The evolution of intracellular compartments: from evolutionary cell biology to translational researchSubmitted by smadeira on Sat, 12/04/2010 - 00:37.
Eukaryotic cells have a complex organization into membrane-delimited organelles. Whereas we have been steadily accumulating mechanistic data on the organization and regulation of these compartments, far less is understood about their origins and evolution. I will discuss our recent work in the evolutionary analysis of three types of intracellular compartments: endosymbiotic, endomembranous, and microtube-derived.
We provide our personal vision of what could be the next generation of Web search engines, based on a single premise: people do not really want to search, they want to get tasks done. Hence, the key to a better experience will come from the combination of the deeper analysis of content with the detailed inference of user intent.
Systems Biology is an emerging field within bioscience, that uses holism,a global and integrative perspective rather than reductionism to explain the biological system's behavior. This approach is particularly useful to quantitatively characterize and predict the systems dynamic.In our application multivariate time-series of Lactococcus lactis metabolite concentrations are measured in perturbation experiments. Prior knowledge about the metabolic network topology is represented in the form of parametrized nonlinear ordinary differential equations.
In this talk, we will discuss graph cores, graph clustering and their application to a real problem. A core in a graph is usually taken as a set of highly connected vertices. Although general, this definition is intuitive and useful for studying the structure of many real networks. Nevertheless, depending on the problem, different formulations of graph core may be required, leading us to the known concept of generalized core. Thus, we study and further extend the notion of generalized core.
Some scientists use Excel as the main application for data storage and analysis. This approach leads to data dispersion and knowledge segregation in organizations, mainly because Excel files are usually stored in personal computers and data contained in these files cannot be queried. Organizations dealing with constant changes in their knowledge domain, such as the life sciences, have been adopting semantic web technologies to withstand large amounts of data and obtain the flexibility needed to support ontology changes over time with minimal impact on the existing data.
In this talk we will study algorithms for the max-plus product of Monge matrices. These algorithms use the underlying regularities of the matrices to be faster than the general multiplication algorithm, hence saving time. A non-naive solution is to iterate the SMAWK algorithm. For specific classes there are more efficient algorithms. We present a new multiplication algorithm (MMT), that is efficient for general Monge matrices and also for specific classes. The theoretical and empirical analysis shows that MMT operates in near optimal space and time.
Computational Methods for the characterization and detection of protein binding sequences through information theorySubmitted by smadeira on Wed, 06/23/2010 - 20:14.
Regulatory sequence detection is a critical facet for understanding the cell mechanisms in order to coordinate the response to stimuli. Protein synthesis involves the binding of a transcription factor to specific sequences in a process related to the gene expression initiation. A characteristic of this binding process is that the same factor binds with different sequences placed along all genome. Thus, any computational approach shows many difficulties related with this variability observed from the binding sequences.
Mathematical modeling is becoming established in the immunologist’s toolbox as a method to gain insight into the dynamics of the immune response and its components. No more so than in the case of the study of human immunodeficiency virus (HIV) infection. I will review different areas of the study of the dynamics of CD4+ T-cells in the setting of HIV, where modeling played important and diverse roles in helping us understand CD4+ T-cell homeostasis and the effect of HIV infection on T-cell dynamics, and the processes of T-cell production and destruction.
The ability of organisms to survive under a multitude of conditions is readily apparent. This robustness in performance is difficult to precisely characterize and quantify. At a biochemical level, it leads to physiological behavior when the parameters of the system remain within some neighborhood of their normal values. However, this behavior can change abruptly, often becoming pathological, as the boundary of the neighborhood is crossed. Currently, there is no generic approach to identifying and characterizing such boundaries.