Fig. 6. Predicted knockdown response for varying knockdowns of PLCß-3 (panel 1 and 2) and GRK2 (panel 3 and 4). KD; KnockDown.

and V21 is the rate of reaction 21 (Fig. 4B).

We note that, (1) Jch, Ca2+ flux rate from ER to cytosol through the Ca2+ releasing channel, is based on the IP3R model of De Young and Keizer (De Young and Keizer 1992) and Li and Rinzel (Li and Rinzel, 1994) (see also, (Fink, Slepchenko, Moraru, Watras, Schaff and Loew 2000)), by implementing the calcium induced calcium release (CICR) mechanism of the Ca2+ releasing channel (h is the fraction of IP3R in which Ca2+ is not bound to the inhibitory site); (2) JSERCA, Ca2+ pumping rate by

SERCA pump, is expressed as Hill-type equation with Hill constant of 2 as observed in many cell types (Gill and Chueh 1985, Lytton, Westlin, burk, Shull and MacLennan 1992); (3) Jmitin and Jmit,out, Ca2+ uptake and release by mitochondria is from (Haberichter, Marhl and Heinrich 2001) except that for Jmitin, a Hill-coefficient of 4 is used instead of 8; (4) JPMCA and JNCX, extrusions of Ca2+ to the extra-cellular space by plasma-membrane calcium ATPase (PMCA) pump and Na+/Ca2+ exchanger (NCX) on plasma membrane are modeled as in (Wiesner, Bernk and Nerem 1996) and JPM,leak, leakage flux from extra-cellular space to cytosol, is treated as in (Hofer, Venance and Giaume 2002); (5) The expression for JpM pdep which includes SOC

4.3 Results for Stimulation of RAW 264.7 Cells with C5a

We have used 4 datasets (a control dataset, Ga,i2,3 knockdown data, the corresponding control data and PLCp-3 knockdown data) to constrain the parameters. The independent control/basic dataset is later used for prediction of dose-response of C5a and knockdown-response for several proteins. The results are shown in Fig. 5. The optimizer is able to find parameter-values that satisfy the data from all the four datasets (Fig. 5A, panels 1-4) and hence, we have confidence in the structure/mechanisms of the model. The sigmoidal shape of the dose-response curve (Fig. 5B, panel 2) is as expected. The predicted knockdown response for various proteins such as the PLCp-3 (Fig. 6, panels 1 and 2), GRK2 (Fig. 6, panels 3 and 4), receptor, Arrestin, GpY and RGS10 (not shown) is accurate. For example, upon knockdown of PLCp-3 (panels 1 and 2 in Fig. 6), rate of IP3 generation through hydrolysis of PIP2 is reduced which results in lower channel flux (Jch) and hence a lower peak is observed. Along similar lines, knockdown of GRK2 leads to higher amount of free GpY for longer time resulting in larger IP3 and hence larger peak and slower return to the basal state (panels 3 and 4 in Fig. 6). More details are presented by Maurya and Subramaniam (2007a,b).

5 Conclusions

The main objective of this review is to emphasize the role systems biology plays in the analysis of complex mammalian cell phenotypes. Diverse phenotypic questions such as how similar are cell lines in portraying in vivo phenotypes, how can one reconstruct inflammatory pathways in macrophages, and how can one quantitatively assess a cellular phenotype are addressed from a systems biology perspective. With the availability of more high and medium throughput measurements, it will be possible to carry out systemic analysis of related components including the complement and other cells associated with innate immunity. This will permit us to move beyond causal descriptions of cellular phenotypes to mechanistic and quantitative analysis of networks and systems.

6 Acknowledgements

The authors would like to acknowledge grants from that National Institutes of Health and from the National Science Foundation.


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