Identification of biochemical networks by S-tree based genetic programming

Cho, Dong-Yeon; Cho, Kwang-Hyun; Zhang, Byoung-Tak

doi:10.1093/bioinformatics/btl122

Cited by 88 publications

(68 citation statements)

References 29 publications

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“…In particular, recent progress in genomics and proteomics makes it possible to understand the biological units and processes at the systems level. These include gene regulation networks [7], metabolic pathways [5], signal transduction networks [2] [8], and other protein interaction networks [9].…”

Section: Introductionmentioning

confidence: 99%

Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency

Zhang¹

2007

2007 IEEE Symposium on Foundations of Computational Intelligence

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Abstract-Recent progress in genomics and proteomics makes it possible to understand the biological networks at the systems level. We aim to develop computational models of learning and memory inspired by the biomolecular networks embedded in their environment. One fundamental question is how the systems rapidly adapt to their changing environment in a short period (learning) while performing persistently through the longer time span (memory). We study this issue in a probabilistic hypergraph model called the hypernetworks. The hypernetwork architecture consists of a huge number of randomly sampled hyperedges, each corresponding to higherorder micromodules in the input. We find that a system consisting of a large number of a wide range of heterogenous low-dimensional components has a fairly competitive chance of long-term survival (memory, persistency) and short-term performance (learning, adaptability) as opposed to a system consisting of a small number of high-dimensional, fine-tuned, complex components. Empirical evidence is offered to support these findings and theoretical explanations are given.

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Section: Introductionmentioning

confidence: 99%

Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency

Zhang¹

2007

2007 IEEE Symposium on Foundations of Computational Intelligence

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“…Others proposed various decomposition schemes for different types of parameters [255,[324][325][326].…”

Section: Parameter Estimation/inverse Problemsmentioning

confidence: 99%

“…In particular, it showed how traditional enzyme kinetic information can be validly converted into power-law models that offer additional modes of analysis and novel insights. is more complex model was later used as a test bed for various optimization and estimation [3,4,255,319,409,[510][511][512][513][514][515][516][517][518][519][520].…”

Section: Microbial Studiesmentioning

confidence: 99%

Biochemical Systems Theory: A Review

Voit

2013

ISRN Biomathematics

144

154

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Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. is paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. e paper is intended as a guide for investigators entering the fascinating �eld of biological systems analysis and as a resource for practitioners and experts. PreambleBiochemical systems theory (BST) is a mathematical and computational framework for analyzing and simulating systems. It was originally developed for biochemical pathways but by now has become much more widely applied to systems throughout biology and beyond. BST is called "canonical, " which means that model construction, diagnosis, and analysis follow stringent rules, which will be discussed throughout this paper. e key ingredient of BST is the power-law representation of all processes in a system.BST has been the focus of a number of books [1-6], including some in Chinese and Japanese [7][8][9]. e most detailed modern text dedicated speci�cally to BST is [3]. Moreover, since its inception, numerous reviews have portrayed the evolving state of the art in BST. Most of these reviews summarized methodological advances for the analysis of biochemical pathway systems . Others compared BST models with alternative modeling frameworks [32,[51][52][53][54][55][56][57][58][59][60][61][62][63]; some of these comparisons will be discussed later. Yet other reviews focused on specialty areas, such as customized methods for optimizing BST models (e.g., [64][65][66][67]) or estimating their parameters (e.g., [68][69][70][71][72]), strategies for discovering design and operating principles in natural system (e.g., [73][74][75][76][77][78]), and even the use of BST models in statistics [79][80][81]. A historical account of the �rst twenty years of BST was presented in [82].Supporting the methodological developments in the �eld, several soware packages were developed for different aspects of BST analysis. e earliest was ESSYNS [83][84][85], which supported all standard steady-state analyses and also contained a numerical solver that, at the time, was many times faster than any off-the-shelf soware [86,87]; see also [88]. Utilizing advances in computing and an extension of the solver from ESSYNS, Ferreira created the very user-friendly package PLAS, which is openly available and still very widely used [89] (see also [3]). Yamada and colleagues developed soware to translate E-cell models into BST models in PLAS format [90]. Okamoto's group created the soware BestKit, which permits the graphical design and translation of models, along with their analysis [91][92][93]. Vera and colleagues developed the soware tool PLMaddon for analyzing BST models within SBML [94]; see also [95]. It expands the Matlab SBToolbox with speci�c functionalities for power-law repr...

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“…• Cho et al [11] evolved GRNs and other biochemical networks using an S-tree model which describes a sparse network of non-linear differential equations.…”

Section: Learning Deterministic Grn Models With Evolutionary Algorithmsmentioning

confidence: 99%

Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

Imada

Ross

2011

New Gener. Comput.

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A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves.Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic π-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

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Identification of biochemical networks by S-tree based genetic programming

Cited by 88 publications

References 29 publications

Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency

Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency

Biochemical Systems Theory: A Review

Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

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