A Kalman Filter Approach for Biomolecular Systems with Noise Covariance Updating

Dey, Abhishek; Chakrabarti, Kushal; Gola, Krishan Kumar; Sen, Shaunak

doi:10.1109/icc47138.2019.9123219

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…The key assumption of using standard Kalman filter is linearity of measurements. Kalman filter is used with CME approximations in (Dey et al, 2018) while estimating noise covariance and allowing for dependency of noise on states and parameter values. Kalman filter is used to obtain initial guess of parameter values for data fitting parameter estimation in (Lillacci and Khammash, 2010b).…”

Section: Other Statistical Methodsmentioning

confidence: 99%

“…Deterministic and stochastic diffusion approximations of stochastic kinetics are reviewed in (Mozgunov et al, 2018). Chemical Langevin equation (CLE) is a SDE consisting of deterministic part describing Draft slow macroscopic changes, and stochastic part representing fast microscopic changes (Golightly et al, 2012;Dey et al, 2018;Cseke et al, 2016) which are dependent on the size of deterministic part. In the limit, as deterministic part increases, random fluctuations can be neglected, and deterministic kinetics of the Langevin equation becomes reaction the rate equation (RRE) (Bronstein et al, 2015;Fr öhlich et al, 2016;Loos et al, 2016).…”

Section: Modeling Brns By Differential Equationsmentioning

confidence: 99%

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Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

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The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

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Section: Other Statistical Methodsmentioning

confidence: 99%

Section: Modeling Brns By Differential Equationsmentioning

confidence: 99%

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

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“…In 2010, Lillacci and Khammash [2010] proposed an algorithm to infer parameters of ODE-driven systems through Kalman filters and proofed their concept on the heat shock response in E. coli and a synthetic gene regulation system. Similarly, Dey et al [2018] combined Kalman Filters with MCMC to create a robust algorithm for parameter inference in biomolecular systems.…”

Section: Parameter Inferencementioning

confidence: 99%

Parameter Inference for an Astrocyte Model using Machine Learning Approaches

Fritschi

Lenk

2023

Preprint

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Astrocytes are the largest subset of glial cells and perform structural, metabolic, and regulatory functions. They are directly involved in the communication at neuronal synapses and the maintenance of brain homeostasis. Several disorders, such as Alzheimer's, epilepsy, and schizophrenia, have been associated with astrocyte dysfunction. Computational models on various spatial levels have been proposed to aid in the understanding and research of astrocytes. The difficulty of computational astrocyte models is to fastly and precisely infer parameters. Physics informed neural networks (PINNs) use the underlying physics to infer parameters and, if necessary, dynamics that can not be observed. We have applied PINNs to estimate parameters for a computational model of an astrocytic compartment. The addition of two techniques helped with the gradient pathologies of the PINNS, the dynamic weighting of various loss components and the addition of Transformers. To overcome the issue that the neural network only learned the time dependence but did not know about eventual changes of the input stimulation to the astrocyte model, we followed an adaptation of PINNs from control theory (PINCs). In the end, we were able to infer parameters from artificial, noisy data, with stable results for the computational astrocyte model.

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A Kalman Filter Approach for Biomolecular Systems with Noise Covariance Updating

Cited by 2 publications

References 29 publications

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

Parameter Inference for an Astrocyte Model using Machine Learning Approaches

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