Biomaterial Database

Aromatic compounds biodegradation under anaerobic conditions and their QSBR models. 2006

Hongwei Yang, and Zhanpeng Jiang, and Shaoqi Shi

Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, China. yangw@tsinghua.edu.cn

Anaerobic biodegradability of 46 kinds of aromatic compounds was tested and assessed in integrate. These aromatic compounds were classified into readily, partially and poorly biodegradable compounds after their integrated assessment indices (IAI) were calculated. Some rules of anaerobic biodegradation of them were drawn. Stepwise regression and backpropagation artificial neural network (BP-ANN) methods were applied to establish quantitative structure biodegradability relationship (QSBR) based on the assessment results. In QSBR models, five molecular structure descriptors, energy of the highest occupied molecular orbital (EHOMO), total energy (TolE), molar refractivity (MR), the logarithm of the partition coefficient for n-octanol/water (LogP), and standard Gibbs free energy (G), were included. After analyzing the sensitivity of variables in QSBR models, it was found that the key molecular structure descriptors affecting anaerobic biodegradability of aromatic compounds were TolE and MR, which were in direct proportion to the anaerobic biodegradability of aromatic compounds.

UI	MeSH Term	Description	Entries
D008962	Models, Theoretical	Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.	Experimental Model,Experimental Models,Mathematical Model,Model, Experimental,Models (Theoretical),Models, Experimental,Models, Theoretic,Theoretical Study,Mathematical Models,Model (Theoretical),Model, Mathematical,Model, Theoretical,Models, Mathematical,Studies, Theoretical,Study, Theoretical,Theoretical Model,Theoretical Models,Theoretical Studies
D012044	Regression Analysis	Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.	Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D004785	Environmental Pollutants	Substances or energies, for example heat or light, which when introduced into the air, water, or land threaten life or health of individuals or ECOSYSTEMS.	Environmental Pollutant,Pollutant,Pollutants,Pollutants, Environmental,Pollutant, Environmental
D006841	Hydrocarbons, Aromatic	Organic compounds containing carbon and hydrogen in the form of an unsaturated, usually hexagonal ring structure. The compounds can be single ring, or double, triple, or multiple fused rings.	Aromatic Hydrocarbon,Aromatic Hydrocarbons,Hydrocarbon, Aromatic
D001421	Bacteria, Anaerobic	Bacteria that can survive and grow in the complete, or nearly complete absence of oxygen.	Anaerobic Bacteria
D001673	Biodegradation, Environmental	Elimination of ENVIRONMENTAL POLLUTANTS; PESTICIDES and other waste using living organisms, usually involving intervention of environmental or sanitation engineers.	Bioremediation,Phytoremediation,Natural Attenuation, Pollution,Environmental Biodegradation,Pollution Natural Attenuation
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron