scholarly journals Parameter estimation in tree graph metabolic networks

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2417
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D. Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

scholarly journals Parameter estimation in tree graph metabolic networks

2016 ◽  
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known, which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: Firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is very fast compared to the usually applied methods, since it is not based on an iterative scheme. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

scholarly journals Parameter estimation in tree graph metabolic networks

2016 ◽  
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known, which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: Firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is very fast compared to the usually applied methods, since it is not based on an iterative scheme. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

scholarly journals Parameter estimation in tree graph metabolic networks

2016 ◽  
Author(s):  
Laura Astola ◽  
Hans Stigter ◽  
Maria Victoria Gomez Roldan ◽  
Fred van Eeuwijk ◽  
Robert D Hall ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Reaction Rates ◽  
Enzymatic Reactions ◽  
Potential Candidate ◽  
Concentration Data ◽  
Time Dynamics ◽  
Tomato Seedlings ◽  
Kinetic Coefficients ◽  
Time Series Gene Expression ◽  
Kinetic Rates

We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known, which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: Firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is very fast compared to the usually applied methods, since it is not based on an iterative scheme. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

scholarly journals Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway

2016 ◽  
Vol 2 (1) ◽  
pp. e1501235 ◽  
Author(s):  
Markus A. Keller ◽  
Andre Zylstra ◽  
Cecilia Castro ◽  
Alexandra V. Turchyn ◽  
Julian L. Griffin ◽  
...  
Keyword(s):  
Pentose Phosphate Pathway ◽  
Metabolic Networks ◽  
Reaction Rates ◽  
Structural Features ◽  
Pentose Phosphate ◽  
Resonance Spectroscopy ◽  
Enzymatic Reactions ◽  
Evolutionary Origins ◽  
Sugar Phosphates ◽  
Neutral Conditions

Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks.

scholarly journals Simultaneous Parameters Identifiability and Estimation of anE. coliMetabolic Network Model

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Kese Pontes Freitas Alberton ◽  
André Luís Alberton ◽  
Jimena Andrea Di Maggio ◽  
Vanina Gisela Estrada ◽  
María Soledad Díaz ◽  
...  
Keyword(s):  
Experimental Data ◽  
Escherichia Coli ◽  
Metabolic Networks ◽  
Initial Conditions ◽  
Model Fit ◽  
Real Problem ◽  
Kinetic Rates ◽  
K 12 ◽  
Procedure Model

This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of theEscherichia coliK-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

scholarly journals Cardiometabolism as an Interlocking Puzzle between the Healthy and Diseased Heart: New Frontiers in Therapeutic Applications

2021 ◽  
Vol 10 (4) ◽  
pp. 721
Author(s):  
Teresa Pasqua ◽  
Carmine Rocca ◽  
Anita Giglio ◽  
Tommaso Angelone
Keyword(s):  
Cardiac Muscle ◽  
Metabolic Networks ◽  
Mechanical Energy ◽  
Cardiac Metabolism ◽  
Protective Mechanism ◽  
Enzymatic Reactions ◽  
Carbohydrate Utilization ◽  
Pharmacological Agents ◽  
Physiological Structure ◽  
And Function

Cardiac metabolism represents a crucial and essential connecting bridge between the healthy and diseased heart. The cardiac muscle, which may be considered an omnivore organ with regard to the energy substrate utilization, under physiological conditions mainly draws energy by fatty acids oxidation. Within cardiomyocytes and their mitochondria, through well-concerted enzymatic reactions, substrates converge on the production of ATP, the basic chemical energy that cardiac muscle converts into mechanical energy, i.e., contraction. When a perturbation of homeostasis occurs, such as an ischemic event, the heart is forced to switch its fatty acid-based metabolism to the carbohydrate utilization as a protective mechanism that allows the maintenance of its key role within the whole organism. Consequently, the flexibility of the cardiac metabolic networks deeply influences the ability of the heart to respond, by adapting to pathophysiological changes. The aim of the present review is to summarize the main metabolic changes detectable in the heart under acute and chronic cardiac pathologies, analyzing possible therapeutic targets to be used. On this basis, cardiometabolism can be described as a crucial mechanism in keeping the physiological structure and function of the heart; furthermore, it can be considered a promising goal for future pharmacological agents able to appropriately modulate the rate-limiting steps of heart metabolic pathways.

scholarly journals KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Hugo Mochão ◽  
Pedro Barahona ◽  
Rafael S Costa
Keyword(s):  
Experimental Data ◽  
Metabolic Networks ◽  
Model Simulation ◽  
Web Interface ◽  
Concentration Data ◽  
Web Based ◽  
Share Data ◽  
Visualization Tools ◽  
Filter Mechanism ◽  
Machine Readable

Abstract The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL:  www.kimosys.org

scholarly journals Elaboration of the corticosteroid synthesis pathway in primates through a multi-step enzyme

2019 ◽  
Author(s):  
Carrie F. Olson-Manning
Keyword(s):  
Cytochrome P450 ◽  
Gene Duplication ◽  
Metabolic Networks ◽  
Cellular Systems ◽  
Potential Outcomes ◽  
Enzymatic Reactions ◽  
Ancestral State ◽  
State Reconstruction ◽  
Specific Regulation

AbstractMetabolic networks are complex cellular systems dependent on the interactions among, and regulation of, the enzymes in the network. However, the mechanisms that lead to the expansion of networks are not well understood. While gene duplication is a major driver of the expansion and functional evolution of metabolic networks, the effect and fate of retained duplicates in a network is poorly understood. Here, I study the evolution of an enzyme family that performs multiple subsequent enzymatic reactions in the corticosteroid pathway in primates to illuminate the mechanisms that shape network components following duplication. The products of the pathway (aldosterone, corticosterone, and cortisol) are steroid hormones that regulate metabolism and stress in tetrapods. These steroids are synthesized by a multi-step enzyme Cytochrome P450 11B (CYP11B) that performs subsequent steps on different carbon atoms of the steroid derivatives. Through ancestral state reconstruction and in vitro characterization, I find the ancestor of the CYP11B1 and CYP11B2 paralogs (in primates) had moderate ability to synthesize cortisol and aldosterone. Following duplication in the primate lineage the CYP11B1 homolog specialized on the production of cortisol while its paralog, CYP11B2, maintained its ability to perform multiple subsequent steps as in the ancestral pathway. Unlike CYP11B1, CYP11B2 could not specialize on the production of aldosterone because it is constrained to perform earlier steps in the corticosteroid synthesis pathway to achieve the final product aldosterone. These results suggest that pathway context, along with tissue-specific regulation, both play a role in shaping potential outcomes of metabolic network elaboration.

scholarly journals Engineering microbial chemical factories using metabolic models

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Debolina Sarkar ◽  
Costas D. Maranas
Keyword(s):  
Metabolic Engineering ◽  
Metabolic Networks ◽  
Reaction Rates ◽  
Control Engineering ◽  
Current Perspective ◽  
Simple Molecules ◽  
Living Organisms ◽  
Metabolic Network Modeling ◽  
And Control ◽  
Analytical Approaches

Abstract Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.

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