Pub Date : 2019-09-20DOI: 10.3923/biotech.2019.55.63
H. Azzaz, H. Murad, Ebtesam Naeim Hosseany, Samy M. Abd Elhamid, A. Khair, M. Zahran
{"title":"Utilization of Hydrolyzed UF-permeate Supplemented with Different Nitrogen Sources and Vitamins for Production of Baker's Yeast","authors":"H. Azzaz, H. Murad, Ebtesam Naeim Hosseany, Samy M. Abd Elhamid, A. Khair, M. Zahran","doi":"10.3923/biotech.2019.55.63","DOIUrl":"https://doi.org/10.3923/biotech.2019.55.63","url":null,"abstract":"","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"279 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75927174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-4666-7461-5.CH006
Gerard G. Dumancas
In the modern era of science, bioinformatics play a critical role in unraveling the potential genetic causes of various diseases. Two of the most important areas of bioinformatics today, sequence analysis and genome annotation, are essential for the success of identifying the genes responsible for different diseases. These two emerging areas utilize highly intensive mathematical calculations in order to carry out the processes. Supercomputers facilitate such calculations in an efficient and time-saving manner generating high-throughput images. Thus, this chapter thoroughly discusses the applications of supercomputers in the areas of sequence analysis and genome annotation. This chapter also showcases sophisticated software and algorithms utilized by the two mentioned areas of bioinformatics.
{"title":"Applications of Supercomputers in Sequence Analysis and Genome Annotation","authors":"Gerard G. Dumancas","doi":"10.4018/978-1-4666-7461-5.CH006","DOIUrl":"https://doi.org/10.4018/978-1-4666-7461-5.CH006","url":null,"abstract":"In the modern era of science, bioinformatics play a critical role in unraveling the potential genetic causes of various diseases. Two of the most important areas of bioinformatics today, sequence analysis and genome annotation, are essential for the success of identifying the genes responsible for different diseases. These two emerging areas utilize highly intensive mathematical calculations in order to carry out the processes. Supercomputers facilitate such calculations in an efficient and time-saving manner generating high-throughput images. Thus, this chapter thoroughly discusses the applications of supercomputers in the areas of sequence analysis and genome annotation. This chapter also showcases sophisticated software and algorithms utilized by the two mentioned areas of bioinformatics.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70428787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-4666-8513-0.CH008
K. Mahata, Anasua Sarkar
Identification of cancer pathways is the central goal in the cancer gene expression data analysis. Data mining refers to the process analyzing huge data in order to find useful pattern. Data classification is the process of identifying common properties among a set of objects and grouping them into different classes. A cellular automaton is a discrete, dynamical system with simple uniformly interconnected cells. Cellular automata are used in data mining for reasons such as all decisions are made locally depend on the state of the cell and the states of neighboring cells. A high-speed, low-cost pattern-classifier, built around a sparse network referred to as cellular automata (ca) is implemented. Lif-stimulated gene regulatory network involved in breast cancer has been simulated using cellular automata to obtain biomarker genes. Our model outputs the desired genes among inputs with highest priority, which are analysed for their functional involvement in relevant oncological functional enrichment analysis. This approach is a novel one to discover cancer biomarkers in cellular spaces.
{"title":"Cancer Pathway Network Analysis Using Cellular Automata","authors":"K. Mahata, Anasua Sarkar","doi":"10.4018/978-1-4666-8513-0.CH008","DOIUrl":"https://doi.org/10.4018/978-1-4666-8513-0.CH008","url":null,"abstract":"Identification of cancer pathways is the central goal in the cancer gene expression data analysis. Data mining refers to the process analyzing huge data in order to find useful pattern. Data classification is the process of identifying common properties among a set of objects and grouping them into different classes. A cellular automaton is a discrete, dynamical system with simple uniformly interconnected cells. Cellular automata are used in data mining for reasons such as all decisions are made locally depend on the state of the cell and the states of neighboring cells. A high-speed, low-cost pattern-classifier, built around a sparse network referred to as cellular automata (ca) is implemented. Lif-stimulated gene regulatory network involved in breast cancer has been simulated using cellular automata to obtain biomarker genes. Our model outputs the desired genes among inputs with highest priority, which are analysed for their functional involvement in relevant oncological functional enrichment analysis. This approach is a novel one to discover cancer biomarkers in cellular spaces.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70429129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-0427-6.CH009
B. Tripathy, R. Mohanty, T. R. Sooraj
This chapter provides the information related to the researches enhanced using uncertainty models in life sciences and biomedical Informatics. The main emphasis of this chapter is to present the general ideas for the time line of different uncertainty models to handle uncertain information and their applications in the various fields of biology. There are many mathematical models to handle vague data and uncertain information such as theory of probability, fuzzy set theory, rough set theory, soft set theory. Literatures from the life sciences and bioinformatics have been reviewed and provided the different experimental & theoretical results to understand the applications of uncertain models in the field of bioinformatics.
{"title":"Application of Uncertainty Models in Bioinformatics","authors":"B. Tripathy, R. Mohanty, T. R. Sooraj","doi":"10.4018/978-1-5225-0427-6.CH009","DOIUrl":"https://doi.org/10.4018/978-1-5225-0427-6.CH009","url":null,"abstract":"This chapter provides the information related to the researches enhanced using uncertainty models in life sciences and biomedical Informatics. The main emphasis of this chapter is to present the general ideas for the time line of different uncertainty models to handle uncertain information and their applications in the various fields of biology. There are many mathematical models to handle vague data and uncertain information such as theory of probability, fuzzy set theory, rough set theory, soft set theory. Literatures from the life sciences and bioinformatics have been reviewed and provided the different experimental & theoretical results to understand the applications of uncertain models in the field of bioinformatics.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70431326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-3004-6.CH017
D. Patel
Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.
{"title":"Big Data Analytics in Bioinformatics","authors":"D. Patel","doi":"10.4018/978-1-5225-3004-6.CH017","DOIUrl":"https://doi.org/10.4018/978-1-5225-3004-6.CH017","url":null,"abstract":"Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70432730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-8903-7.ch045
V. K. Dhatwalia, M. Nanda
Aromatic compounds are widely distributed in nature. Free phenols are frequently liberated as metabolic intermediates during the degradation of plant materials. In recent years the natural supply of phenolic substances has been greatly increased due to the release of industrial byproducts into the environment. Phenolic compounds are hazardous pollutants that are toxic at relatively low concentration. Effluents from petrochemical, textile and coal industries contain phenolic compounds in very high concentration; therefore there is a necessity to remove phenolic compounds from the environment. Microorganisms capable of degrading phenol are common and include both aerobes and anaerobes. The use of microbial catalysts in the biodegradation of organic compounds has advanced significantly during the past three decades. The efficiency of biodegradation of organic compounds is influenced by the type of the organic pollutant, the nature of the organism, the enzyme involved, the mechanism of degradation and the nature of the influencing factors.
{"title":"Biodegradation of Phenol","authors":"V. K. Dhatwalia, M. Nanda","doi":"10.4018/978-1-5225-8903-7.ch045","DOIUrl":"https://doi.org/10.4018/978-1-5225-8903-7.ch045","url":null,"abstract":"Aromatic compounds are widely distributed in nature. Free phenols are frequently liberated as metabolic intermediates during the degradation of plant materials. In recent years the natural supply of phenolic substances has been greatly increased due to the release of industrial byproducts into the environment. Phenolic compounds are hazardous pollutants that are toxic at relatively low concentration. Effluents from petrochemical, textile and coal industries contain phenolic compounds in very high concentration; therefore there is a necessity to remove phenolic compounds from the environment. Microorganisms capable of degrading phenol are common and include both aerobes and anaerobes. The use of microbial catalysts in the biodegradation of organic compounds has advanced significantly during the past three decades. The efficiency of biodegradation of organic compounds is influenced by the type of the organic pollutant, the nature of the organism, the enzyme involved, the mechanism of degradation and the nature of the influencing factors.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70434718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-5225-8903-7.ch077
N. Babu, V. Pathak, Akash, Navneet
Large-scale production of commodities for mankind by industries did huge damage to the environment. Industrial waste contains lots of toxic materials including heavy metals were drained to water bodies like river, lakes, ponds, etc. These effluents drastically ruin water quality as well as the soil fertility. Type of industry and its raw material decides quantity and quality of the emerged wastes including both biodegradable as well as non-biodegradable. Among non-biodegradable wastes, copper, chromium, nickel, cadmium, etc. are widespread contaminants of soil, water, and these are most common heavy metals. Several heavy metals such as cadmium, mercury, and lead are highly poisonous and fatal to human as well as animals. Several plants as well as microbes respond to heavy metals by diverse biological processes like biosorption to their cell wall and entrapment in their capsule, oxidation and reduction, precipitation, complexation, etc. These responses may help significantly in the remediation of heavy metals from the contaminated sites.
{"title":"Biosorption of Heavy Metals","authors":"N. Babu, V. Pathak, Akash, Navneet","doi":"10.4018/978-1-5225-8903-7.ch077","DOIUrl":"https://doi.org/10.4018/978-1-5225-8903-7.ch077","url":null,"abstract":"Large-scale production of commodities for mankind by industries did huge damage to the environment. Industrial waste contains lots of toxic materials including heavy metals were drained to water bodies like river, lakes, ponds, etc. These effluents drastically ruin water quality as well as the soil fertility. Type of industry and its raw material decides quantity and quality of the emerged wastes including both biodegradable as well as non-biodegradable. Among non-biodegradable wastes, copper, chromium, nickel, cadmium, etc. are widespread contaminants of soil, water, and these are most common heavy metals. Several heavy metals such as cadmium, mercury, and lead are highly poisonous and fatal to human as well as animals. Several plants as well as microbes respond to heavy metals by diverse biological processes like biosorption to their cell wall and entrapment in their capsule, oxidation and reduction, precipitation, complexation, etc. These responses may help significantly in the remediation of heavy metals from the contaminated sites.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70435556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-4666-8213-9.CH009
Dariusz Mrozek
Bioinformatics as a scientific domain develops tools that enable understanding the wealth of information hidden in huge volumes of biological data. However, there are several problems in bioinformatics that, although already solved or at least equipped with promising algorithms, still require huge computing power in order to be completed in a reasonable time. Cloud computing responds to these demands. This chapter shows several cloud-based computing architectures for solving hot issues in structural bioinformatics, such as protein structure similarity searching or 3D protein structure prediction. Presented architectures have been implemented in Microsoft Azure public cloud and tested in several projects developed by Cloud4Proteins research group.
{"title":"Cloud-Based Computing Architectures for Solving Hot Issues in Structural Bioinformatics","authors":"Dariusz Mrozek","doi":"10.4018/978-1-4666-8213-9.CH009","DOIUrl":"https://doi.org/10.4018/978-1-4666-8213-9.CH009","url":null,"abstract":"Bioinformatics as a scientific domain develops tools that enable understanding the wealth of information hidden in huge volumes of biological data. However, there are several problems in bioinformatics that, although already solved or at least equipped with promising algorithms, still require huge computing power in order to be completed in a reasonable time. Cloud computing responds to these demands. This chapter shows several cloud-based computing architectures for solving hot issues in structural bioinformatics, such as protein structure similarity searching or 3D protein structure prediction. Presented architectures have been implemented in Microsoft Azure public cloud and tested in several projects developed by Cloud4Proteins research group.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70428488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.4018/978-1-4666-7332-8.CH010
R. Rashmi
Biopharmaceutical is the most upcoming segment of the pharmaceutical industry as the use of biotechnology has the potential to provide cures for the most life threatening and difficult ailments. At the same time for biopharma innovation, factors such as increasing costs, high continuous funding, and risk funding are of increasing concern for the emerging economies. This chapter measures the strength of biopharma innovation indicators in Gulf Cooperation Council (GCC) and explores the potential biopharma innovation models for gulf countries.
{"title":"Biopharma Innovation Models for Gulf Region in the Era of Globalisation","authors":"R. Rashmi","doi":"10.4018/978-1-4666-7332-8.CH010","DOIUrl":"https://doi.org/10.4018/978-1-4666-7332-8.CH010","url":null,"abstract":"Biopharmaceutical is the most upcoming segment of the pharmaceutical industry as the use of biotechnology has the potential to provide cures for the most life threatening and difficult ailments. At the same time for biopharma innovation, factors such as increasing costs, high continuous funding, and risk funding are of increasing concern for the emerging economies. This chapter measures the strength of biopharma innovation indicators in Gulf Cooperation Council (GCC) and explores the potential biopharma innovation models for gulf countries.","PeriodicalId":93084,"journal":{"name":"Biotechnology (Faisalabad, Pakistan)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70428613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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