Ga yoon Kim
Principle of Bioinformatics
- Definition of Bioinformatics.
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data.
- History of Bioinformatics.
Paulien Hogeweg and Ben Hesper coined the term Bioinformatics in 1970 to refer to the study of information processes in biotic system. This definition placed Bioinformatics as a field parallel to Biophysics or Biochemistry.
- Structural Bioinformatics.
Protein structure prediction is another important applicaiton of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it.
- Goals of Bioinformatics.
1. Development and implementation of computer programs that enable efficient access to, use and management of, various types of information.
2. Development of new algorithms and statistical measures that assess relationships among members of large data sets.
- Principles of Bioinformatics.
Bioinformatics is a new interdiscipline at the interface of computer science, mathematics and biology. It is naturally characteriized more by the problem domains it addresses than by a foundational set of philosophical or scientific principles.
- References.
1. https://en.wikipedia.org/wiki/Bioinformatics
2. http://14.139.227.92/mkumar/Teaching/intro2.pdf
Bioprogramming Class
- Definition of Bioprogramming.
Bioprogramming is analyzing big data for any biological thing.
- References.
My idea.
Genomics Class
- Definition of Genomics.
Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyze the function and structure of genomes (the complete set of DNA within a single cell of an organism).
- Research areas of Genomics.
1. Functional Genomics.
2. Structural Genomics.
3. Epigenomics.
4. Metagenomics.
- Applications of Genomics.
Genomics has provided applications in many fields, including medicine, biotechnology, anthropology and other social sciences.
- References.
1. https://en.wikipedia.org/wiki/Genomics
Transcriptomics Class
- Definition of Transcriptomics.
Transcriptomics is the study of the transcriptome-the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell-using high-throughput methods, such as microarray analysis. Comparison of transcriptomes allows the identification of genes that are differentially expressed in distinct cell populations, or in response to different treatments.
- Applications of Transcriptomics.
Transcriptomics is an emerging and continually growing field in biomarker discovery for use in assessing the safety of drugs or chemical risk assessment.
- References.
1. http://www.nature.com/subjects/transcriptomics
2. https://en.wikipedia.org/wiki/Transcriptome
Proteomics Class
- Definition of Proteomics.
Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. The term proteomics was first coined in 1997 to make an analogy with genomics, the study of the genome.
- Practical applications of Proteomics.
One major development to come from the study of human genes and proteins has been the identification of potential new drugs for the treatment of disease. This relies on genome and proteome information to identify proteins associated with a disease, which computer software can then use as targets for new drugs.
Proteomics is also used to reveal complex plant-insect interactions that help identify candidate genes involved in the defensive response of plants to herbivory.
- Structural Proteomics.
Structural proteomics includes the analysis of protein structures at large-scale. It compares protein structures and helps identify functions of newly discovered genes. The structural analysis also helps to understand that where drugs bind to proteins and also show where proteins interact with each other. This understanding is achieved using different technologies such as X-ray crystallography and NMR spectroscopy.
- Current proteomic technologies.
Proteomics has steadily gained momentum over the past decade with the evolution of several approaches. Few of these are new and others build on traditional methods. Mass spectrometry-based methods and micro arrays are the most common technologies for large-scale study of proteins.
- Emerging trends in Proteomics.
A number of emerging concepts have the potential to improve current features of proteomics. Obtaining absolute quantification of proteins and monitoring post-translational modifications are the two tasks that impacts the understanding of protein function in healthy and diseased cells. Advances in quantitative proteomics would clearly enable more in-depth analysis of cellular systems. For many cellular events, the protein concentrations do not change; rather, their function is modulated by post-transitional modifications (PTM). Methods of monitoring PTM are an underdeveloped area in proteomics. Selecting a particular subset of protein for analysis substantially reduces protein complexity, making it advantageous for diagnostic purposes where blood is the starting material. Another important aspect of proteomics, yet not addressed, is that proteomics methods should focus on studying proteins in the context of the environment. The increasing use of chemical cross linkers, introduced into living cells to fix protein-protein, protein-DNA and other interactions, may ameliorate this problem partially. The challenge is to identify suitable methods of preserving relevant interactions. Another goal for studying protein is to develop more sophisticated methods to image proteins and other molecules in living cells and real time.
- References.
1. https://en.wikipedia.org/wiki/Proteomics
Epigenomics
- Definition of Epigenomics.
Epigenomics is the study of the complete set of epigenetic modifications on the genetic material of a cell, known as the epigenome. The field is analogous to genomics and proteomics, which are the study of the genome and proteome of a cell.
- Relation to other genomic fields.
Epigenomics shares many commonalities with other genomics fields, in both methodology and in its abstract purpose. Epigenomics seeks to identify and characterize epigenetic modifications on a global level, similar to the study of the complete set of DNA in genomics or the complete set of proteins in a cell in proteomics. The logic behind performing epigenetic analysis on a global level is that inferences can be made about epigenetic modifications, which might not otherwise be possible through analysis of specific loci. As in the other genomics fields, epigenomics relies heavily on bioinformatics, which combines the disciplines of biology, mathematics and computer science. However while epigenetic modifications had been known and studied for decades, it is through these advancements in bioinformatics technology that have allowed analyses on a global scale. Many current techniques still draw on older methods, often adapting them to genomic assays as is described in the next section.
- Epigenomics Methods.
1. Histone modification assays.
2. DNA Methylation assays.
3. Direct Detection.
- Theoretical modeling approaches.
First mathematical models for different nucleosome states affecting gene expression were introduced in 1980s. Later, this idea was almost forgotten, until the experimental evidence has indicated a possible role of covalent histone modifications as an epigenetic code. In the next several years, high-throughput data have indeed uncovered the abundance of epigenetic modifications and their relation to chromatin functioning which has motivated new theoretical models for the appearance, maintaining and changing these patterns. These models are usually formulated in the frame of one-dimensional lattice approaches.
- References.
1. https://en.wikipedia.org/wiki/Epigenomics#Relation_to_other_genomic_fields
- Definition of Cancer.
Cancer, also known as a malignant tumor or malignant neoplasm, is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body.
- Symptoms of Cancer.
General symptoms occur due to distant effects of the cancer that are not related to direct or metastatic spread. These may include: unintentional weight loss, fever, being excessively tired, and changes to the skin. Hodgkin disease, leukemias, and cancers of the liver or kidney can cause a persistent fever of unknown origin.
Some cancers may cause specific groups of systemic symptoms, termed paraneoplastic phenomena. Examples include the appearance of myasthenia gravis in thymoma and clubbing in lung cancer.
- Causes of Cancer.
The great majority of cancers, some 90–95% of cases, are due to environmental factors. The remaining 5–10% are due to inherited genetics. Environmental, as used by cancer researchers, means any cause that is not inherited genetically, such as lifestyle, economic and behavioral factors, and not merely pollution. Common environmental factors that contribute to cancer death include tobacco (25–30%), diet and obesity (30–35%), infections (15–20%), radiation (both ionizing and non-ionizing, up to 10%), stress, lack of physical activity, and environmental pollutants.
- Definition of Aging.
Aging is the process of becoming older. It represents the accumulation of changes in a person over time. In humans, ageing refers to a multidimensional process of physical, psychological, and social change.
- Effects of Aging.
Age is a major risk factor for most common neurodegenerative diseases, including Mild cognitive impairment, Alzheimer's disease, cerebrovascular disease, Parkinson's disease and Lou Gehrig's disease. Steady decline in many cognitive processes is seen across the lifespan, accelerating from the twenties or even thirties. Research has focused in particular on memory and ageing and has found decline in many types of memory with ageing, but not in semantic memory or general knowledge such as vocabulary definitions, which typically increases or remains steady until the late adulthood. Early studies on changes in cognition with age generally found declines in intelligence in the elderly, but studies were cross-sectional rather than longitudinal and thus results may be an artefact of cohort rather than a true example of decline. However, longitudinal studies could be confounded due to prior test experience. Intelligence may decline with age, though the rate may vary depending on the type and may in fact remain steady throughout most of the lifespan, dropping suddenly only as people near the end of their lives. Individual variations in rate of cognitive decline may therefore be explained in terms of people having different lengths of life. There are changes to the brain: though neuron loss is minor after 20 years of age there is a 10% reduction each decade in the total length of the brain's myelinated axons.
- References.
