Challenges of Nanotechnology


1
Challenges of Nanotechnology

• Dr.A.Ramakrishna
• Associate Professor
• Dept. of Education
• Osmania University Hyderabad A.P.
• INDIA

2
Nanotechnology promises significant advances in
electronics materials biotechnology
alternative energy sources other applications.
Nanocrystals nanotubes nanowires nanofibers
next generation materials.
3
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.
4
5 grand challenges for nanotechnology
The five main challenges are to develop instrume
nts 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 val
idate methods to evaluate the toxicity of
engineered nano-materials within the next 5 to 15
years.
5
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.
6
How should nanotechnology programs be governed
and controlled The Food and Drug Administratio
n 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.
7
To address these 5 challenges Pool resources in
ternationally and with the issue of hazard
identification of exposure to and risk analysis
of engineered nano-materials. Many assumptions ab
out 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.
8
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.
9
Challenges of Bioinformatics

• Dr.A.Ramakrishna
• Associate Professor
• Dept. of Education
• Osmania University Hyderabad A.P.
• INDIA

10
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 t
erm 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.
11
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.
12
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 F-X174 was sequence
d in 1977 the DNA sequences of hundreds of
organisms have been decoded and stored in
databases.
13
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 g
enome and proteome projects--for example in the
use of DNA sequences for protein identification.
14
Genome annotation In the context of genomics a
nnotation 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
15
Measuring biodiversity Biodiversity of an ecosyst
em 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.
16
Prediction of protein structure
Protein structure prediction is another importan
t 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 As 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.
17
Software tools Software tools for bioinformatic
s 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 Architec
ture 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.