In addition, diversity in the FP2 sequence did not change over time and was not influenced by recent ACT use. Our analysis had limitations. First, with the short half-life of artemisinins, the primary selective pressure is likely that of the ACT partner drug, rather than the artemisinin component, so studying isolates from recently treated subjects may not be a sensitive means of identifying rare Amikacin hydrate artemisinin-resistant parasites. Second, as delayed clearance of parasites after therapy is uncommon and associated with high baseline parasitemia in Uganda, persistent parasitemia 2 days after the onset of therapy is likely not a reliable indicator of resistance. Third, since the best means of identifying rare resistant parasites is uncertain, parasites with a range of selective pressures were chosen for sequencing, limiting statistical power for some comparisons. Finally, due to the lack of sensitivity of Sanger sequencing in detecting minority alleles in mixed infections, we may have missed low abundance alleles which might play a role in artemisinin resistance. However, because of the scarcity of the artemisininresistance phenotype in Uganda, we felt that it was more useful to study parasites under potential selection or with somewhat slow clearance, rather than to survey parasites collected randomly. The absence of known molecular markers of artemisinin resistance in studied isolates is consistent with clinical findings, as ACTs remain highly efficacious in Uganda, where delayed Ganoderic-acid-F parasite clearance following treatment with ACTs has been uncommon. These results are encouraging, and suggest that artemisinin resistance is not yet established in Uganda. However, continued drug pressure, facilitated by decreasing sensitivity to ACT partner drugs, will offer strong selection for resistance, either driving the spread of resistant parasites imported from Asia, or selecting for de novo evolution of resistance in Africa. Thus, continued studies to better characterize the genetics of artemisinin resistance and continued surveillance for markers of resistance in Africa are urgent priorities. Protein phosphorylation is a fundamental part of cellular information processing, with a role in controlling numerous physiological functions, including immune defenses. Links between dysfunctional regulation of phosphorylation and disease underscore the need to elucidate underlying regulatory mechanisms. To this end, phosphorylation-dependent signaling networks have been investigated extensively, largely in studies targeting individual proteins and interactions. However, cell signaling is marked by features, such as feedback and feedforward loops, parallel pathways, and crosstalk, which may only be apparent when a network is studied as a whole. For this reason, multiplexed measurements of phosphorylation dynamics are needed, paired with reasoning aids for interpretation of these data. A useful reasoning aid is a mechanistic model, meaning a model in which information about molecular interactions is cast in a form that enables simulations consistent with physicochemical principles. Simulation of such a model reveals the logical consequences of the collected knowledge upon which the model is based. Comparisons of model simulations to experimental measurements can drive discovery through generation of hypotheses and identification of knowledge gaps. Successful integration of modeling and experimentation depends on both approaches having compatible and relevant levels of resolution.