Lauren Childs

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  Modeling the impact of coinfection on persistence and infectivity of P. falciparum malaria
  Lauren Childs

  Each year nearly 200 million people are infected with the malaria parasite, Plasmodium falciparum. One of its most notable features is the variable course and duration of infection experienced by different individuals, ranging from high parasite density, acute and often severe infections to persistent, chronic infections that are often undetectable by microscopy. Field studies examining persistence of infection have used a variety of different genotyping methods, but due to limitations, it is difficult to determine the extent of mixed infections, and nearly impossible to determine if the reemergence of parasitemia is due to a new infection or recrudescence of an existing one. Mathematical models, despite limited knowledge of mechanistic details of host-parasite interactions, have qualitatively reproduced single parasite dynamics observed in patient data. Based on a discrete model of blood-stage parasite dynamics including innate and adaptive immune responses, we analyze simulated output to examine how coinfecting strains, particularly from similar clones that elicit overlapping immune responses, impact infection length and infectiousness. We find that the level of both innate and adaptive immune responses present at the time of coinfection as well as the similarity of the coinfecting strains significantly alters the duration of both the resident and coinfecting strains, particularly during chronic infections. Timing of coinfection also influences the infectivity of the coinfecting strains, likely altering transmission patterns at a population level. Duration of infection and infectivity are critical epidemiological parameters for predicting the efficacy of control strategies, particularly with the looming problem of emerging drug resistance.

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  Exploring the dynamics of CRISPRs: How much can a bacterium remember about viruses that infected it?
  Lauren Childs
  A novel bacterial defense system against invading viruses, known as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), has recently been described. Unlike other bacterial defense systems, CRISPRs, are virus-specific and heritable, producing a form of adaptive immune memory. Specific bacterial DNA regions, CRISPR loci, incorporate on average 25 copies of unique short (30 base pair) regions of viral DNA which allow the bacteria to detect, degrade and have immunity against viruses with matching sub-sequences. Ideally, the number of unique viral-copied regions a CRISPR loci contains would grow indefinitely to allow immunity to accumulate to a large number of viruses. However, the number of these viral-copied regions in the CRISPR loci of any bacteria is limited in length and number. We use a birth-death master equation model to explore the growth and decay of the length of the CRISPR loci and thus the number of viral-copied regions. Additionally, we use a simple probabilistic model to determine bounds on the length of viral-copied region within the CRISPR locus.