scholarly journals Bacterial metabolic heterogeneity: From stochastic to deterministic models

2020 ◽  
Vol 17 (5) ◽  
pp. 5120-5133
Author(s):  
Carl Graham ◽  
◽  
Jérôme Harmand ◽  
Sylvie Méléard ◽  
Josué Tchouanti ◽  
...  
Keyword(s):  
Deterministic Models ◽  
Metabolic Heterogeneity

scholarly journals Computation of Single-Cell Metabolite Distributions Using Mixture Models

2020 ◽  
Vol 8 ◽  
Author(s):  
Mona K. Tonn ◽  
Philipp Thomas ◽  
Mauricio Barahona ◽  
Diego A. Oyarzún
Keyword(s):  
Single Cell ◽  
Mixture Models ◽  
Single Cells ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Stochastic Simulations ◽  
Deterministic Models ◽  
Metabolic Heterogeneity ◽  
Cell Expression ◽  
The Impact

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

scholarly journals Computation of single-cell metabolite distributions using mixture models

2020 ◽  
Author(s):  
Mona K. Tonn ◽  
Philipp Thomas ◽  
Mauricio Barahona ◽  
Diego A. Oyarzún
Keyword(s):  
Single Cell ◽  
Mixture Models ◽  
Single Cells ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Stochastic Simulations ◽  
Deterministic Models ◽  
Metabolic Heterogeneity ◽  
Cell Expression ◽  
The Impact

Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

scholarly journals Stochastic modelling reveals mechanisms of metabolic heterogeneity

2019 ◽  
Author(s):  
Mona K. Tonn ◽  
Philipp Thomas ◽  
Mauricio Barahona ◽  
Diego A. Oyarzún
Keyword(s):  
Stochastic Modelling ◽  
Population Based ◽  
Published Data ◽  
Deterministic Models ◽  
Multimodal Distribution ◽  
Growth Variability ◽  
Cellular Physiology ◽  
A Cell ◽  
Complex Forms ◽  
Metabolic Heterogeneity

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.

DETERMINISTIC APPROACH TO WATERSHED MODELING

1971 ◽  
Vol 2 (3) ◽  
pp. 146-166 ◽  
Author(s):  
DAVID A. WOOLHISER
Keyword(s):  
Boundary Conditions ◽  
Watershed Modeling ◽  
Deterministic Approach ◽  
Mass Energy ◽  
Deterministic Models ◽  
Conservation Of Mass ◽  
Hydrologic Process ◽  
Theoretical Structure ◽  
Physically Based ◽  
Initial And Boundary Conditions

Physically-based, deterministic models, are considered in this paper. Physically-based, in that the models have a theoretical structure based primarily on the laws of conservation of mass, energy, or momentum; deterministic in the sense that when initial and boundary conditions and inputs are specified, the output is known with certainty. This type of model attempts to describe the structure of a particular hydrologic process and is therefore helpful in predicting what will happen when some change occurs in the system.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

Ancient Philosophy, Quantum Reality and Management

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Anindo Bhattacharjee
Keyword(s):  
Uncertainty Principle ◽  
Quantum Physics ◽  
Ancient Philosophy ◽  
Physical World ◽  
Scientific Management ◽  
Werner Heisenberg ◽  
Deterministic Models ◽  
Late 19Th Century ◽  
Quantum Reality ◽  
New View

The romanticism of management for numbers, metrics and deterministic models driven by mathematics, is not new. It still exists. This is exactly the problem which classical physicists had in the late 19th century until Werner Heisenberg brought the uncertainty principle and opened the doors of quantum physics that challenged the deterministic view of the physical world mostly driven by the Newtonian view. In this paper, we propose an uncertainty principle of management and then list a set of factors which capture this uncertainty quite well and arrive at a new view of scientific management thought. The new view which we call as the Quantum view of Management (QVM) will be based on the major tenets from the ancient philosophical traditions viz., Jainism, Taoism, Advaita Vedanta, Buddhism, Greek philosophers (like Hereclitus) etc.

Temperature and its measurement

2017 ◽  
Author(s):  
Andrew Clarke
Keyword(s):  
Thermal Equilibrium ◽  
Isotope Fractionation ◽  
Thermodynamic Temperature ◽  
The Other ◽  
Deterministic Models ◽  
Triple Point Of Water ◽  
Temperature Scales ◽  
Classical Thermodynamics ◽  
The Relationship ◽  
Unit Of Temperature

Temperature is that property of a body which determines whether it gains or loses energy in a particular environment. In classical thermodynamics temperature is defined by the relationship between energy and entropy. Temperature can be defined only for a body that is in thermodynamic and thermal equilibrium; whilst organisms do not conform to these criteria, the errors in assuming that they do are generally small. The Celsius and Fahrenheit temperature scales are arbitrary because they require two fixed points, one to define the zero and the other to set the scale. The thermodynamic (absolute) scale of temperature has a natural zero (absolute zero) and is defined by the triple point of water. Its unit of temperature is the Kelvin. The Celsius scale is convenient for much ecological and physiological work, but where temperature is included in statistical or deterministic models, only thermodynamic temperature should be used. Past temperatures can only be reconstructed with the use of proxies, the most important of which are based on isotope fractionation.

scholarly journals Shared and unique metabolic features of the malignant and benign thyroid lesions determined with use of 1H HR MAS NMR spectroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Agnieszka Skorupa ◽  
Mateusz Ciszek ◽  
Ewa Chmielik ◽  
Łukasz Boguszewicz ◽  
Małgorzata Oczko-Wojciechowska ◽  
...  
Keyword(s):  
Nmr Spectra ◽  
Univariate Analysis ◽  
Mas Nmr ◽  
Metabolic Reprogramming ◽  
Benign Lesions ◽  
Chronic Thyroiditis ◽  
Malignant Lesions ◽  
Metabolic Heterogeneity ◽  
Benign Thyroid ◽  
Thyroid Lesions

AbstractThe purpose of this work was to investigate the distinct and common metabolic features of the malignant and benign thyroid lesions in reference to the non-transformed tissue from the contralateral gland (chronic thyroiditis and colloid goiter). 1H HR MAS NMR spectra of 38 malignant lesions, 32 benign lesions and 112 samples from the non-tumoral tissue (32 from chronic thyroiditis and 80 samples from colloid goiter) were subjected both to multivariate and univariate analysis. The increased succinate, glutamine, glutathione, serine/cysteine, ascorbate, lactate, taurine, threonine, glycine, phosphocholine/glycerophosphocholine and decreased lipids were found in both lesion types in comparison to either colloid goiter or chronic thyroiditis. The elevated glutamate and choline, and reduced citrate and glucose were additionally evident in these lesions in reference to goiter, while the increased myo-inositol—in comparison to thyroiditis. The malignant lesions were characterized by the higher alanine and lysine levels than colloid goiter and thyroiditis, while scyllo-inositol was uniquely increased in the benign lesions (not in cancer) in comparison to both non-tumoral tissue types. Moreover, the benign lesions presented with the unique increase of choline in reference to thyroiditis (not observed in the cancerous tissue). The metabolic heterogeneity of the non-tumoral tissue should be considered in the analysis of metabolic reprogramming in the thyroid lesions.

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