Ordinary differential equation modeling | With mass action kinetics and assuming pA catalyzes the formation of pB but is not consumed, we get the reaction B->pB with kinetic rate law k1*pA*B, where k1 denotes a kinetic parameter for this reaction. |
Ordinary differential equation modeling | These parameters allow for a potential reduction of the kinetic parameters within a range of 0% to 100%. |
Ordinary differential equation modeling | In addition to kinetic parameters , the ODE model is defined by scaling and noise parameters. |
Supporting Information | Values of each kinetic parameter for model 4_8_12 are shown in SS Table. |
Supporting Information | The second column shows the loglo value of each kinetic parameter involved in the model reactions shown in S4 Table. |
E-glc-atp | As previously mentioned, reaction mechanisms with branched steps can eXhibit apparent substrate inhibition depending on the kinetic parameter values and the substrate concentrations. |
General framework for modelling metabolic reactions | In this article, we shall describe a general framework that enables parameterization and sampling of kinetic parameters consistent with thermodynamic constraints. |
General framework for modelling metabolic reactions | An important feature of this parameterization is that it enables inclusion of fundamental thermodynamic relations between kinetic parameters , as it is compatible with the elementary reaction formalism. |
Introduction | All use simplified kinetic expressions (loss of generality) and most ignore intrinsic thermodynamic constraints between kinetic parameters , hence they will sample infeasible parameter sets. |
Introduction | An advantage is that the parameterization retains all intrinsic thermodynamic constraints between kinetic parameters . |
Revealing the impact of thermodynamics on enzyme kinetics | For example, depending on the kinetic parameter values and the substrate concentrations, the reaction rate for the enzymes following this mechanism can display an apparent cooperative behaviour (sigmoidal reaction rate) upon addition of one substrate maintaining the other constant, while in the opposite situation they can exhibit substrate inhibition (the reaction rate pass through a maximum) [41]. |
A transcription (in)activation cycle model with realistic kinetics can reproduce experimentally observed population-level transcriptional cycHng | This experimental data (Fig 6B) can be reproduced fairly well by a 9-state cycle model with realistic kinetic parameters (Fig 6A). |
A transcription (in)activation cycle model with realistic kinetics can reproduce experimentally observed population-level transcriptional cycHng | This could be explained by an even higher number of proteins and chromatin modifications involved serially in the initiation process than currently known experimentally, by more peaked waiting time distributions for individual cycle transitions, which could again be due to multi-step serial processes, or by differences in kinetic parameters . |
Introduction | However, whether models with realistic kinetic parameters can also generate such slow dynamics remains unclear. |
Introduction | Do the stochastic dynamics of the transcription cycle model agree with single-cell and population-level studies of transcription when realistic kinetic parameters are considered? |
Characteristic power-law parameters describe uptake in different cell lines | Changes in many kinetic parameters associated with the Zipper mechanism can be mapped to changes in the power-law parameters (Fig 4A; simulated data in 84C Fig). |
Characteristic power-law parameters describe uptake in different cell lines | For the same bacterial strain, different host cell types would likely lead to different kinetic parameters for the Zipper mechanisms, and thus different corresponding power-law parameters. |
Supporting Information | (C) Mapping of kinetic parameters associated with the 3-stage model to power-law parameters. |