Hierarchy cuckoo search algorithm for parameter estimation in biological systems

Rakhshani, Hojjat; Dehghanian, Effat; Rahati, Amin

doi:10.1016/j.chemolab.2016.10.011

Cited by 20 publications

(6 citation statements)

References 39 publications

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“…Motivation and selected papers Genetic algorithms (GAs) largest class of EAs, inspired by evolution and natural selection, often near optimum solution Sun et al (2012); Chou and Voit (2009) Genetic programming (GP) evolution of computer programs towards improving their fitness to solve a given task Nobile et al (2013); Chou and Voit (2009); Sun et al (2012) Evolutionary programming (EP) parameters of computer program evolve towards improving its fitness to solve a given task Baker et al (133, 2010); Sun et al (2012); Revell and Zuliani (2018) Simulated annealing (SA) probabilistic search combining sampling with random but controlled acceptance of candidate solutions Cuckoo search employs random sub-populations which can be discarded to improve the solution (Rakhshania et al, 2016). Optimization programs include non-linear simplex method (Cazzaniga et al, 2015), non-linear programming (NLP) (Moles et al, 2003;Rodriguez-Fernandez et al, 2013;Sun et al, 2012;Zhan and Yeung, 2011), semi-definite programming (Kuepfer et al, 2007;Rumschinski et al, 2010), and quadratic programming (Gupta, 2013).…”

Section: Algorithmmentioning

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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Section: Algorithmmentioning

confidence: 99%

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

2019

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“…Many applications concern the optimization of a system or its important parts, and researchers have used cuckoo search and its variants to carry out optimization such as optimal positioning platform [14], structural optimization [23], affinity propagation [28], aerodynamic shape optimization [47], optimization related to wellbore trajectories [75], dimensionality reduction [78], overhead crane system optimization [90] and parameter estimation in biological systems [56].…”

Section: Design Optimizationmentioning

confidence: 99%

Cuckoo search: State-of-the-art and opportunities

Yang

Deb

2017

2017 IEEE 4th International Conference on Soft Computing &Amp; Machine Intelligence (ISCMI)

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“…Eggs which are more similar to host nest eggs have a bigger chance to survive others are detected by host bird and thrown away. The grown eggs show the surviving rate in those nests [31]. Cuckoo search algorithm consists of three main steps: 1) Initialization.…”

Section: Cuckoo Search Algorithms For the Identification Of An Optimamentioning

confidence: 99%

Non-uniform embedding applications for synchronization analysis

Drulyte¹,

Lukoševičiūtė²,

Ragulskiene³

2016

J. vibroeng.

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Hierarchy cuckoo search algorithm for parameter estimation in biological systems

Cited by 20 publications

References 39 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

Cuckoo search: State-of-the-art and opportunities

Non-uniform embedding applications for synchronization analysis

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