Observability based parameter identifiability for biochemical reaction networks

Geffen, D.; Findeisen, Rolf; Schliemann, Monica; Allgöwer, Frank; Guay, Martin

doi:10.1109/acc.2008.4586807

Cited by 20 publications

(26 citation statements)

References 20 publications

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“…Observers are especially important in the on-line monitoring and control of biotechnological processes. Indeed, on-line measurements of temperature or flow rates are usually available whereas, concentrations of biomass and some products and reactants require the use of state-observers due to the lack of cheap or reliable on-line sensors (see Garcia et al (2008); Geffen et al (2008) and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Observability of Boolean networks: A graph-theoretic approach

2013

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Boolean networks (BNs) are discrete-time dynamical systems with Boolean state-variables and outputs. BNs are recently attracting considerable interest as computational models for genetic and cellular networks. We consider the observability of BNs, that is, the possibility of uniquely determining the initial state given a time sequence of outputs. Our main result is that determining whether a BN is observable is NP-hard. This holds for both synchronous and asynchronous BNs. Thus, unless P=NP, there does not exist an algorithm with polynomial time complexity that solves the observability problem. We also give two simple algorithms, with exponential complexity, that solve this problem. Our results are based on combining the algebraic representation of BNs derived by D. Cheng with a graph-theoretic approach. Some of the theoretical results are applied to study the observability of a BN model of the mammalian cell cycle.

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Section: Introductionmentioning

confidence: 99%

Observability of Boolean networks: A graph-theoretic approach

2013

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“…More generally, mechanistic models are obtained by assuming that biological systems are built up from actual or perceived components which are governed by physical laws (Fröhlich et al, 2017;Hasenauer, 2013;Pullen and Morris, 2014;White et al, 2016). It is a different strategy to empirical models which are reverse engineered from observations (Bronstein et al, 2015;Dattner, 2015;Geffen et al, 2008). Black-box modeling can be used with some limitations when there is little knowledge about the underlying biological processes (Chou and Voit, 2009).…”

Section: Review Of Modeling Strategies For Brnsmentioning

confidence: 99%

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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“…In another study, an empirical observability Gramian method was proposed for determining identifiability on a model of biological system of the NF-B signal transduction pathway (Geffen, 2008). Another approach proposed by Yao et al, (2003) uses an orthogonalization procedure to obtain a set of identifiable parameters applied to a dynamic reactor model for gas-phase ethylene-butene copolymerization.…”

Section: Parameter Sensitivity and Identifiabilitymentioning

confidence: 99%