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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.
Nanocrystals, nanotubes, nanowires,
nanofibers – next generation materials.
The challenges arising from nanotechnology is largely on target.
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.
5 grand challenges for nanotechnology
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.
How should nanotechnology programs be
governed and controlled?
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. Nanomaterials 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.
To address these 5 challenges
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.
Challenges of Bioinformatics
Dr.A.Ramakrishna
Associate Professor
Dept. of Education
Osmania University, Hyderabad, A.P.
INDIA
“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.
Genome annotation
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,
Measuring biodiversity
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.
Prediction of protein structure
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
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.