Dataset Information

Introduction of a new platform for parameter estimation of kinetically complex environmental systems.

ABSTRACT: A modeling framework (ReKinSim - Reaction Kinetics Simulator) is introduced, within which biogeochemical reactions in environmental systems can be described and inversely fitted to experimental data. Three key features of this simulation environment are: (1) a generic mathematical tool for solving sets of unlimited, arbitrary, non-linear ordinary differential equations; (2) no limitation to the number or type of reactions or other influential dynamics (e.g., isotope fractionation or small-scale mass-transfer limitations); (3) an easy to use and flexible module for nonlinear data-fitting. It allows users to easily define any kinetic model by a set of biogeochemical reactions relevant to the experimental application and to obtain the values of the kinetic parameters by fitting of the model to data. By allowing users to include the environmentally related processes and solving them along with the chemical kinetics, ReKinSim helps the user to elucidate the extent that these processes are controlled by factors other than kinetics. The novelty of the presented program primary lays in its unique combination of flexibility, computational efficiency and user-friendliness. ReKinSim's usability is showcased by four case studies of varying complexity, and compared against a set of currently available modeling tools.

SUBMITTER: Gharasoo M

PROVIDER: S-EPMC5669308 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Introduction of a new platform for parameter estimation of kinetically complex environmental systems.

Gharasoo Mehdi M Thullner Martin M Elsner Martin M

Environmental modelling & software : with environment data news 20171201

A modeling framework (ReKinSim - Reaction Kinetics Simulator) is introduced, within which biogeochemical reactions in environmental systems can be described and inversely fitted to experimental data. Three key features of this simulation environment are: (1) a generic mathematical tool for solving sets of unlimited, arbitrary, non-linear ordinary differential equations; (2) no limitation to the number or type of reactions or other influential dynamics (e.g., isotope fractionation or small-scale ...[more]

PMID: 29200932

Similar Datasets

Project description:Dealing with a system of first-order reactions is a recurrent issue in chemometrics, especially in the analysis of data obtained by spectroscopic methods applied on complex biological systems. We argue that global multiexponential fitting, the still common way to solve such problems, has serious weaknesses compared to contemporary methods of sparse modeling. Combining the advantages of group lasso and elastic net-the statistical methods proven to be very powerful in other areas-we created an optimization problem tunable from very sparse to very dense distribution over a large pre-defined grid of time constants, fitting both simulated and experimental multiwavelength spectroscopic data with high computational efficiency. We found that the optimal values of the tuning hyperparameters can be selected by a machine-learning algorithm based on a Bayesian optimization procedure, utilizing widely used or novel versions of cross-validation. The derived algorithm accurately recovered the true sparse kinetic parameters of an extremely complex simulated model of the bacteriorhodopsin photocycle, as well as the wide peak of hypothetical distributed kinetics in the presence of different noise levels. It also performed well in the analysis of the ultrafast experimental fluorescence kinetics data detected on the coenzyme FAD in a very wide logarithmic time window. We conclude that the primary application of the presented algorithms-implemented in available software-covers a wide area of studies on light-induced physical, chemical, and biological processes carried out with different spectroscopic methods. The demand for this kind of analysis is expected to soar due to the emerging ultrafast multidimensional infrared and electronic spectroscopic techniques that provide very large and complex datasets. In addition, simulations based on our methods could help in designing the technical parameters of future experiments for the verification of particular hypothetical models.

Project description:BACKGROUND: The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data. RESULTS: Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality. CONCLUSIONS: The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.

Dataset Information

Introduction of a new platform for parameter estimation of kinetically complex environmental systems.

Publications

Introduction of a new platform for parameter estimation of kinetically complex environmental systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets