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Yi, Reiss, Shannon, Kwieciszewski, Coito, Li, Keller, Eng, Galitski, Goodlett,
Aebersold, and Katze 2004). At present these networks are incomplete, dependent on
the context of a given experiment and often lack the detail of knowledge available
within the literature, all of which should improve with time.
Determining the dynamic relationships between network components from
genomics data is a vibrant area of research but suffers from many problems, both
technical and theoretical. Problems with lack of long time courses of geneexpression
data, lack of integration of genome-scale transcription rate data, and lack of
integration of existing knowledge bases all hinder dynamic modeling. Even with
better data, advances in modeling multivariate time series data consisting of a great
excess of variables to observations (as is true from gene expression microarrays) are
needed (Kellam, Liu, Martin, Orengo, Swift, and Tucker 2001; Bar-Joseph 2004)
To date, the best approaches to modeling immune cell functions have used
theoretical models that are tested against known biologically relevant parameters or
intuitive estimates of the parameters. In immunology, such models have been
assessed at the level of T-cell proliferation following priming by APCs (Allan,
Callard, Stark, and Yates 2004). These models show control of CD4
+
and CD8
+
proliferation is mediated by different mechanisms. To prevent runaway T-cell
expansion, the rate of apoptosis must progressively increase over time. Apoptosis
mediated by cell-to-cell contacts alone is sufficient to regulate both CD4
+
and CD8
+
T-cell responses. However, if proliferation is controlled by other mechanisms such as
cytokine signaling or by APCs then CD8
+
cells must change both apoptosis and cell
division rates over time to reduce cell numbers possibly reflecting the need to rapidly
reduce CD8
+
T-cell numbers after pathogen clearance to prevent immune pathology.
Intracellular signaling has also benefited from a system-theoretic approach for
modeling TNFα-mediated NFκB signaling (Cho, Shin, Lee, and Wolkenhauer 2003).
When signaling and cell phenotype models are combined, the influence of T-cell
stimulating signals on T
H
1 and T
H
2 polarization can be modeled (Yates, Callard, and
Stark 2004). The model shows specific T
H
1 and T
H
2 polarization signals give rise to
rapid but reversible induction of the transcription factors T-bet (T
H
1) and GATA-3
(T
H
2). The model predicts that T
H
differentiation can be reversed at the single cell
level, suggesting a possible therapeutic means of manipulating T
H
1 and T
H
2
responses. Furthermore, such models would be of considerable interest in the context
of DCs interacting with more than one T-cell, where potential T-cell interactions
could be orchestrated and manipulated by the DC.
7.4.2 Systems Immunology
Despite the promise of models of immune cell function the problem remains of how
we can assemble the parts into a more integrated understanding. This moves from
modeling in isolation into a more systems biology framework. Theories of systems
biology also impact on immunology both at intracellular signaling and at the cell
phenotype and interaction level. One feature of complex biological systems is
robustness against environmental and genetic changes. Robust systems, however,
