Seminário 1) On the Use of Antibody Data to Assessing Malaria Transmission Intensity – Seminário 2) Information Theory Applications for Sequence Analysis and Collaborative Projects in Systems Biomedicine


  • Prof. Nuno Sepúlveda – London School of Hygiene and Tropical Medicine, UK & CEAUL / Profª Susana Vinga – IDMEC/IST- UL
  • FCUL (DEIO) – Campo Grande – Bloco C6 Piso 4 Sala 6.4.31 – 14:00h – 16:00h
  • Quarta-feira, 22 de Janeiro de 2014
  • Referência Projeto: PEst-OE/MAT/UI0006/2014
Seminário 1

Abstract: Malaria is a global health problem with more than 1 billion people estimated to be at risk worldwide. This infectious disease is caused by Plasmodium parasites transmitted to humans through bites of infected Anopheles mosquitos. Geographically, Plasmodium falciparum predominates in sub-Saharan Africa while Plasmodium vivax is the major human malaria agent in South America and Southeast Asia. In terms of public health policies, it is crucial not only to design cost-effective treatment programs, but also to sponsor initiatives aiming to prevent, or at least decrease, disease transmission intensity in the populations at risk. On the one hand, there are currently strong research efforts to develop new anti-malarial drugs, some of which already being tested in the field. On the other hand, the implementation of new malaria control/eradication programs brings the statistical challenge of finding the most accurate and reliable quantitative approaches to assessing the underlying disease transmission intensity and its putative changes throughout time. Plasmodium falciparum prevalence in children with age between 2 and 10 years old (PrPf2-10) or the entomological inoculation rate (EIR) are two popular monitoring measures in malaria epidemiology. However, these do not seem reliable measures for populations with low disease prevalence, or for regions where disease is spread seasonally. To overcome such limitations, the use of antibody-based measures has been recently proposed. Antibodies against Plasmodium parasite antigens are hallmark of malaria infection and remain stable throughout time, even when there is seasonality in disease transmission. Since malaria-specific antibodies can persist in time, they are also good indicators of past disease exposure, particularly in individuals living in areas of low malaria burden. This talk aims then to review the statistical methodology for malaria antibody data, starting off from data processing to the analysis of the so-called age-adjusted seroprevalence curve, which can provide a direct estimate of the underlying disease transmission intensity. As an example of application, we will present data from an observational study carried out in the Brazilian Amazonia region, where Plasmodium vivax infections predominate. Finally, we will discuss some of the pros and cons associated with the use of such data in malaria epidemiology.

Information theory applications for sequence analysis and collaborative projects in systems biomedicine

Seminário 2

Abstract:  This seminar will have two parts. In the first I will overview Information Theory (IT) in computational biology, focusing on alignment-free applications. These range from genome global analysis and comparison – including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis – comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or proteins features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes.

In the second part I will present projects and problems arising in systems biomedicine, more specifically in oncology and pharmacogenomics, where biostatistics can provide useful solutions. We will discuss how the integration of dynamic systems, model identification/parameter estimation, machine learning methods and genomics might contribute to assess bone metastatic patients survival probabilities (project CancerSys) and also to predict pharmacokinetic responses in HIV/HPV/HPC co-infected sub-populations (project InteleGen). This part of the seminar constitutes an open invitation for future collaborations between our research centers.