Mapping the Genotype–Phenotype Relationship in Cellular Signaling Networks: Building Bridges Over the Unknown - Systems Biology: Networks, Models, and Applications - page 152

of proteins affect network function. However, the combinatorial possi-
bilities would be tremendous.
ELUCIDATING ENDPOINT PHENOTYPES
Endpoint phenotypes are measurable, global characteristics of cellular
signaling network function. For example, whether a cell undergoes
apoptosis as a result of a series of signaling events is an endpoint
phenotype. These global behaviors (endpoint phenotypes) are readily
measured because they involve cell-scale properties (e.g., growth, apop-
tosis, differentiation, and migration). Furthermore, recent technologies,
shown in table 5.2 and discussed above, allow for precise control of sig-
naling network components that influence systems-level properties.
The measurement of cell migration is well developed. In table 5.3,
three such techniques are listed. Each approach uses a concentration
gradient to induce cell migration. After a defined time period, the num-
bers of cells that have migrated to a particular location are counted.
These techniques measure the strength of a response to a given
chemokine or the associated conditions and can be used to evaluate the
effects of different genetic modifications or environmental conditions
on migration processes.
The additional techniques listed in table 5.3 include flow cytometry
and cell growth assays. Flow cytometry is a highly quantitative tech-
nique that counts the number of cells with a given fluorescent tag. This
fluorescent marker can be associated with cell division, or a variety of
intermediate phenotypes. Similarly, growth assays based on the optical
density of a cell culture have been implemented in high-throughput
experiments, enabling the simultaneous measurement of cell growth
under a variety of conditions.
Global measurements of cellular signaling network function are
also used to characterize cellular differentiation, including the identi-
fication of original and differentiated populations of stem cells. For
instance, hematopoietic stem cells are known to express a unique sur-
face marker, CD34, that distinguishes them from the remaining bone
marrow cells, and from any differentiated cells [74]. Gene expression
profiling or antibody screening via protein affinity chromatography
for CD34 therefore enables identification of stem or differentiated cell
populations. Similarly, identifying the derivatives of mesenchymal stem
cells is made possible via expression profiling or screening for protein
markers. Bone tissue, for example, may be screened for markers like
osteopontin or osteocalcin. An investigation of rat central nervous
system tissue illustrated the utility of global network function measure-
ments in characterizing differentiated states; gene expression profiling
enabled the identification of five basic “waves” of expression, each
corresponding to a distinct development phase [75].