Particle-based simulation reveals macromolecular crowding effects on the Michaelis-Menten mechanism

Weilandt, Daniel; Hatzimanikatis, Vassily

doi:10.1101/429316

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…Therefore, it makes sense to assume a "hybrid model," which could contain both M-M kinetics and some empirical model elements, as the best approximation to the kinetic data at hand. Similar models for multifactor response surfaces can be seen in Bogacka et al (2017), Moyano et al (2018), Strouwen et al (2019) and Weilandt and Hatzimanikatis (2019).…”

Section: Alternative Modelsmentioning

confidence: 62%

Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics

Huang

Gilmour

Mylona

et al. 2020

JABES

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Biochemical mechanism studies often assume statistical models derived from Michaelis–Menten kinetics, which are used to approximate initial reaction rate data given the concentration level of a single substrate. In experiments dealing with industrial applications, however, there are typically a wide range of kinetic profiles where more than one factor is controlled. We focus on optimal design of such experiments requiring the use of multifactor hybrid nonlinear models, which presents a considerable computational challenge. We examine three different candidate models and search for tailor-made D- or weighted-A-optimal designs that can ensure the efficiency of nonlinear least squares estimation. We also study a compound design criterion for discriminating between two candidate models, which we recommend for design of advanced kinetic studies. Supplementary materials accompanying this paper appear on-line

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Section: Alternative Modelsmentioning

confidence: 62%

Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics

Huang

Gilmour

Mylona

et al. 2020

JABES

View full text Add to dashboard Cite

show abstract

“…This emphasizes the significance of accurately determining the kinetic parameters of such important enzymes in order to obtain model responses consistent with the experimental observations. This also implies that we have to consider complex kinetic phenomena such as crowding when modeling kinetic properties of certain enzymes [75].…”

Section: Refinement Of Model Responses To Six Single-gene Knockoutsmentioning

confidence: 99%

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

Tokic

Mišković

Hatzimanikatis

2019

Preprint

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A high tolerance of Pseudomonas putida to toxic compounds and its ability to grow on a wide variety of substrates makes it a promising candidate for the industrial production of biofuels and biochemicals. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. In this work, we developed large-scale kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites.We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then applied the redGEM and lumpGEM algorithms to reduce the curated iJN1411 model systematically, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we employed the ORACLE framework to generate populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Overall, we demonstrated the potential and predictive capabilities of developed kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals.

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“…Each cell occupies a volume in space, the biochemical processes in which are well known to experience effects of spatial mechanisms like volume exclusion of macromolecules, the cytoskeleton and organelles, resulting in macromolecular crowding that causes highly non-mass-action reaction rates (8) and anomalous diffusion (9). Recent computational models have sought to describe the spatiotemporal effects on the kinetics of elementary reactions (10), as well as to relate discrete and continuous mathematical descriptions of spatially resolved subcellular reaction kinetics (11). Likewise, models of spatiotemporal multicellular systems in development and disease have shown the significance of spatial mechanisms like diffusive transport and the shape and position of individual cells in angiogenesis (12), polycystic kidney disease (13) and spheroid fusion (14).…”

Section: Introductionmentioning

confidence: 99%

Generating Agent-Based Multiscale Multicellular Spatiotemporal Models from Ordinary Differential Equations of Biological Systems, with Applications in Viral Infection

Sego

Aponte-Serrano

Gianlupi

et al. 2021

Preprint

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The biophysics of an organism span scales from subcellular to organismal and include spatial processes like diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent spatial and stochastic features of a biological system, limiting their insights and applications. Spatial models describe biological systems with spatial information but are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them. In this work we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice-versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters, investigate objects and processes implicitly represented by ODE model terms and parameters, and improve the reproducibility of spatial, stochastic models. We hope to employ our method to generate new ODE model terms from spatiotemporal, multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.Statement of SignificanceOrdinary differential equations (ODEs) are widely used to model and efficiently simulate multicellular systems without explicit spatial information, while spatial models permit explicit spatiotemporal modeling but are mathematically complicated and computationally expensive. In this work we develop a method to generate stochastic, agent-based, multiscale models of multicellular systems with spatial resolution at the cellular level according to non-spatial ODE models. We demonstrate how to directly translate model terms and parameters between ODE and spatial models and apply non-spatial model terms to boundary conditions using examples of viral infection modeling, and show how spatial models can interrogate implicitly represented biophysical mechanisms in non-spatial models. We discuss strategies for co-developing spatial and non-spatial models and reconciling disagreements between them.

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Particle-based simulation reveals macromolecular crowding effects on the Michaelis-Menten mechanism

Cited by 4 publications

References 40 publications

Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics

Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

Generating Agent-Based Multiscale Multicellular Spatiotemporal Models from Ordinary Differential Equations of Biological Systems, with Applications in Viral Infection

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