Author(s):

Harry K. Moffat ◽

Anthony S. Geller ◽

R. Kee ◽

S. Allu

Keyword(s):

Mechanistic Modeling ◽

Modeling Framework

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A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009211 ◽

2021 ◽

Vol 17 (7) ◽

pp. e1009211

Author(s):

Bernard Cazelles ◽

Clara Champagne ◽

Benjamin Nguyen-Van-Yen ◽

Catherine Comiskey ◽

Elisabeta Vergu ◽

...

Keyword(s):

Temporal Evolution ◽

Reproduction Number ◽

Transmission Rate ◽

Specific Model ◽

Mechanistic Modeling ◽

Mitigation Strategies ◽

Brownian Diffusion ◽

Third Wave ◽

Time Varying ◽

Modeling Framework

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).

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A novel mechanistic modeling framework for analysis of electrode balancing and degradation modes in commercial lithium-ion cells

Journal of Power Sources ◽

10.1016/j.jpowsour.2017.01.026 ◽

2017 ◽

Vol 343 ◽

pp. 226-236 ◽

Cited By ~ 27

Author(s):

Stefan Schindler ◽

Michael A. Danzer

Keyword(s):

Lithium Ion ◽

Mechanistic Modeling ◽

Modeling Framework ◽

Lithium Ion Cells ◽

Degradation Modes

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Multidimensional Model of Fluid Flow and Heat Transfer in Generation-IV Supercritical Water Reactors

Volume 2: Thermal Hydraulics ◽

10.1115/icone14-89897 ◽

2006 ◽

Cited By ~ 1

Author(s):

Tara Gallaway ◽

Steven P. Antal ◽

Michael Z. Podowski

Keyword(s):

Heat Transfer ◽

Fluid Flow ◽

Supercritical Water ◽

Three Dimensional ◽

Future Generation ◽

Computer Code ◽

Mechanistic Modeling ◽

Analytical Models ◽

Modeling Framework ◽

Flow And Heat Transfer

This paper is concerned with the mechanistic modeling and theoretical/computational analysis of flow and heat transfer in future Generation-IV Supercritical Water Cooled Reactors (SCWR). The issues discussed in the paper include: the development of analytical models of the properties of supercritical water, and the application of full three-dimensional computational modeling framework to simulate fluid flow and heat transfer in SCWRs. Several results of calculations are shown, including the evaluation of water properties (density, specific heat, thermal conductivity, viscosity, and Prandtl number) near the pseudo-critical temperature for various supercritical pressures, and the CFD predictions using the NPHASE computer code. It is demonstrated that the proposed approach is very promising for future mechanistic analyses of SCWR thermal-hydraulics and safety.

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Soil bacterial diversity mediated by microscale aqueous-phase processes across biomes

Nature Communications ◽

10.1038/s41467-019-13966-w ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 3

Author(s):

Samuel Bickel ◽

Dani Or

Keyword(s):

Bacterial Diversity ◽

Aqueous Phase ◽

Soil Type ◽

Bacterial Abundance ◽

Spatial Scales ◽

Mechanistic Modeling ◽

Intermediate Water ◽

Soil Bacterial Diversity ◽

Modeling Framework ◽

Soil Bacterial

AbstractSoil bacterial diversity varies across biomes with potential impacts on soil ecological functioning. Here, we incorporate key factors that affect soil bacterial abundance and diversity across spatial scales into a mechanistic modeling framework considering soil type, carbon inputs and climate towards predicting soil bacterial diversity. The soil aqueous-phase content and connectivity exert strong influence on bacterial diversity for each soil type and rainfall pattern. Biome-specific carbon inputs deduced from net primary productivity provide constraints on soil bacterial abundance independent from diversity. The proposed heuristic model captures observed global trends of bacterial diversity in good agreement with predictions by an individual-based mechanistic model. Bacterial diversity is highest at intermediate water contents where the aqueous phase forms numerous disconnected habitats and soil carrying capacity determines level of occupancy. The framework delineates global soil bacterial diversity hotspots; located mainly in climatic transition zones that are sensitive to potential climate and land use changes.

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