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8/7/2019 Exploring Artificial Immune System Through Immunoinformatics
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Exploring Artificial
Immune Systems
through
Immunoinformatics
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What is Immunology?
Immunology is the study of all aspects of the immunesystem including its structure, function & malfunction
during normal as well as diseased state.
What is Bioinformatics?
The application of computational techniques for thecollection of biological data, its analysis, solving
biological problems is known as Bioinformatics.
What is an algorithm?
An algorithm is a finite number of well defined
instructions for reaching a predictable solution for
any given problem.
What is Immunoinformatics?
Immunoinformatics = Immunology + Bioinformatics
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What is Immunoinformatics?
Immunoinformatics or Computational Immunology is a
breed of Immunology which encompasses the
application power of Bioinformatics and the vast
immunological data.This synergy of these two fields is very useful for
researchers in analysis of immunological data, its
conversion to computational problems, finding
solutions using computational and mathematical
approaches and finally interpretation of obtained
results into meaningful data.
The major exponents in this field are D. Dasgupta, C.
Aeckelin, Z. Ji, S. Forrest and F. Gonzalez.
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Applications of
Immunoinformatics
} Artificial Immune System (AIS)
} Immunological Databases and Data Mining
} Epitope Mapping Studies
} Predicting Peptide MHC Binding
} Computer Aided Vaccine Design (CAVD)
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Artificial Immune System} Artificial immune systems (AIS) can be defined
as computational systems inspired by
theoretical immunology, observed immune
functions, principles and mechanisms in order
to solve problems.
}
An AIS is an abstraction of one or moreimmunological processes.
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Relationship with Systems
Biology
The concept of AIS draws from Systems Biology
paradigm which is the study of complex
interactions in a number of biological systems.
The aim of Systems Biology is the modeling and
discovery of emergent properties whose
theoretical description has only been possible
thus far.
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Major AISs
}Artificial Negative Selection Algorithm
}Artificial Clonal Selection Algorithm
}Artificial Immune Network Algorithm
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Artificial Negative Selection
Algorithm
It is inspired by the self ² nonself discrimination
behavior observed in the T-cells. The
information processing principles of the self ²
nonself discrimination process via negative
selection are that of an anomaly and change
reduction systems that model the anticipation
of variation from what is known.
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http://www.cleveralgorithms.com/nature-inspired/immune.html
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Artificial Clonal SelectionAlgorithm
The general Clonal Selection Algorithm involves
the selection of antibodies based on affinity
either by matching against an antigen
pattern or via evolution of a pattern, by a
cost function. Selected antibodies are
subjected to cloning proportional to affinity
and the hypermutation of clones is inversely
proportional to the clone affinity.
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Artificial Immune Network
Algorithm
Jerne proposed an Immune Network theory where
immune cells are not at rest in the absence of
pathogen, instead antibody and immune cells
recognize and respond to each other. The classical
clonal selection and negative selection paradigms
integrate the accumulative and filtered learning of the
acquired immune system, whereas the immune network theory proposes an additional order of complexity
between the cells and molecules under selection.
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http://www.cleveralgorithms.com/nature-inspired/immune.html
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Our Ideas«} Computer simulated experiments to study the
efficacy of drugs can drastically reduce the number
of potential drugs while making them more specific.
} Creation of Artificial Neural Network based on the
AIS.
} Predicting all possible mutations in RNA viruses and
studying the relationship between them can pave
way for developing drugs against diseases like AIDS.
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References
Text References:
} Immunoinformatics ² Predicting
Immunogenicity In silico ² Darren R. Flower
} Introduction to Systems Biology ² Sangdun
Choi
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Web References:
} www.asap.cs.nott.ac.uk/publications/pdf/ux
a_05intros_ais_tutorial.pdf
} www.mitpress.mit.edu/journals/pdf/evco_13_
2_145_0.pdf
} www.cleveralgorithms.com/nature-
inspired/immune.html
} www.ncbi.nlm.nih.gov/pubmed/2477327
} www.zhouji.net/prof/CEC-03.pdf
} www.zhouji.net/prof/438reformat.pdf
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Made by:
Ravi K. Rayapati
SnehashishC
howdhary
B.Tech Bioinformatics
Semester VI