Construction Of Kinetic Models Research Articles

Construction of Kinetic Model of Vacuum Arc Magnetic Fluid Under External Magnetic Field

The external magnetic field is an important control method of vacuum arc at present. Because of its high efficiency and economy, it has become an important method to study the kinetic model of vacuum arc magnetic fluid. Therefore, the kinetic model of vacuum arc magnetic fluid under the external magnetic field is constructed. Firstly, the mass conservation equation, radial momentum conservation equation, axial momentum conservation equation, energy conservation equation, electromagnetic equations are analyzed. The boundary conditions of the dynamic model are cathode boundary, anode boundary and fluid wall boundary. The fluid dynamics calculation software FLUENT is used as the calculation platform, and its secondary development is carried out. Combining VC++self-programming and self-defined macro functions, according to the boundary conditions, the kinetic model of vacuum arc magnetic fluid under external magnetic field is solved, and the kinetic model analysis of vacuum arc magnetic fluid under external magnetic field is realized. Experimental analysis shows under the action of longitudinal magnetic field, the intensity of longitudinal magnetic field increases, the maximum point of ion rotation speed in the magnetic fluid of vacuum arc moves outward along the edge of vacuum arc, the number density of electrons gradually moves from the central axis to the radial direction, and the high density area increases first and then decreases; Under the action of transverse magnetic field, the intensity of transverse magnetic field increases, the ion pressure in vacuum arc magnetic fluid gradually shifts and increases, and the ion number density also increases.

Biomass yield characteristics and removal kinetic model construction with mass transfer optimization of a moving bed and constructed wetland (MBCW) integrated bioreactor

The degradation of pollutants in a moving bed and constructed wetland (MBCW) integrated bioreactor depended on rich microbial diversity and complex community structure. Sludge yield characteristics showed this system had an advantage on sludge decrement. Removal of pollutants was mainly through a simultaneous nitrification denitrification and further denitrification (SND-DN) process, according to the analysis of removal mass balance and contribution. Based on the fluidization characteristics of mixed biocarriers, the overall first-order kinetic model of MBCW system was constructed. The comparison between theoretical and measured values proved the kinetic model of CODCr, NH3-N and TN removal was validated.

Top‐down kinetic modeling from power law to elementary steps using KASTER: A methane steam reforming case study

AbstractA top‐down methodology for kinetic model construction including regression against experimental data is proposed using “KASTER.” As a case study, it is applied in the assessment of methane steam reforming (MSR) including water–gas shift (WGS) on a Ni catalyst at 923 K. The degree of detail in the reaction mechanism and the corresponding model is gradually enhanced, typically ranging from a simple power law to a microkinetic model. The reactor equations are solved transiently, preventing the numerical challenges encountered in the steady‐state solution, particularly for microkinetic models. The microkinetic variant indicated that CH4 dissociative adsorption and CO formation are kinetically relevant steps in MSR, while COOH formation is rate‐determining in WGS. However, the model providing the best balance between detail accounted for and parameter significance corresponded to a Langmuir–Hinshelwood–Hougen–Watson (LHHW) mechanism accounting for dissociative adsorption, with CO formation and COOH formation as rate‐determining steps for MSR and WGS, respectively.

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis.

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Ab Initio Group Additive Values for Thermodynamic Carbenium Ion Property Prediction

Carbenium ions are important intermediates in both zeolite and plasma chemistry. The construction of kinetic models for zeolite and plasma chemistry requires the incorporation of thermodynamic properties of these carbenium ions. In this way, thermodynamic equilibrium is incorporated resulting in more accurate and general kinetic models, which facilitate rational zeolite design and plasma process development. In this work, a consistent set of 46 group additive values (GAVs) and non-nearest neighbor interactions (NNIs) is determined for the standard enthalpy of formation, standard molar entropy, and heat capacity. The GAVs are regressed based on 165 quantum chemistry calculations with the CBS-QB3 composite method for carbenium ions as CBS-QB3 was proven a valid method for carbenium property prediction. The presented group additive approach gives the first GAVs and NNIs for entropy and heat capacity and 33 novel values for enthalpy prediction. The determined non-nearest neighbor interactions account for the effect of longer range hyperconjugation, resonance stabilization, and inductive stabilization, which are shown to be crucial for the carbenium stability. The presented group additivity scheme ensures an accurate prediction of all properties with a mean absolute error of 4.82 kJ/mol, 4.92 J/(mol·K), and 1.95 J/(mol·K) for the standard enthalpy of formation, standard molar entropy, and heat capacity, respectively. The reported GAVs and NNIs ensure the thermodynamic property prediction of paraffinic, olefinic, alkynic, aromatic, and five- and six-ring naphthenic cations.

A workflow for automatic generation and efficient refinement of individual pressure-dependent networks

Manual chemical kinetic model construction often requires modelists to at least implicitly guess all possible decay pathways and their relative fluxes for all important species. We present a workflow and associated tool for automatic generation and refinement of pressure-dependent networks (multiwell potential energy surfaces) that should enable modelists to efficiently and comprehensively identify decay pathways and estimate respective parameters for chemical species. This tool within the Arkane software combines the capabilities of the Reaction Mechanism Generator (RMG) software to generate possible reaction paths, determine thermochemistry, approximate rate coefficients and estimate frequencies with the capabilities of Arkane to use quantum chemical parameters to compute thermochemistry and pressure-dependent rate coefficients. A flux-based algorithm is used to decide which isomers to add to the network. Isomers added to the network are reacted to form other channels. When enough of the flux is accounted for by the isomers and bimolecular product channels, the generation process terminates to yield a comprehensive network. Network sensitivity analysis is applied to identify the most important wells and barriers that can be refined using quantum chemistry calculations. Iterative refinement can then be used to achieve accuracy. The comprehensive network can be easily reduced using energy and flux-based algorithms to the most important channels and isomers.

KinMod database: a tool for investigating metabolic regulation.

The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation. Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz

Deep Learning-Based Increment Theory for Formation Enthalpy Predictions.

Machine learning predictions of molecular thermochemistry, such as formation enthalpy, have been limited for large and complicated species because of the lack of available training data. Such predictions would be important in the prediction of reaction thermodynamics and the construction of kinetic models. Herein, we introduce a graph-based deep learning approach that can separately learn the enthalpy contribution of each atom in its local environment with the effect of the overall molecular structure taken into account. Because this approach follows the additivity scheme of increment theory, it can be generalized to larger and more complicated species not present in the training data. By training the model on molecules with up to 11 heavy atoms, it can predict the formation enthalpy of testing molecules with up to 42 heavy atoms with a mean absolute error of 2 kcal/mol, which is less than half of the error of the conventional increment theory. We expect that this approach will also enable rapid prediction of other extensive properties of large molecules that are difficult to derive from experiments or ab initio calculation.

A Theoretical Study of H-Abstractions of Benzaldehyde by H, O3(P), 3O2, OH, HO2, and CH3 Radicals: Ab Initio Rate Coefficients and Their Uncertainty Quantification.

Benzaldehyde is a vital intermediate during the oxidation of toluene and oxygenated aromatics, but benzaldehyde's combustion chemistry currently gains little attention. The H-atom abstraction of benzaldehyde is one critical reaction class, yet its rate coefficients rely on the analogy of molecular similarity. For this reason, we have employed the ab initio calculations at the CCSD(T)/cc-pVTZ//M06-2X/6-311+g(d,p) level of theory, along with the RRKM master equation to predict the rate coefficients of H-abstraction reactions of benzaldehyde by H, O3(P), 3O2, OH, HO2, and CH3 radicals. The calculated rate coefficients of benzaldehyde + OH generally agree with literature measurements. From the branching ratio analysis, all the H-abstractions from ring sites can be neglected in kinetic model construction except by OH radical at temperature above 1700 K, where the ring sites contribute a relatively large branching ratio to consume benzaldehyde. A random sampling method has been used to estimate the global uncertainty in the calculated rate coefficients. The logarithm of uncertainty is proportional to the reciprocal temperature, and the global uncertainty is primarily derived from the energy errors and is almost independent of the attacking species. Comparison between benzaldehyde and acetaldehyde reveals that their rate coefficients are consistent only in H-abstractions by H and HO2 but are conflictive in other reactions, especially in the case of OH. By incorporating the new calculations, existing models show a faster prediction in autoignition delay times of benzaldehyde. This study reports the first high-level ab initio calculations for the H-atom abstraction reaction class of benzaldehyde. It is necessary for the future comprehensive kinetic modeling of aromatic aldehyde.

Reaction-kinetic model-guided biocatalyst engineering for dual-enzyme catalyzed bioreaction system

Current research on dual-enzyme-catalyzed bioreaction systems has focused on single-enzyme engineering and enzyme cascade development. However, few studies have investigated the bioreaction kinetic model, which creates a gap between the biocatalyst and reaction systems. To fill this gap, we developed a reaction-kinetic model-guided biocatalyst engineering strategy, including the iterative cycle of “kinetic model construction, limiting factor analysis, and biocatalyst engineering”. In the proof-of-concept experiment, a dual-enzyme-catalyzed bioreaction of the atorvastatin precursor (3R,5R)-2 was selected and the iterative cycle was performed in two rounds. First, a double-substrate-ordered Bi–Bi kinetic model was constructed and validated. Thereafter, the limiting factors were analyzed using the constructed model and further confirmed experimentally. The limiting factor was glucose dehydrogenase total activity in the first round, whereas it changed to aldo–keto reductase total activity in the second round. Under this guidance, the bioreaction system was improved through enzyme selection, protein engineering, promoter optimization, and optimization of the expression conditions. Finally, the coupling efficiency of the two enzymes increased, resulting in high space–time yields (>1 kg/L d-1) of the atorvastatin precursor in both 250 mL flasks and 50 L bioreactors.

Mathematical modeling and kinetic behavior of Indian Umblachery cow butter and its nutritional degradation analysis under modified atmospheric packaging technique

AbstractMain objective of the study is to evaluate the effect of polyphenols enrichment, modified atmosphere packaging (CO2: 20%, N2: 75%, O2: 5%) and storage time on the shelf‐life solidity and nutritional degradation of butter from Indian native breed of Umblachery cow. The butter samples were incorporated and enriched with polyphenols and flavonoids from the seed coat extracts of Cajanus cajan (136.5 mg of gallic acid equivalents/g and 68.7 mg of quercetin equivalents/g) and butylated hydroxyl anisole (200 ppm) and the oxidative solidity and nutritional and flavor retention was evaluated without changing its organoleptic properties during 6 months of storage with and without MAP at 4, 15, and 25°C. SCECC significantly reduced the formation of oxidation products and the kinetics of shelf‐life of butter with predictive Arrhenius model was assessed in real time and accelerated according to temperature with the maximum activation energy of 5,309.2 J/mol for SCECC enriched and 3,191.4 J/mol for butylated hydroxyl anisole incorporated samples with modified atmosphere packaging at 4°C for peroxide value. The loss of polyphenols during storage was analyzed and it was 21–36% at 4°C and 31–55% at 25°C with and without modified atmosphere packaging at the end of sixth month. Organoleptic evaluations indicated that the samples treated with polyphenols combined with modified atmosphere packaging had lowermost lipid deterioration and exhibited high level of protective effects on nutritional degradation and recorded highest overall acceptability at 4°C and minimum level of sensorial behavior was observed at 25°C at the end of sixth month.Practical ApplicationsEnrichment of polyphenols with food emulsion can profitably influence their oxidative stability and the emulsions should sustain optimal quality and stability throughout the storage period. Lipid oxidation is the major deteriorative process with substantial commercial implications in terms of product cost. Due to the heavy demand for natural antioxidants, the polyphenols of seed coat extracts of Cajanus cajan have been used and correlated these results with enhancement of butter and increase its shelf life by modified atmospheric packaging. The activation energy and the rate constant of chemical reaction enabled the construction of kinetic models, which helped to estimate rate of changes in the butter sample. In order to estimate quality changes, we evaluated the polyphenolic loss, sensory features, and microbiological changes. The results of this study may deliver technical basis in the utilization of SCECC as a natural antioxidant with MAP to prevent lipid rancidification or nutritional loss in butter.

Experimental study on hypergolic ignition and non-ignition for dicyanamide-based ionic liquids at low impact velocity conditions

Ionic liquids have a wide engineering application foresight due to their easy and safe storage and handling while are limited by the unclear mechanism of hypergolic ignition. In this study, three dicyanamide-based ionic liquids with different cations, 1-allyl-3-methyl imidazolium ([AMIm]), 1-ethyl-3-methylimidazolium ([EMIm]) and 1-butyl-3-methyl-imidazolium ([BMIm]), are prepared to react with white fuming nitric acid through drop tests. The diameter of ionic droplet is 2.2 ​mm, the impact velocities are controlled at 0.16/0.45/0.62/0.82/1.01 ​m ​s-1 by adjusting the dropping height, and the whole reacting process is recorded by three cameras to provide macroscopic, microscopic and infrared details. The promotion effect of impact velocity on hypergolic ignition probability is confirmed for all the ionic liquids, and the minimum impact velocity for successful hypergolic ignition of each ionic liquid was determined. The temperature evolution of hypergolic process shows a non-monotonic trend, and a four-stage process was performed to reveal the hypergolic mechanism. The experimental results indicate that short-chain alkyl and unsaturated substituents in cations are favored for hypergolic ignition, and the datasets are conducive for kinetic model construction.

Safe model-based design of experiments using Gaussian processes

The construction of kinetic models has become an indispensable step in developing and scale-up of processes in the industry. Model-based design of experiments (MBDoE) has been widely used to improve parameter precision in nonlinear dynamic systems. Such a framework needs to account for both parametric and structural uncertainty, as the physical or safety constraints imposed on the system may well turn out to be violated, leading to unsafe experimental conditions when an optimally designed experiment is performed. In this work, Gaussian processes are utilized in a two-fold manner: 1) to quantify the uncertainty realization of the physical system and calculate the plant-model mismatch, 2) to compute the optimal experimental design while accounting for the parametric uncertainty. The proposed method, Gaussian process-based MBDoE (GP-MBDoE), guarantees the probabilistic satisfaction of the constraints in the context of model-based design of experiments. GP-MBDoE is assisted with the use of adaptive trust regions to facilitate a satisfactory local approximation. The proposed method can allow the design of optimal experiments starting from limited preliminary knowledge of the parameter set, leading to a safe exploration of the parameter space. This method’s performance is demonstrated through illustrative case studies regarding the parameter identification of kinetic models in flow reactors.

Nitromethane Decomposition via Automated Reaction Discovery and an Ab Initio Corrected Kinetic Model.

We explore the systematic construction of kinetic models from in silico reaction data for the decomposition of nitromethane. Our models are constructed in a computationally affordable manner by using reactions discovered through accelerated molecular dynamics simulations using the ReaxFF reactive force field. The reaction paths are then optimized to determine reaction rate parameters. We introduce a reaction barrier correction scheme that combines accurate thermochemical data from density functional theory with ReaxFF minimal energy paths. We validate our models across different thermodynamic regimes, showing predictions of gas phase CO and NO concentrations and high-pressure induction times that are similar to experimental data. The kinetic models are analyzed to find fundamental decomposition reactions in different thermodynamic regimes.

A functional-group-based approach to modeling real-fuel combustion chemistry – II: Kinetic model construction and validation

Abstract Construction of kinetic models to predict real-fuel combustion properties requires significant human and computational resources. In the first of this two-part study, a functional group correlation approach called FGMech was proposed for predicting the stoichiometric parameters in lumped pyrolysis reactions. The stoichiometric parameters were implemented in a recent real-fuel kinetic model, HyChem (Xu et al., 2018), and the validity of this approach was demonstrated for simulating real-fuel combustion. The present work extends the FGMech approach for developing surrogate and real-fuel kinetic models. Our approach is fundamentally different from the HyChem development approach in that no parameters are tuned to match actual real-fuel pyrolysis/oxidation data, and all model parameters are derived only from functional group data. Along with the stoichiometric parameters obtained in the first part of this study, the thermodynamic data, lumped reaction rate parameters and transport data were predicted in this work based on the functional group characterization of real fuels. The Benson group additivity method was adopted to estimate the thermodynamic data of real fuels, while rate rules developed for pure fuels were used to estimate the rate constants of lumped reactions in real-fuel models. For transport data, normal boiling point, critical temperature and pressure (estimated using the Joback group contribution method) were used to obtain Lennard-Jones parameters. The format of lumped reactions in FGMech followed the HyChem approach, and the base mechanism was adopted from the AramcoMech 2.0 and USC Mech II, respectively, to compare the model performance with different base mechanisms. Fourteen surrogate and twelve real-fuel models were developed based on this approach; they were validated against the experimental data in the literature. FGMech's performance was also compared with detailed and reduced models available in the literature. FGMech reasonably captures the experimental data in the literature, indicating that the present modeling approach is promising for modeling the combustion behavior of fuel, including surrogate mixtures and real fuels.

Combining model structure identification and hybrid modelling for photo-production process predictive simulation and optimisation.

Integrating physical knowledge and machine learning is a critical aspect of developing industrially focused digital twins for monitoring, optimisation, and design of microalgal and cyanobacterial photo-production processes. However, identifying the correct model structure to quantify the complex biological mechanism poses a severe challenge for the construction of kinetic models, while the lack of data due to the time-consuming experiments greatly impedes applications of most data-driven models. This study proposes the use of an innovative hybrid modelling approach that consists of a simple kinetic model to govern the overall process dynamic trajectory and a data-driven model to estimate mismatch between the kinetic equations and the real process. An advanced automatic model structure identification strategy is adopted to simultaneously identify the most physically probable kinetic model structure and minimum number of data-driven model parameters that can accurately represent multiple data sets over a broad spectrum of process operating conditions. Through this hybrid modelling and automatic structure identification framework, a highly accurate mathematical model was constructed to simulate and optimise an algal lutein production process. Performance of this hybrid model for long-term predictive modelling, optimisation, and online self-calibration is demonstrated and thoroughly discussed, indicating its significant potential for future industrial application.

Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data: A Hydroprocessing Study Case

Hydroprocessing reactions require several days to reach steady-state, leading to long experimentation times for collecting sufficient data for kinetic modeling purposes. The information contained in the transient data during the evolution toward the steady-state is, at present, not used for kinetic modeling since the stabilization behavior is not well understood. The present work aims at accelerating kinetic model construction by employing these transient data, provided that the stabilization can be adequately accounted for. A comparison between the model obtained against the steady-state data and the one after accounting for the transient information was carried out. It was demonstrated that by accounting for the stabilization, combined with an experimental design algorithm, a more robust and faster manner was obtained to identify kinetic parameters, which saves time and cost. An application was presented in hydrodenitrogenation, but the proposed methodology can be extended to any hydroprocessing reaction.

Analytical solution for a hybrid Logistic-Monod cell growth model in batch and continuous stirred tank reactor culture.

Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis–Menten equation. In the case of the Logistic equation, the specific growth rate is determined by the carrying capacity of the system, which could be growth‐inhibiting factors (i.e., toxic chemical accumulation) other than the nutrient level. Both equations have been found valuable to guide us build unstructured kinetic models to analyze the fermentation process and understand cell physiology. In this work, we present a hybrid Logistic‐Monod growth model, which accounts for multiple growth‐dependent factors including both the limiting nutrient and the carrying capacity of the system. Coupled with substrate consumption and yield coefficient, we present the analytical solutions for this hybrid Logistic‐Monod model in both batch and continuous stirred tank reactor (CSTR) culture. Under high biomass yield (Y x/s) conditions, the analytical solution for this hybrid model is approaching to the Logistic equation; under low biomass yield condition, the analytical solution for this hybrid model converges to the Monod equation. This hybrid Logistic‐Monod equation represents the cell growth transition from substrate‐limiting condition to growth‐inhibiting condition, which could be adopted to accurately describe the multi‐phases of cell growth and may facilitate kinetic model construction, bioprocess optimization, and scale‐up in industrial biotechnology.

Kinetic modelling of waste wood devolatilization during pyrolysis based on thermogravimetric data and solar pyrolysis reactor performance

Within presented paper complex kinetic analysis of waste wood solar pyrolysis with fuel characterisation has been presented. Thermogravimetric (TGA) data were obtained for heating rates denoted in solar pyrolysis experiments: 5, 10, 15 and 20 K/min. Study presents alternative approach to lignocellulosic biomass kinetics using NETZSCH Kinetics Neo (v. 2.1.2.2) software. TGA data were enriched with the gas analyser indications and experiments results from solar pyrolysis examined on self-designed laboratory reactor. Presented methodology consisted of (1) isoconversional analysis: Friedman, Kissinger-Akahira-Sunose and Ozawa-Flynn-Wall analysis yielding apparent activation energies Eα, varying from 185.37 to 375.56 kJ/mol at reaction extent 0.1–0.9, (2) reaction model identification with generalised master plot method which showed that decomposition was driven by three-dimensional diffusion (D3) with transition to three-dimensional phase boundary (R3) and reaction order based models (F1,F2,Fn) at the end of conversion. Finally, (3) kinetic model construction was performed based on experiments observations, resulting in modelling of 3 main reactions of pyrolysis products formations. The most intensive condensable release was observed between 250 and 440 °C what correlated with CO2 release described by dominant two-step D3-F2 consecutive reaction with activation energies and logarithmic pre-exponential factors 159.92 kJ/mol, 10.44 log(1/s) and 256.78 kJ/mol, 19.05 log(1/s) for first and second step respectively. CO formation obeyed R3 reaction mechanism with activation energy 181 kJ/mol and pre-exponential factor 12.16 log(1/s). Last stage of pyrolysis, charring, was described by two-step consecutive reaction Fn(n = 3.463)-F2 resulting in formation of CH4 and H2 with activation energies and pre-exponential factors of 249.72 kJ/mol, 15.87 log(1/s) and 162 kJ/mol, 6.61 log(1/s) respectively. Kinetic models and isoconversional methods results were tested using Fisher-Snedecor test. Presented methodology was compared with traditional three pseudo-component wood pyrolysis model and discussed.

Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’

Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.