The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Gour Karmakar - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (3) - Decoupled modeling of gene regulatory networks using Michaelis-Menten Kinetics
Neural Information Processing, 2015Co-Authors: A.S.K. Youseph, Madhusudan Rajgopal Chetty, Gour KarmakarAbstract:A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten Kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed.
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decoupled modeling of gene regulatory networks using Michaelis Menten Kinetics
International Conference on Neural Information Processing, 2015Co-Authors: A.S.K. Youseph, Madhusudan Rajgopal Chetty, Gour KarmakarAbstract:A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten Kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed.
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Gene regulatory network inference using Michaelis-Menten Kinetics
Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015Co-Authors: A.S.K. Youseph, Marshini Chetty, Gour KarmakarAbstract:A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten Kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten Kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.
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CEC - Gene regulatory network inference using Michaelis-Menten Kinetics
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: A.S.K. Youseph, Madhu Chetty, Gour KarmakarAbstract:A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten Kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten Kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.
A.S.K. Youseph - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (3) - Decoupled modeling of gene regulatory networks using Michaelis-Menten Kinetics
Neural Information Processing, 2015Co-Authors: A.S.K. Youseph, Madhusudan Rajgopal Chetty, Gour KarmakarAbstract:A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten Kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed.
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decoupled modeling of gene regulatory networks using Michaelis Menten Kinetics
International Conference on Neural Information Processing, 2015Co-Authors: A.S.K. Youseph, Madhusudan Rajgopal Chetty, Gour KarmakarAbstract:A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten Kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed.
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Gene regulatory network inference using Michaelis-Menten Kinetics
Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015Co-Authors: A.S.K. Youseph, Marshini Chetty, Gour KarmakarAbstract:A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten Kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten Kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.
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CEC - Gene regulatory network inference using Michaelis-Menten Kinetics
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: A.S.K. Youseph, Madhu Chetty, Gour KarmakarAbstract:A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten Kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten Kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.
Vidya Suseela - One of the best experts on this subject based on the ideXlab platform.
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plant invasion alters the Michaelis Menten Kinetics of microbial extracellular enzymes and soil organic matter chemistry along soil depth
Biogeochemistry, 2020Co-Authors: Kyungjin Min, Vidya SuseelaAbstract:Microbial extracellular enzymes decompose distinct components of soil organic matter (SOM), thus influencing its stability. However, we lack the knowledge about how the Kinetics of individual enzymes vary when multiple substrates change simultaneously. Here we used Japanese knotweed (Polygonum cuspidatum) invasion as a model system to explore how the Michaelis–Menten Kinetics (Vmax and km) of microbial extracellular enzymes vary with corresponding SOM components across soil depth (0–5, 5–10, and 10–15 cm). We hypothesized that invasion will increase the Vmax (maximum enzyme activity) and km (substrate concentration at half Vmax) of oxidative enzymes but decrease the Vmax and km of hydrolytic enzymes, and that increasing soil depth will alleviate the invasion effects on the enzyme Kinetics. The invasion of knotweed, which input litter rich in recalcitrant compounds, altered soil chemistry including an increase in lignin and fungal biomass compared to the adjacent non-invaded soils. The Vmax of peroxidase, the oxidative enzyme that degrades lignin, increased in the invaded soils (0–5 cm) compared to the non-invaded soils. Among the hydrolytic enzymes, the Vmax of N-acetyl-glucosaminidase which degrades chitin from fungal cell walls increased in the invaded soils (0–5 cm). However, there was no associated change in the km of peroxidase and N-acetyl-glucosaminidase under invasion, suggesting that microbes modified the enzyme production rates, not the types (isozyme) of enzymes under invasion. The Vmax of all enzymes decreased with depth, due to the reduced substrate availability. These results highlight that the addition of relatively recalcitrant substrates due to plant invasion altered the Kinetics of microbial extracellular enzymes with implications for SOM chemistry in the invaded soils.
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Plant invasion alters the Michaelis–Menten Kinetics of microbial extracellular enzymes and soil organic matter chemistry along soil depth
Biogeochemistry, 2020Co-Authors: Kyungjin Min, Vidya SuseelaAbstract:Microbial extracellular enzymes decompose distinct components of soil organic matter (SOM), thus influencing its stability. However, we lack the knowledge about how the Kinetics of individual enzymes vary when multiple substrates change simultaneously. Here we used Japanese knotweed ( Polygonum cuspidatum ) invasion as a model system to explore how the Michaelis–Menten Kinetics ( V _ max and k _ m ) of microbial extracellular enzymes vary with corresponding SOM components across soil depth (0–5, 5–10, and 10–15 cm). We hypothesized that invasion will increase the V _ max (maximum enzyme activity) and k _ m (substrate concentration at half V _ max ) of oxidative enzymes but decrease the V _ max and k _ m of hydrolytic enzymes, and that increasing soil depth will alleviate the invasion effects on the enzyme Kinetics. The invasion of knotweed, which input litter rich in recalcitrant compounds, altered soil chemistry including an increase in lignin and fungal biomass compared to the adjacent non-invaded soils. The V _ max of peroxidase, the oxidative enzyme that degrades lignin, increased in the invaded soils (0–5 cm) compared to the non-invaded soils. Among the hydrolytic enzymes, the V _ max of N -acetyl-glucosaminidase which degrades chitin from fungal cell walls increased in the invaded soils (0–5 cm). However, there was no associated change in the k _ m of peroxidase and N -acetyl-glucosaminidase under invasion, suggesting that microbes modified the enzyme production rates, not the types (isozyme) of enzymes under invasion. The V _ max of all enzymes decreased with depth, due to the reduced substrate availability. These results highlight that the addition of relatively recalcitrant substrates due to plant invasion altered the Kinetics of microbial extracellular enzymes with implications for SOM chemistry in the invaded soils.
Steven D. Allison - One of the best experts on this subject based on the ideXlab platform.
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the Michaelis Menten Kinetics of soil extracellular enzymes in response to temperature a cross latitudinal study
Global Change Biology, 2012Co-Authors: Donovan P. German, Kathleen R. B. Marcelo, Madeleine M. Stone, Steven D. AllisonAbstract:Decomposition of soil organic matter (SOM) is mediated by microbial extracellular hydrolytic enzymes (EHEs). Thus, given the large amount of carbon (C) stored as SOM, it is imperative to understand how microbial EHEs will respond to global change (and warming in particular) to better predict the links between SOM and the global C cycle. Here, we measured the Michaelis–Menten Kinetics [maximal rate of velocity (Vmax) and half-saturation constant (Km)] of five hydrolytic enzymes involved in SOM degradation (cellobiohydrolase, b-glucosidase, b-xylosidase, a-glucosidase, and N-acetyl-b-D-glucosaminidase) in five sites spanning a boreal forest to a tropical rainforest. We tested the specific hypothesis that enzymes from higher latitudes would show greater temperature sensitivities than those from lower latitudes. We then used our data to parameterize a mathematical model to test the relative roles of Vmax and Km temperature sensitivities in SOM decomposition. We found that both Vmax and Km were temperature sensitive, with Q10 values ranging from 1.53 to 2.27 for Vmax and 0.90 to 1.57 for Km. The Q10 values for the Km of the cellulose-degrading enzyme b-glucosidase showed a significant (P = 0.004) negative relationship with mean annual temperature, indicating that enzymes from cooler climates can indeed be more sensitive to temperature. Our model showed that Km temperature sensitivity can offset SOM losses due to Vmax temperature sensitivity, but the offset depends on the size of the SOM pool and the magnitude of Vmax. Overall, our results suggest that there is a local adaptation of microbial EHE Kinetics to temperature and that this should be taken into account when making predictions about the responses of C cycling to global change.
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The Michaelis–Menten Kinetics of soil extracellular enzymes in response to temperature: a cross‐latitudinal study
Global Change Biology, 2012Co-Authors: Donovan P. German, Kathleen R. B. Marcelo, Madeleine M. Stone, Steven D. AllisonAbstract:Decomposition of soil organic matter (SOM) is mediated by microbial extracellular hydrolytic enzymes (EHEs). Thus, given the large amount of carbon (C) stored as SOM, it is imperative to understand how microbial EHEs will respond to global change (and warming in particular) to better predict the links between SOM and the global C cycle. Here, we measured the Michaelis–Menten Kinetics [maximal rate of velocity (Vmax) and half-saturation constant (Km)] of five hydrolytic enzymes involved in SOM degradation (cellobiohydrolase, b-glucosidase, b-xylosidase, a-glucosidase, and N-acetyl-b-D-glucosaminidase) in five sites spanning a boreal forest to a tropical rainforest. We tested the specific hypothesis that enzymes from higher latitudes would show greater temperature sensitivities than those from lower latitudes. We then used our data to parameterize a mathematical model to test the relative roles of Vmax and Km temperature sensitivities in SOM decomposition. We found that both Vmax and Km were temperature sensitive, with Q10 values ranging from 1.53 to 2.27 for Vmax and 0.90 to 1.57 for Km. The Q10 values for the Km of the cellulose-degrading enzyme b-glucosidase showed a significant (P = 0.004) negative relationship with mean annual temperature, indicating that enzymes from cooler climates can indeed be more sensitive to temperature. Our model showed that Km temperature sensitivity can offset SOM losses due to Vmax temperature sensitivity, but the offset depends on the size of the SOM pool and the magnitude of Vmax. Overall, our results suggest that there is a local adaptation of microbial EHE Kinetics to temperature and that this should be taken into account when making predictions about the responses of C cycling to global change.
Philip N. Barlett - One of the best experts on this subject based on the ideXlab platform.
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Reaction/diffusion with Michaelis–Menten Kinetics in electroactive polymer films. Part 1. The steady-state amperometric response
The Analyst, 1996Co-Authors: Michael E. G. Lyons, James C. Greer, Catherine A. Fitzgerald, Thomas Bannon, Philip N. BarlettAbstract:The theoretical analysis of the steady-state amperometric response for a polymer-modified electrode system whcih exhibits Michaelis–Menten Kinetics is discussed. In particular, the interplay between substrate diffusion within the polymer matrix and substrate reaction at the catalytic polymer sites is examined. A non-linear reaction/diffusion equation describing the substrate transport and reaction Kinetics within the film is formulated and approximate analytical solutions for the substrate concentration profiles and corresponding current responses are developed. Four distinct limiting cases are developed and are represented schematically in a kinetic case diagram. The theoretical analysis is extended to consider the complicating situation of substrate diffusion in the Nernst diffusion layer adjacent to the polymer film. The allied problem of the response of a potentiometric sensor exhibiting Michaelis–Menten Kinetics is also examined. The theoretical model developed in the paper is validated by examining the electro-oxidation of dopamine, adrenaline and noradrenaline at surfactant-doped polypyrrole-modified electrodes. Good agreement between the amperometric current response predicted from the theoretical model and the current response obtained experimentally from batch amperometry is obtained. Non-linear least-squares analysis of the batch amperometric data in tandem with the theoretical expression derived for the steady-state current response produces reasonable values for the Michaelis constant, Km, the catalytic rate constant, Kc, and the substrate diffusion coefficient, Ds, for each of the three catecholamine substrates examined.
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reaction diffusion with Michaelis Menten Kinetics in electroactive polymer films part 1 the steady state amperometric response
Analyst, 1996Co-Authors: Michael E. G. Lyons, James C. Greer, Thomas Bannon, Catherine Fitzgerald, Philip N. BarlettAbstract:The theoretical analysis of the steady-state amperometric response for a polymer-modified electrode system whcih exhibits Michaelis–Menten Kinetics is discussed. In particular, the interplay between substrate diffusion within the polymer matrix and substrate reaction at the catalytic polymer sites is examined. A non-linear reaction/diffusion equation describing the substrate transport and reaction Kinetics within the film is formulated and approximate analytical solutions for the substrate concentration profiles and corresponding current responses are developed. Four distinct limiting cases are developed and are represented schematically in a kinetic case diagram. The theoretical analysis is extended to consider the complicating situation of substrate diffusion in the Nernst diffusion layer adjacent to the polymer film. The allied problem of the response of a potentiometric sensor exhibiting Michaelis–Menten Kinetics is also examined. The theoretical model developed in the paper is validated by examining the electro-oxidation of dopamine, adrenaline and noradrenaline at surfactant-doped polypyrrole-modified electrodes. Good agreement between the amperometric current response predicted from the theoretical model and the current response obtained experimentally from batch amperometry is obtained. Non-linear least-squares analysis of the batch amperometric data in tandem with the theoretical expression derived for the steady-state current response produces reasonable values for the Michaelis constant, Km, the catalytic rate constant, Kc, and the substrate diffusion coefficient, Ds, for each of the three catecholamine substrates examined.