Challenges of Nanotechnology Dr.A.Ramakrishna Associate Professor Dept. of Education Osmania University, Hyderabad, A.P. INDIA Nanotechnology promises significant advances in electronics, materials, biotechnology, alternative energy sources & other applications.
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Dept. of Education
Osmania University, Hyderabad, A.P.
Nanotechnology promises significant advances in electronics, materials, biotechnology, alternative energy sources & other applications.
Nanocrystals, nanotubes, nanowires, nanofibers – next generation materials.
No single person can provide the answers to the challenges that bring nanotechnology, nor can any single group or intellectual discipline. However, those who know the technology best (those who create it) must ultimately prepare the agenda for broad discussion, and participate fully in creation of relevant policy. In the realm of nanotechnology, public policy and science have become inseparable.
The five main challenges are to develop instruments to assess exposure to engineered nano-materials in the air and water and we think that that challenge will take three to ten years. The emergence of new nano-technologies we feel that there is a very real need to monitor exposure to humans in the air and within water. The challenge becomes increasingly difficult in more complex matrices like food.
The second challenge would be to develop and validate methods to evaluate the toxicity of engineered nano-materials within the next 5 to 15 years.
To develop models for predicting the potential impact of engineered nano-materials on the environment and human health.
The next challenge would be to develop reverse systems to evaluate impact on the environment and the health impact of engineered nano-materials over their entire life span, which speaks to the life cycle issue.The fifth is more of a grand challenge to develop the tools to properly assess risk to human health and to the environment.
The Food and Drug Administration attempts to ensure materials that are safe and effective. EPA ensures that there is no demonstrable harm to an environment or to people in that environment. Nano-materials are just another instance of the wide array of technologies that we have developed. The regulatory agencies need to have adequate resources to monitor nano molecules properly.
Pool resources internationally and with the issue of hazard identification of, exposure to, and risk analysis of engineered nano-materials.
Many assumptions about risk assessment and risk management that work in the macro world we all inhabit will also work for nanotechnology and nanomaterials, but some issues may be unique to nanomaterials because of their small size.
Some have suggested that the surface area of a nanoparticle is really a key parameter in determining how much of the material produces a toxic effect.
The charge of the particle that affects how much it can be absorbed across the cell membrane.
Dept. of Education
Osmania University, Hyderabad, A.P.
“Bioinformatics is the Science of Managing and Analysing Genomic (Molecular) Data.”
Ewen and Grants (2001) “ The application of mathematics, statistics, and information technology, including computers and the theory surrounding them, to the study and analysis of very large biological, and particularly genetic, data sets.... in particular the data from the human genome project, as well as other genome projects.
Seillier-Moiseiwitschetal., 2002 “The term proteome denotes the PROTEin complement expressed by a genOME or tissue. While the genome is an invariant feature of an organism, the proteome depends on its development stage, the tissue considered, and environmental/experimental conditions.
The first time, the term “Proteomes” was coined in 1994 by Marc Wilkins & Keith Williams who defined it as “Proteomes contain the total protein expression of a set of chromosomes”.
The terms bioinformatics and computational biology are often used interchangeably. However bioinformatics more properly refers to the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems inspired from the management and analysis of biological data. Computational biology, on the other hand, refers to hypothesis-driven investigation of a specific biological problem using computers, carried out with experimental or simulated data, with the primary goal of discovery and the advancement of biological knowledge. Put more simply, bioinformatics is concerned with the information while computational biology is concerned with the hypotheses.
Hypothesis-driven research in computational biology and technique-driven research in bioinformatics. Bioinformatics is also often specified as an applied subfield of the more general discipline of Biomedical informatics.
A representative problem in bioinformatics is the assembly of high-quality genome sequences from fragmentary "shotgun" DNA sequencing. Other common problems include the study of gene regulation using data from microarrays or mass spectrometry.
Since the Phage Φ-X174 was sequenced in 1977, the DNA sequences of hundreds of organisms have been decoded and stored in databases.
In the case of the Human Genome Project, it took several months of CPU time (on a circa-2000 vintage DEC Alpha computer) to assemble the fragments. Shotgun sequencing is the method of choice for virtually all genomes sequenced today, and genome assembly algorithms are a critical area of bioinformatics research.
Another aspect of bioinformatics in sequence analysis is the automatic search for genes and regulatory sequences within a genome.
Bioinformatics helps to bridge the gap between genome and proteome projects--for example, in the use of DNA sequences for protein identification.
In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. The first genome annotation software system was designed in 1995 by Dr. Owen White, who was part of the team that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae.
Computational evolutionary biology
Informatics has assisted evolutionary biologists in several key ways; it has enabled researchers to trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone, more recently, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, lateral gene transfer, and the prediction of factors important in bacterial speciation,
Biodiversity of an ecosystem might be defined as the total genomic complement of a particular environment, from all of the species present, whether it is a biofilm in an abandoned mine, a drop of sea water, a scoop of soil, or the entire biosphere of the planet Earth.
Analysis of protein expression
Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data.
Protein structure prediction is another important application 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.
One of the key ideas in bioinformatics is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function.
One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.
Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services. The computational biology tool best-known among biologists is probably BLAST, an algorithm for determining the similarity of arbitrary sequences against other sequences, possibly from curated databases of protein or DNA sequences.
SOAP-based (Service Oriented Architecture Protocol) interfaces have been developed for a wide variety of bioinformatics applications allowing an application running on one computer in one part of the world to use algorithms, data and computing resources on servers in other parts of the world.