Dataset Information

Studying the evolution of promoter sequences: a waiting time problem.

ABSTRACT: To gain a better understanding of the evolutionary dynamics of regulatory DNA sequences, we address the following questions: (1) How long does it take until a given transcription factor (TF) binding site emerges at random in a promoter sequence? and (2) How does the composition of a TF binding site affect this waiting time? Using two different probabilistic models (an i.i.d. model and a neighbor dependent model), we can compute the expected waiting time for every k-mer, k ranging from 5 to 10, until it appears in a promoter of a species. Our findings indicate that new TF binding sites can be created on a short evolutionary time scale, i.e. in a time span below the speciation time of human and chimp. Furthermore, one can conclude that the composition of a TF binding site plays a crucial role concerning the waiting time until it appears and that the CpG methylation-deamination substitution process probably accelerates the creation of new TF binding sites. A screening of existing TF binding sites moreover reveals that k-mers predicted to have short waiting times occur more frequently than others. Supplementary Material is available at www.libertonline.com/cmb .

SUBMITTER: Behrens S

PROVIDER: S-EPMC3119604 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Studying the evolution of promoter sequences: a waiting time problem.

Behrens Sarah S Vingron Martin M

Journal of computational biology : a journal of computational molecular cell biology 20101201 12

To gain a better understanding of the evolutionary dynamics of regulatory DNA sequences, we address the following questions: (1) How long does it take until a given transcription factor (TF) binding site emerges at random in a promoter sequence? and (2) How does the composition of a TF binding site affect this waiting time? Using two different probabilistic models (an i.i.d. model and a neighbor dependent model), we can compute the expected waiting time for every k-mer, k ranging from 5 to 10, u ...[more]

PMID: 21128851

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Project description:Background and objectivesApproximately 20% of deceased donor kidneys are discarded each year in the United States. Some of these kidneys could benefit patients who are waitlisted. Understanding patient preferences regarding accepting marginal-quality kidneys could help more of the currently discarded kidneys be transplanted.Design, setting, participants, & measurementsThis study uses a discrete choice experiment that presents a deceased donor kidney to patients who are waiting for, or have received, a kidney transplant. The choices involve trade-offs between accepting a kidney today or a future kidney. The options were designed experimentally to quantify the relative importance of kidney quality (expected graft survival and level of kidney function) and waiting time. Choices were analyzed using a random-parameters logit model and latent-class analysis.ResultsIn total, 605 participants completed the discrete choice experiment. Respondents made trade-offs between kidney quality and waiting time. The average respondent would accept a kidney today, with 6.5 years of expected graft survival (95% confidence interval, 5.9 to 7.0), to avoid waiting 2 additional years for a kidney, with 11 years of expected graft survival. Three patient-preference classes were identified. Class 1 was averse to additional waiting time, but still responsive to improvements in kidney quality. Class 2 was less willing to accept increases in waiting time for improvements in kidney quality. Class 3 was willing to accept increases in waiting time even for small improvements in kidney quality. Relative to class 1, respondents in class 3 were likely to be age ≤61 years and to be waitlisted before starting dialysis, and respondents in class 2 were more likely to be older, Black, not have a college degree, and have lower Karnofsky performance status.ConclusionsParticipants preferred accepting a lower-quality kidney in return for shorter waiting time, particularly those who were older and had lower functional status.

Project description:BackgroundThe time delay between the start of an influenza pandemic and its subsequent initiation in other countries is highly relevant to preparedness planning. We quantify the distribution of this random time in terms of the separate components of this delay, and assess how the delay may be extended by non-pharmaceutical interventions.Methods and findingsThe model constructed for this time delay accounts for: (i) epidemic growth in the source region, (ii) the delay until an infected individual from the source region seeks to travel to an at-risk country, (iii) the chance that infected travelers are detected by screening at exit and entry borders, (iv) the possibility of in-flight transmission, (v) the chance that an infected arrival might not initiate an epidemic, and (vi) the delay until infection in the at-risk country gathers momentum. Efforts that reduce the disease reproduction number in the source region below two and severe travel restrictions are most effective for delaying a local epidemic, and under favourable circumstances, could add several months to the delay. On the other hand, the model predicts that border screening for symptomatic infection, wearing a protective mask during travel, promoting early presentation of cases arising among arriving passengers and moderate reduction in travel volumes increase the delay only by a matter of days or weeks. Elevated in-flight transmission reduces the delay only minimally.ConclusionsThe delay until an epidemic of pandemic strain influenza is imported into an at-risk country is largely determined by the course of the epidemic in the source region and the number of travelers attempting to enter the at-risk country, and is little affected by non-pharmaceutical interventions targeting these travelers. Short of preventing international travel altogether, eradicating a nascent pandemic in the source region appears to be the only reliable method of preventing country-to-country spread of a pandemic strain of influenza.

Dataset Information

Studying the evolution of promoter sequences: a waiting time problem.

Publications

Studying the evolution of promoter sequences: a waiting time problem.

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