scholarly journals Algorithms to Study Large Metabolic Network Dynamics

2015 ◽  
Vol 10 (5) ◽  
pp. 100-118 ◽  
Author(s):  
D. Grigoriev ◽  
S. S. Samal ◽  
S. Vakulenko ◽  
A. Weber
Keyword(s):  
Metabolic Network ◽  
Network Dynamics

scholarly journals Visualizing metabolic network dynamics through time-series metabolomic data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Lea F. Buchweitz ◽  
James T. Yurkovich ◽  
Christoph Blessing ◽  
Veronika Kohler ◽  
Fabian Schwarzkopf ◽  
...  
Keyword(s):  
Time Series ◽  
Metabolic Network ◽  
Network Dynamics ◽  
Metabolomic Data

Metabolic network dynamics in open chaotic flow

2002 ◽  
Vol 12 (2) ◽  
pp. 460-469 ◽  
Author(s):  
György Károlyi ◽  
István Scheuring ◽  
Tamás Czárán
Keyword(s):  
Metabolic Network ◽  
Network Dynamics ◽  
Chaotic Flow

scholarly journals A NEW METABOLOMICS ANALYSIS TECHNIQUE: STEADY-STATE METABOLIC NETWORK DYNAMICS ANALYSIS

2012 ◽  
Vol 10 (01) ◽  
pp. 1240003 ◽  
Author(s):  
ALI CAKMAK ◽  
XINJIAN QI ◽  
A. ERCUMENT CICEK ◽  
ILYA BEDERMAN ◽  
LEIGH HENDERSON ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Network ◽  
Network Dynamics ◽  
Nutritional Deficiencies ◽  
Dynamics Analysis ◽  
Network Database ◽  
Computational Performance ◽  
Analysis Technique ◽  
Common Task ◽  
Output Space

With the recent advances in experimental technologies, such as gas chromatography and mass spectrometry, the number of metabolites that can be measured in biofluids of individuals has markedly increased. Given a set of such measurements, a very common task encountered by biologists is to identify the metabolic mechanisms that lead to changes in the concentrations of given metabolites and interpret the metabolic consequences of the observed changes in terms of physiological problems, nutritional deficiencies, or diseases. In this paper, we present the steady-state metabolic network dynamics analysis (SMDA) approach in detail, together with its application in a cystic fibrosis study. We also present a computational performance evaluation of the SMDA tool against a mammalian metabolic network database. The query output space of the SMDA tool is exponentially large in the number of reactions of the network. However, (i) larger numbers of observations exponentially reduce the output size, and (ii) exploratory search and browsing of the query output space is provided to allow users to search for what they are looking for.

scholarly journals Genome-Wide Analysis Reveals Gene Expression and Metabolic Network Dynamics during Embryo Development in Arabidopsis

2011 ◽  
Vol 156 (1) ◽  
pp. 346-356 ◽  
Author(s):  
Daoquan Xiang ◽  
Prakash Venglat ◽  
Chabane Tibiche ◽  
Hui Yang ◽  
Eddy Risseeuw ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Network ◽  
Embryo Development ◽  
Network Dynamics ◽  
Genome Wide Analysis ◽  
Genome Wide

OBSERVATION CONFLICT RESOLUTION IN STEADY-STATE METABOLIC NETWORK DYNAMICS ANALYSIS

2012 ◽  
Vol 10 (01) ◽  
pp. 1240004 ◽  
Author(s):  
A. ERCUMENT CICEK ◽  
GULTEKIN OZSOYOGLU
Keyword(s):  
Conflict Resolution ◽  
Steady State ◽  
Metabolic Network ◽  
Experimental Evaluation ◽  
Network Dynamics ◽  
Metabolic Activation ◽  
Dynamics Analysis ◽  
Network Database ◽  
Number Of Factors ◽  
Inherent Error

Steady state metabolic network dynamics analysis (SMDA) is a recently proposed computational metabolomics tool that (i) captures a metabolic network and its rules via a metabolic network database, (ii) mimics the reasoning of a biochemist, given a set of metabolic observations, and (iii) locates efficiently all possible metabolic activation/inactivation (flux) alternatives. However, a number of factors may cause the SMDA algorithm to eliminate feasible flux scenarios. These factors include (i) inherent error margins in observations (measurements), (ii) lack of knowledge to classify measurements as normal versus abnormal, and (iii) choosing a highly constrained metabolic subnetwork to query against. In this work, we first present and formalize these obstacles. Then, we propose techniques to eliminate them and present an experimental evaluation of our proposed techniques.

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