Parameter optimization in S-system models

Vilela, Marco; Chou, I-Chun; Vinga, Susana; Vasconcelos, Ana Tereza Ribeiro de; Voit, Eberhard O.; Almeida, Jonas S.

doi:10.1186/1752-0509-2-35

Cited by 80 publications

(49 citation statements)

References 21 publications

(20 reference statements)

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“…Eigenvector centrality defines the ''prestige'' of a node in a network. In other words, a node in a network is more central if it is connected to many central nodes (nodes with high degree) [30].…”

Section: Centralities Parameters and Topological Analysismentioning

confidence: 99%

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples

et al. 2016

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Corticosteroid resistance (CR) is a major barrier to the effective treatment of severe asthma. Hence, a better understanding of the molecular mechanisms involved in this condition is a priority. Network analysis is an emerging strategy to explore this complex heterogeneous disorder at system level to identify a small own network for CR in asthma. Gene expression profile of GSE7368 from bronchoalveolar lavage (BAL) of CR in subjects with asthma was downloaded from the gene expression omnibus (GEO) database and compared to BAL of corticosteroid-sensitive (CS) patients. DEGs were identified by the Limma package in R language. In addition, DEGs were mapped to STRING to acquire protein-protein interaction (PPI) pairs. Topological properties of PPI network were calculated by Centiscape, ClusterOne and BINGO. Subsequently, text-mining tools were applied to design one own cell signalling for CR in asthma. Thirty-five PPI networks were obtained; including a major network consisted of 370 nodes, connected by 777 edges. After topological analysis, a minor PPI network composed by 48 nodes was indentified, which is composed by most relevant nodes of major PPI network. In this subnetwork, several receptors (EGFR, EGR1, ESR2, PGR), transcription factors (MYC, JAK), cytokines (IL8, IL6, IL1B), one chemokine (CXCL1), one kinase (SRC) and one cyclooxygenase (PTGS2) were described to be associated with inflammatory environment and steroid resistance in asthma. We suggest a biomarker network composed by 48 nodes that could be potentially explored with diagnostic or therapeutic use.

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Section: Centralities Parameters and Topological Analysismentioning

confidence: 99%

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples

et al. 2016

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“…Seatzu proposed B-splines [335,336], and Vilela et al composed a smoother that accounted for the noise structure in the data [337,338]. Wang et al compared various alternatives, including B-splines [339].…”

Section: Parameter Estimation/inverse Problemsmentioning

confidence: 99%

Biochemical Systems Theory: A Review

Voit

2013

ISRN Biomathematics

146

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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“…Over the last two decades the systems biology community has witnessed rapid advances in system identification methods development including the following: linear, loglinear and nonlinear differential equations; artificial neural networks [53]; genetic algorithm [54]; evolutionary optimization with data collocation [55]; interval analysis [56]; alternating regression [57]; parameter estimation for noisy metabolic profiles using newton-flow analysis [58]; simulated annealing [59]; ant colony optimization algorithm for parameter estimation and network inference [60]; substitution of slopes for differentials; dynamic-flux estimation [61]; eigen-vector optimization [62]; transposive and repressive regression method [63][64][65]; and so on. For instance, the following presented ODE-based methods that use dynamic data and steady-state measurements to capture and identify complex systems: Sorribas and Cascante et al [66]; Irvine [67]; Savageau et al [68]; Tominaga and Okamoto [69]; etc.…”

Section: Inroductionmentioning

confidence: 99%

Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

Idowu¹

2013

Bidiscovery

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The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways.

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Parameter optimization in S-system models

Cited by 80 publications

References 21 publications

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples

Biochemical Systems Theory: A Review

Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

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