Biology Reference
In-Depth Information
information generated by 'omics' technologies. Machine
learning techniques, such as Bayesian network analyses,
are the state of the art for reasoning over such data.
However, the underlying specification for the models being
used by these statistical methodologies is usually abstract
and rarely incorporates detailed knowledge in the field.
Hybrid models combining the rich knowledge gathered to
date with the novel high-bandwidth data are needed to
advance towards
and able to develop the computational and technological
advances needed.
Despite these challenges, we cannot emphasize enough
what a transformative time this is for immunology. As an
illustration of this, few immune-related citations in this
chapter are older than 2
3 years. We do not think this is
because of bias on our part, but rather because little of
systems immunology and systems-like approaches to
immunology existed before then. It is only now that we
have reached a time where we have the ability to peek into
this fascinating system, to discover how little we actually
understood to date, and try to understand the whole. A
direct benefit of this should be a better understanding of the
human immune system and some of its many diseases,
offering us new opportunities for drug target development
and even in vitro clinical testing for diagnostics and treat-
ment decision support
[69]
.
e
full-scale detailed immune system
simulation.
CONCLUSIONS AND FUTURE
CHALLENGES
We have described a new and upcoming direction for
systems-levels analysis, that of systems immunology. The
aim of systems immunology is to 'put the pieces together'
such that we develop a true understanding of how immunity
functions, why has it developed as it is, and what the forces
are that shape it. Likely more so than other fields, immu-
nology bridges basic and clinical research. As a derivative
of that unique position, and due to the high complexity of
the human immune system, systems immunology is also
leading the charge to enhance our understanding of
immunity and its critical role in disease to a level that
allows predictive personalized medicine.
We grouped the many research efforts in systems
immunology based on the principal approaches taken. One
is no more important than another: rather, they are all
complementary and synergistic. For brevity, we did not go
into detail on the numerous features of the immune system
that would require a combined quantitative-experimental
effort to understand. For example, the sensory organlike
REFERENCES
[1] Newell EW, Sigal N, Bendall SC, Nolan GP, Davis MM. Cytom-
etry by time-of-flight shows combinatorial cytokine expression and
virus-specific cell niches within a continuum of CD8 (
รพ
) T cell
phenotypes. Immunity 2012;36(1):142
e
52.
[2] Bendall SC, Simonds EF, Qiu P, Amir el AD, Krutzik PO, Finck R,
et al. Single-cell mass cytometry of differential immune and drug
responses across a human hematopoietic continuum. Science
2011;332(6030):687
e
96.
[3] Jiang N, Weinstein JA, Penland L, White 3rd RA, Fisher DS,
Quake SR. Determinism and stochasticity during maturation of the
zebrafish antibody repertoire. Proc Natl Acad Sci USA 2011;
108(13):5348
e
53.
[4] Weinstein JA, Jiang N, White 3rd RA, Fisher DS, Quake SR. High-
throughput sequencing of the zebrafish antibody repertoire. Science
2009;324(5928):807
e
10.
[5] Amit I, Garber M, Chevrier N, Leite AP, Donner Y, Eisenhaure T,
et al. Unbiased reconstruction of a mammalian transcriptional
network mediating pathogen responses. Science 2009;326(5950):
257
e
63.
[6] Han Q, Bagheri N, Bradshaw EM, Hafler DA, Lauffenburger DA,
Love JC. Polyfunctional responses by human T cells result from
sequential release of cytokines. Proc Natl Acad Sci USA
2012;109(5):1607
e
sensitivity and specific recognition properties of the T-cell
receptor for a particular MHC-peptide complex derived
from a pathogen
[68]
. Cell-focused, systems-focused or
multi-scale focused, it is our view that all are needed to
advance on this momentous task and that true under-
standing of immunity, the end goal of systems immunology
and likely an emergent phenomenon in its own right, will
only emerge if it becomes seamless to transition between
these different approaches.
For this to happen, system-wide measurements of
molecules must find their way back into to the cell, where
they naturally reside, and this genome-wide information
integrated into dynamic multi-scale models of cell pop-
ulations. To do so will require both a strong computational
effort as well as the development of technologies to assay
for molecules other than proteins, which, like mass-
cytometry, are highly dimensional yet work at a single cell
level and on all cells of the immune system. We believe that
this may require training a new breed of scientists (perhaps
those reading this chapter), adept in immunology in all of
its many layers and complexity, yet also highly quantitative
12.
[7] Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. Causal
protein-signaling networks derived from multiparameter single-cell
data. Science 2005;308(5721):523
e
9.
[8] Chevrier N, Mertins P, Artyomov MN, Shalek AK, Iannacone M,
Ciaccio MF, et al. Systematic discovery of TLR signaling
components delineates viral-sensing circuits. Cell 2011;147(4):
853
e
67.
[9] Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R,
Califano A. Reverse engineering of regulatory networks in human
B cells. Nat Genet 2005;37(4):382
e
90.
[10] Mani KM, Lefebvre C, Wang K, LimWK, Basso K, Dalla-Favera R,
et al. A systems biology approach to prediction of oncogenes and
molecular perturbation targets in B-cell lymphomas. Mol Syst Biol
2008;4:169.
e
