Find research datasets worth reusing

opencc-by-4.0Oct 2019View details →

Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models

IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION. This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (Bos taurus), sheep (Ovis aries), and swine (Sus scrofa domesticus). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model. The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).

opencc-by-4.0Jan 2021View details →

Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"

Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".

opencc-by-4.0Nov 2022View details →

A CO2 valorization plant to produce light hydrocarbons: kinetic model, process design and life cycle assessment

Supplementary material:  Reaction indexes, Conservation equations, boundary conditions and used coefficients. Additional experimental results, Experimental data fitting,  Stream properties and composition of the CO2 plant, Life Cycle Assessment indicators, assumptions and data input

opencc-by-4.0Apr 2024View details →

Data, plotting scripts, and figures for "A physics-based ignition model with detailed chemical kinetics for live fuel burning studies"

This repository contains the data, plotting scripts, and figures associated with the paper "A physics-based ignition model with detailed chemical kinetics for live fuel burning studies" by Diba Behnoudfar and Kyle E. Niemeyer. See the README file for additional details.

Ensemble statistics for modelled Eddy Kinetic Energy in the Southern Ocean

This dataset contains surface eddy kinetic energy over the Southern Ocean region, sourced from a 50-member ensemble of 0.25° ocean model simulations. It is used in the paper "Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics" published in Journal of Geophysical Research - Oceans. This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (<a href="https://www.nemo-ocean.eu/doc">Madec, 2008</a>). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in <a href="https://doi.org/10.1016/j.ocemod.2013.02.004">Brankart, 2013</a>) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. Data is for the period 1979-2015. The sea level anomaly is found according to <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al (2020)</a> and converted into surface geostrophic velocity anomaly using the geostrophic relation. This velocity field is then used to calculate the eddy kinetic energy (EKE). Data is averaged over calendar month, and restricted to the latitude range 40°-60°S. A full description of this process is included in the companion paper. The dataset includes EKE files (eke_0??.nc), with monthy EKE saved for the period 1979-2015 for each ensemble member, and a single file (tau.nc) for the monthly-averaged wind stress over the same period.

opencc-by-4.0Dec 2020View details →

Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides

We provide here the underlying data of the publication "Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides". Please find the abstract below. Background: Cellodextrin phosphorylase (CdP; EC 2.4.1.49) catalyzes the iterative β-1,4-glycosylation of cellobiose using α-D-glucose 1-phosphate as the donor substrate. Cello-oligosaccharides (COS) with a degree of polymerization (DP) of up to 6 are soluble while those of larger DP self-assemble into solid cellulose material. The soluble COS have attracted considerable attention for their use as dietary fibers that offer a selective prebiotic function. An efficient synthesis of soluble COS requires good control over the DP of the products formed. A mathematical model of the iterative enzymatic glycosylation would be important to facilitate target-oriented process development. Results: A detailed time-course analysis of the formation of COS products from cellobiose (25 mM, 50 mM) and α-D-glucose 1-phosphate (10–100 mM) was performed using the CdP from Clostridium cellulosi. A mechanism-based, Michaelis–Menten type mathematical model was developed to describe the kinetics of the iterative enzymatic glycosylation of cellobiose. The mechanistic model was combined with an empirical description of the DP-dependent self-assembly of the COS into insoluble cellulose. The hybrid model thus obtained was used for kinetic parameter determination from time-course fits performed with constraints derived from initial rate data. The fitted hybrid model provided excellent description of the experimental dynamics of the COS in the DP range 3–6 and also accounted for the insoluble product formation. The hybrid model was suitable to disentangle the complex relationship between the process conditions used (i.e., substrate concentration, donor/acceptor ratio, reaction time) and the reaction output obtained (i.e., yield and composition of soluble COS). Model application to a window-of-operation analysis for the synthesis of soluble COS was demonstrated on the example of a COS mixture enriched in DP 4. Conclusions: The hybrid model of CdP-catalyzed iterative glycosylation is an important engineering tool to study and optimize the biocatalytic synthesis of soluble COS. The kinetic modeling approach used here can be of a general interest to be applied to other iteratively catalyzed enzymatic reactions of synthetic importance.

opencc-by-4.0Oct 2019View details →

Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)

This repository contains the WRF configuration files necessary to reproduce the simulations  as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100) The file windturbines_GMD.txt contains the locations of  all windturbines implemented in the simulations. The corresponding attributes of each  wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019). The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that  there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files. The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model.  Note that the dates and pathes have to be adjusted in the python files.  After downloading the surface and model level data some postprocessing  is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.

opencc-by-4.0Dec 2018View details →

Raw data from Qin et al. (2018) "Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles"

Raw hydrogen concentration vs. time data from Qin, H., X. Guan, J. Z. Bandstra, R. L. Johnson, and P. G. Tratnyek (2018) “Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles” Environ. Sci. Technol.  52(23): 13887-13896. [10.1021/acs.est.8b04436] This manuscript reports a large set of new concentration vs. time data for dihydrogen (H2) produced by corrosion of granular zerovalent iron (i.e., the hydrogen evolution reaction, HER) in aqueous media relevant to groundwater remediation. Four alternative kinetic models are evaluated by fitting the data using global non-linear regression. Details are given in the main text and supporting information of the (open access) manuscript.  The data provided here are in two formats: (i) a .csv file that contains only data and labels, and (ii) a .pxp file that includes the data and graphs (without fits) in the same layout as figures in the original manuscript. The .pxp file was prepared with Igor Pro 8.02 (https://www.wavemetrics.com).

opencc-by-4.0Apr 2023View details →

Dynamically coupled kinetic chemistry in brown dwarf atmospheres I. Performing global scale kinetic modelling

Gifs and Exo-FMS GCM output from the 3D brown dwarf atmospheric simulations in Lee, Tan and Tsai (2023).  Animated gifs for each effective temperature (Teff - first number in filename) of the brown dwarf (OLR and CH4 VMR). The gifs frames are every hour of simulation for 4 simulated days. Exo-FMS GCM output in netCDF format containing the 3D T-p structure and chemical results from the coupled mini-chem and GCM model for each Teff simulation (number in filename). `average' is the averaged output of the last 100 days. `daily' is the snapshot at the end of the simulation.

opencc-by-4.0May 2023View details →

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details. The dataset contains the following files: 1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv). 2. scripts.zip - scripts to generate figure 2-5 on toy data

opencc-by-4.0Feb 2023View details →

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387). The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details. The dataset contains the following files: 1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of E. coli metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4). 2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b) 3. ODE_solutions.zip - self-explanatory (Figure 2c) 4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d) 5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a) 6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g The remaining datasets are published in the following links  - https://doi.org/10.5281/zenodo.7930084  - https://doi.org/10.5281/zenodo.10391802

opencc-by-4.0Aug 2023View details →

Datasets supporting the publication "Technical Note: in-situ measurements and modelling of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Milsom et al.

Supporting experimental and modelling data for the manuscript entitled "Technical Note: Modelling and in-situ measurements of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Adam Milsom et al. 2023.  Includes raw QCM-D data with the numbers at the beginning of the files corresponding to the experiment numbers in the manuscript.  Normalised Raman peak area data for modelling and model ensemble outputs, including uptake coefficients.

opencc-by-4.0Nov 2023View details →

Ab Initio and Kinetic Modelling of β-D-xylopyranose under Fast Pyrolysis Conditions

Zip file containing all the IRC connecting transition states to minima in our paper published in The Journal of Physical Chemistry A, <a title="DOI URL" href="https://doi.org/10.1021/acs.jpca.3c07063">https://doi.org/10.1021/acs.jpca.3c07063</a>

opencc-by-4.0Jun 2019View details →

Dataset - Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties

Data needed to reproduce the results from the manuscript “Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties" by L. Miskovic, J. Beal, M. Moret, and V. Hatzimanikatis 1. Data generated with the ORACLE workflow that was used in the iSCHRUNK training: <ul> <li>Classification label vectors for the three analyzed metabolic concentration cases: <ul> <li>Reference case: class_vector_train_ref.mat</li> <li>Extreme1 case: class_vector_train_ex1.mat</li> <li>Extreme2 case: class_vector_train_ex2.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.  <ul> <li>Reference case: training_set_ref.mat</li> <li>Extreme1 case: training_set_ex1.mat</li> <li>Extreme2 case: training_set_ex2.mat</li> </ul> </li> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ref.mat</li> <li>Extreme1 case: ccXTR_ex1.mat</li> <li>Extreme2 case: ccXTR_ex2.mat</li> </ul> </li> <li>Thermodynamics-based Flux Analysis (TFA) models for the three cases: <ul> <li>Reference case: tfa_ref.mat</li> <li>Extreme1 case: tfa_ex1.mat</li> <li>Extreme2 case: tfa_ex2.mat</li> </ul> </li> <li>Parameter names identical for the three cases <ul> <li>parameterNames.mat</li> </ul> </li> </ul> 2. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 4). <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>ccXTR_ValidNeg.mat</li> </ul> </li> <li>Parameter sets used in validation <ul> <li>validation_set_neg.mat</li> </ul> </li> </ul> 3. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Table 3). <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_neg_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_neg_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_neg_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.  <ul> <li>Reference case: validation_set_ref_neg_agg.mat</li> <li>Extreme1 case: validation_set_ref_neg_agg.mat</li> <li>Extreme2 case: tvalidation_set_ref_neg_agg.mat</li> </ul> </li> </ul> </li> </ul> <ul> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_pos_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_pos_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_pos_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.  <ul> <li>Reference case: validation_set_ref_pos_agg.mat</li> <li>Extreme1 case: validation_set_ex1_pos_agg.mat</li> <li>Extreme2 case: validation_set_ex2_pos_agg.mat</li> </ul> </li> </ul> </li> </ul> 4. Reassignment study: validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 6 and Table 4). <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_neg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.  <ul> <li>Reference case: validation_set_neg_reassignment.mat</li> </ul> </li> </ul> </li> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_pos.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σA, which is constrained between 0 and 1.  <ul> <li>Reference case: validation_set_pos_reassignment.mat</li> </ul> </li> </ul> </li> </ul>

Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, main part

Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at  <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details. Datasets: <ul> <li> models.zip - Datasets parameterizing kinetic nonlinear models of a wild-type E. coli strain used for training generative adversarial networks <ul> <li>subfolder 1: kinetic - contains the kinetic model (kin_varma_curated.yml)</li> <li>subfolder 2:  thermo - contains the thermodynamic model for all the four physiologies (varma_fdp1, varma_fdp2, varma_fdp3, varma_fdp4)</li> <li>subfolder 3:  steady_state_samples: contains the TFA steady state profiles for all four physiologies (samples_fdp1, sample_fdp2, samples_fdp3, samples_fdp4)</li> <li>subfolder 4: parameters - contains the kinetic parameter training dataset for each physiology (.hdf5 files), maximal eigenvalues (training labels)  (maximal_eigenvalues.csv) and the minimum eigenvalues (minimal_eigenvalues.csv)</li> </ul> </li> <li>vanilla_learning_training.zip: contains 4 folders for each of the 4 physiologies. <ul> <li>each of these folders contains 6 subsubfolders in the format N-{n} ( N-10, N-50, N-100, N-500, N-1000, N-72000), where {n} represents the number of used training data samples.</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy (Note: eigenvalues were not calculated for N=10, 50, 100 as traning failed)/</li> </ul> </li> </ul> </li> <li>transfer_learning_training.zip - contains 12 subfolders "tl_fdpi_fdpj" where i,j ={1,2,3,4} for each of the 12 transfer learning case <ul> <li>each of these folders contains 5 subsubfolders N-10, N-50, N-100, N-500, N-1000</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy </li> </ul> </li> </ul> </li> </ul> <ul> <li>best_generators.zip <ul> <li>The best generators (with the highest incidence of relevant models) for each physiology (generator1- 4.h5)</li> <li>The normalizing scaling parameters for each generator (d_scaling.pkl).</li> <li> </li> </ul> </li> <li>Temporal evolution of perturbations in non-linear ordinary differential equations <ul> <li>vanilla_ODE_sample_parameters.zip - contains (i) 1000 REKINDLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total) (ii) 1000 ORACLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total). These parameter sets parameterize the ODEs which are integrated.</li> <li>ode_solutions_physiology1.zip (available at <a href="https://zenodo.org/record/5818192">https://zenodo.org/record/5818192</a>) -  contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li>ode_solutions_physiology1_ORACLE.zip (available at <a href="https://zenodo.org/record/5819669">https://zenodo.org/record/5819669</a>) -  contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li>ode_solutions_physiologies2-4.zip - contains 6 subfolders (physiology_2-4, physiology_2-4_ORACLE), with each subfolder containing 10 sub subfolders. Each sub subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE / ORACLE generated parameter sets for physiology 2-4, each of the 1000 models having a random perturbation.</li> <li>transfer_learning_ODE_solutions.zip - contains two subfolders N_10, N_50, each subfolder contains 12 subsubfolders titled i_j (where i = {1,2,3,4} and j = {1,2,3,4} where 1_2 represent the transfer learning case from physiology 2 to physiology 1 and when using {n} samples from physiology 2 and so on (where {n}=10 and 50 respectively).  Each subsubfolders contain <ul> <li>i_j.hdf5: contains 300 kinetic parameter sets generated using (i) REKINDLE for this transfer learning case</li> <li>i_j.csv: the maximal eigenvalues of the parameter sets</li> <li>solutions.csv: ODE integrated time series data for the relevant kinetic parameters out of the 300 generated.</li> </ul> </li> </ul> </li> </ul>

Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 2

Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1

opencc-zeroFeb 2022View details →

dryad40/100

Analytical kinetic model of native tandem promoters in E. coli

Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in E. coli. We also studied the sequence (distance between transcription start sites 'dTSS', pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and dTSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when dTSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when dTSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of dTSS is implemented by a continuous step function, fit to in vivo data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of dTSS. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.

opencc-by-4.0Jun 2022View details →

Generic solving of a multi-compartment physiologically-based kinetic model

This repository makes available supplementary material related to a scientific paper in progress.