(a) Mathematical modeling of the immunotoxin delivery process We have published the initial mathematical model of the immunotoxin delivery process (Chen et al., Annals of Biomedical Engineering, 36: 486-512, 2008). This model fairly well reproduces the dose-dependent in vitro cytotoxic activity and the reduction in volume of xenograft tumors in mice for a number of immunotoxins. More importantly, the modeling process identified some two dozen factors that are involved in the delivery process. We determined sensitivity of the tumor volume reducing capacity of different immunotoxins to each of these factors according to the model. For example, the tumor volume changed most sensitively to the changes in the normal growth rate of the tumor and the blood vessel density of the tumor tissue. The model gives a detailed space-time distribution of the immunotoxin in the tumor tissue. It also reproduces the """"""""active site barrier"""""""" effect, which indicates that strong binders are not necessarily always the most potent and that there is an optimal binding constant for the given set of diffusion rate and number of binding sites on the tumor cell surface. This model, however, is incomplete in that it does not account for all the immunotoxin that is administered, i.e. if one adds up all the immunotoxin that is lost by various different mechanisms that are incorporated in the model, the sum does not equal the total administered dose. This accounting error of the model prevents one from computing the possible source of waste with confidence. We are currently working to devise an alternate model, which correctly accounts for all the losses and still work as well as the initial model. Additionally, Ira Pastan's group found that shed antigen in the tumor tissue is probably a major factor that reduces the efficacy of the immunotoxins. Accordingly, we will incorporate shed antigen in any new model that we produce in the future. (b) Investigation of the yeast protein interaction network The Neighbor Overlap (NO) between two proteins in a protein-protein interaction network (PPIN) is defined as the number of neighbors that are common to both proteins. One expects that high NO pairs would have a similar function and therefore could serve as backup copies of one another. We could show that yeast PPIN is enriched with high NO pairs compared with random PPINs that are carefully constructed to preserve single protein properties (degree distribution and cluster coefficient) of the network intact. This is an expected result if high NO pairs indeed served as backup copies since many of the proteins in the yeast system must have backup mates. The high NO pairs also tend to share the same (high level) Gene Ontology (GO) annotation, indicating that they have similar functions, and stronger genetic interaction than the low NO pairs. Some, but by no means all, of the high NO pairs arise from the existence of protein complexes. Examination of many individual cases indicates that others appear to provide functional variation in addition to a backup function. This work is almost finished and a manuscript is in preparation.
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