McEwan, Chris (2010) Representation and Decision Making in the Immune System. PhD thesis, Edinburgh Napier University.
|PDF - Published Version |
Available under License Creative Commons Attribution Non-commercial.
Download (6MB) | Preview
The immune system has long been attributed cognitive capacities such as
"recognition" of pathogenic agents; "memory" of
previous infections; "regulation" of a cavalry of detector and
effector cells; and "adaptation" to a changing environment and
evolving threats. Ostensibly, in preventing disease the immune system must be capable of discriminating states of pathology in the organism; identifying causal agents or ``pathogens''; and correctly deploying lethal effector mechanisms. What is
more, these behaviours must be learnt insomuch as the paternal genes cannot
encode the pathogenic environment of the child.
Insights into the mechanisms underlying these phenomena are of
interest, not only to immunologists, but to computer scientists pushing the
envelope of machine autonomy.
This thesis approaches these phenomena from the perspective that immunological
processes are inherently inferential processes. By considering the immune system
as a statistical decision maker, we attempt to build a bridge between
the traditionally distinct fields of biological modelling and statistical
modelling. Through a mixture of novel theoretical and empirical analysis we
assert the efficacy of competitive exclusion as a general principle
that benefits both. For the immunologist, the statistical modelling perspective
allows us to better determine that which is phenomenologically sufficient from
the mass of observational data, providing quantitative insight that may offer
relief from existing dichotomies. For the computer scientist, the biological
modelling perspective results in a theoretically transparent and empirically
effective numerical method that is able to finesse the trade-off between myopic
greediness and intractability in domains such as sparse approximation, continuous learning and boosting weak heuristics. Together, we offer this as a modern reformulation of the interface between computer science and immunology, established in the seminal work of Perelson and collaborators, over 20 years ago.
|Item Type:||Thesis (PhD)|
|Uncontrolled Keywords:||Immune system; cognitive capacities; mechanisms; inferential processes; biological modelling; statistical modelling; novel theoretical analysis; empirical analysis;|
|University Divisions/Research Centres:||Faculty of Engineering, Computing and Creative Industries > School of Computing|
|Dewey Decimal Subjects:||000 Computer science, information & general works > 000 Computer science, knowledge & systems > 003 Systems > 003.3 Computer modelling & simulation|
500 Science > 510 Mathematics > 518 Numerical analysis
|Library of Congress Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Depositing User:||Mr Chris McEwan|
|Date Deposited:||02 Feb 2011 11:37|
|Last Modified:||25 Aug 2011 10:23|
Actions (login required)