The importance of a system theory based approach in understanding immunological diseases, in particular the HIV-1 infection, is being increasingly recognized. The dynamics of virus infection may be effectively represented by compact state space models in the form of nonlinear ordinary differential equations (ODEs).
Nonlinear Bayesian filtering offers various online tools for system identification of parametric ordinary differential equation models. Since parameters may change with time, it is a relevant question to assess how well time-varying parameters can be estimated from data.
In the Computational Genomics Lab we combine the study of evolutionary cell biology with translational, or medical bioinformatics. We study evolutionary cell biology, i.e. the evolutionary mechanisms underlying the origins and evolution of cellular life and the complex structures within the cell. We are also very interested in the medical, or translational applications of bioinformatics and evolutionary genomics, and are conducing collaborative projects on pathogenic bacteria, protozoa and several types of human cancers.
Complex networks are ubiquitous in real-world systems. In order to understand their design principles, the concept of network motifs emerged. These are recurrent overrepresented patterns of interconnections that can be seen as building blocks of networks. Algorithmically, discovering these motifs is a hard problem, which limits their practical applicability.
The talk will have two parts. In the first part I will give a broad overview of the area of Inductive Logic Programming (ILP) as a promising approach to Relational Data Mining. Advantages of such approach together with their main applications will then be presented. In the second part I will focus on: i) a technique for conceptual clustering in Relational Data Mining that I have been working recently; ii) the application of ILP in rational Drug Design; iii) work on using ILP for Protein Folding.
"CAMP - Computational Analysis of MicroRNAs in Plants" & "NetDyn: Understanding real large networks, from structure to dynamics"Submitted by sarasilva72 on Tue, 09/20/2011 - 14:59.
Paulo Fonseca and Alexandre Francisco are researchers at INESC-ID. They are the Principal Investigators of the two projects specified below, approved in the last FCT call. This friday they will informally talk about them.
Title: CAMP - Computational Analysis of MicroRNAs in Plants, PTDC/EIA-EIA/122534/2010
Speaker: Paulo Fonseca
Title: NetDyn: Understanding real large networks, from structure to dynamics, PTDC/EIA-CCO/118533/2010
Speaker: Alexandre Francisco
Biocides have been widely used for several decades to preserve materials including food and cosmetics, to decontaminate surfaces, to disinfect instruments, used in fabrics and, even, in toys, for personal hygiene, and to prevent transmission of infections. Nevertheless, when used in large volumes or at high concentrations, biocides have toxic effects and excessive use is dangerous for the environment, including animals and humans.
Free Software distributions, like Debian, RedHat, or Ubuntu, are some of the largest component based software systems, and they all use packages as their building blocks, together with tools for selecting, installing and removing packages on a running system.Evolving such complex software systems is a daunting task that carries significant challenges: in this talk, after providing a simple formalisation of packages and distributions, we will survey some recent results and algorithms developed to answer questions like "which is the most important package among the 27000 ones in Debian squeeze?"
The dynamic modeling of the Human Immunodeficiency Virus 1 (HIV-1) infection is still one of the great challenges in systems biology. The high prevalence of Acquired Immune Deficiency Syndrome (AIDS), known to be caused by HIV, and the fact that no cure has yet been discovered, confers relevancy to this area of study. In this paper, a dynamic model for the HIV-1 infection is analyzed. The sensitivity and identifiability issues are addressed with the purpose of optimizing the time points at which patients' blood samples should be drawn.
Generation of pluripotent stem cells starting from adult human cells using induction processes is a technology that has the potential to revolutionize regenerative medicine. However, the production of these so called iPS cells is still quite inefficient and may be dominated by stochastic effects. In this work we build mass action models of the core circuitry controlling stem cell induction and maintenance. The model includes not only the network of transcription factors NANOG, OCT4, SOX2, but also important epigenetic regulatory features of DNA methylation and histone modifications.