Biology in the 21st Century: Post Genomic Era
Science and technology have had a major impact on society, and their impact is growing.
By drastically changing our means of communication, the way we work, our housing, clothes,
and food, our methods of transportation, and, indeed, even the length and quality of life itself,
science has generated changes in the moral values and basic philosophies of mankind.
The 21st century biology requires interdisciplinary approaches across different
disciplines, such as engineering, computer science, physics, chemistry and mathematics to deal
with higher level of complex problems, especially related to health, food, energy and
environment which are becoming more dependent on other disciplines to collaborate in
providing new applicants, new methods, new techniques and new tools. Four key societal needs:
Sustainable food production, Ecosystem restoration, Optimized biofuel production, and
Improvement in human health.
Health Environment Energy Food
Biology Based solutions to the societal
problems
N
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w Deeper understanding of biological system:
B Organizing principles
i Predict/ Analyze/Modulate
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o
g Scientific integration
y
Physical & Computer Science Biology Mathematics
Chemical Sciences
Engineering
Post-Genomic Era:
The branch of science known informally as ‘omics’ are various disciplines in biology where
names end in the suffix omics, such as genomics, transcriptomics, proteomics, metagenomics,
metabolomics etc.
Genetic Terminologies:
Genome: The total genetic makeup of an organism/cell.
DNA (Deoxyribo-Nucleic acid): Master molecule found in cells of organism that
encodes genetic information.
Gene: A hereditary unit that codes for distinct traits or characteristics.
Genomics: Provides an overview of the complete set of genetic instructions provided by
the DNA.
Transcriptomics: Transcriptomics is the study of protein products and their interactions.
Metabolome: Metabolomics is the large-scale study of small molecules, commonly
known as metabolites, within cells, bio fluids, tissues or organisms.
Definition of Post-genomic era:
In genomics, the post genomics era refers to the time period from after the completion of the
human genome project to the present day.
Scope of Post genomic era:
Post-genomic era is characterized by a paradigm shift/ fundamental shift in which new
genetic research has turned on many dogmas about the way in which genes influences
phenotypes.
The Human Genome Project was a 13-year-long publicly funded project initiated in
1990, it was an international scientific research project with the goal of determining
the base pairs sequences that make up human DNA, and of identifying, mapping all of
the genes of the human genome from both a physical and a functional standpoint.
HGP provided researchers with basic information about the sequences of the three billion
chemical base pairs (i.e., adenine [A], thymine [T], guanine [G], and cytosine [C]) that
make up human genomic DNA (deoxyribonucleic acid). The HGP was further intended
to improve the technologies needed to interpret and analyze genomic sequences, to
identify all the genes encoded in human DNA, and to address the ethical, legal, and
social implications that might arise from defining the entire human genomic sequence.
Following the publication of human genome sequence draft in 2001, the post genomic era is
driven by the need to extract useful information from genetic data. There are different
approaches were used to extract useful information from genome. Such approaches were
called post genomic approaches. These approaches includes
Genomics: Analysis of genetic variations.
Transcriptomics: Gene expression.
Proteomics: Gene products (proteins).
Metabolomics: Understanding metabolism of a cell.
Each of these post genomic approaches has already contributed our understanding of specific
aspects of the disease process and development of diagnostic clinical applications.
Cardiovascular diseases, obesity, diabetes, auto immune diseases and neurodegenerative
disorders are some of the disease areas that benefited from these types of data.
Advances in the technologies and informatics used to generate and process large biological data
sets (omics data) are promoting a critical shift in the study of biomedical sciences. While
genomics, transcriptomics and proteomics coupled with bioinformatics and biostatistics are
gaining momentum.
