MODELLING LIPID BIOSYNTHESIS PATHWAYS OF OIL PALM BY BOOLEAN AND GRAPHICAL APPROACHES

Date

2011-05-05

Author

Quek, Emily Ming Poh
Purutçuoğlu Gazi, Vilda
Sambanthamurthi, Ravigadevi
WilhelmWeber, Gerhard

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

197
views

0
downloads

The mathematical modelling provides an alternative way for the interpretation and prediction of cellular metabolisms. This study delves into the modelling of lipid biosynthesis pathway and the primary metabolism in the fruit of the oil palm (Elaeis guineensis). Two different data are used for the analysis. In the first dataset, the distribution of carbon flux into the major classes of lipids, i.e., triacylglycerols, diacylglycerols, monoacylglycerols, phospholipids, and free fatty acids, is investigated using radioactive isotope. Then, the model of carbon flux is constructed based on the Boolean approach. On the other hand in the second dataset, the graphical methods are implemented to construct a model for the lipid contents of the developing oil palm mesocarp based on the major lipid metabolites of this system as listed previously. Moreover, the generated models are analysed for the control structure depending on the relative ranking of degree for each lipid class in the pathway. The results demonstrate the ability of the models to predict the site of control in this biosynthetic system. Furthermore, the analyses of the networks of lipids do not only reveal underlying interactions between the major classes of lipids but also the response of these networks towards changing environments.

Subject Keywords

GENETIC NETWORKS, BIOCHEMISTRY

URI

https://hdl.handle.net/11511/53425

Conference Name

6th International Symposium on Health Informatics and Bioinformatics (HIBIT)

Collections

Department of Statistics, Conference / Seminar

Suggestions

OpenMETU
Core

Comparison of two inference approaches in Gaussian graphical models Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01) Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.
Examination of the dielectrophoretic spectra of MCF7 breast cancer cells and leukocytes Çağlayan, Zeynep; Demircan Yalçın, Yağmur; Külah, Haluk (Wiley, 2020-03-01) The detection of circulating tumor cells (CTCs) in blood is crucial to assess metastatic progression and to guide therapy. Dielectrophoresis (DEP) is a powerful cell surface marker-free method that allows intrinsic dielectric properties of suspended cells to be exploited for CTC enrichment/isolation from blood. Design of a successful DEP-based CTC enrichment/isolation system requires that the DEP response of the targeted particles should accurately be known. This paper presents a DEP spectrum method to inve...
Neural networks with piecewise constant argument and impact activation Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011) This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites Kockan, Umit; Ozturk, Fahrettin; Evis, Zafer (2014-01-01) In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted ...
Asymptotical lower limits on required number of examples for learning boolean networks Abul, Osman; Alhajj, Reda; Polat, Faruk (2006-11-03) This paper studies the asymptotical lower limits on the required number of samples for identifying Boolean Networks, which is given as Omega(logn) in the literature for fully random samples. It has also been found that; O(logn) samples are sufficient with high probability. Our main motivation is to provide tight lower asymptotical limits for samples obtained from time series experiments. Using the results from the literature on random boolean networks, lower limits on the required number of samples from tim...

Citation Formats

E. M. P. Quek, V. Purutçuoğlu Gazi, R. Sambanthamurthi, and G. WilhelmWeber, “MODELLING LIPID BIOSYNTHESIS PATHWAYS OF OIL PALM BY BOOLEAN AND GRAPHICAL APPROACHES,” presented at the 6th International Symposium on Health Informatics and Bioinformatics (HIBIT), Izmir Univ Econ, Izmir, TURKEY, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53425.