Modeling and analysis of genetic regulatory networks is essential both for better understanding their dynamic behavior and for elucidating and refining open issues. We hereby present a discrete computational model that effectively describes the transient and sequential expression of a network of genes in a representative developmental pathway. Our model system is a transcriptional cascade that includes positive and negative feedback loops directing the initiation and progression through meiosis in budding yeast.

The study of biological networks has led to the development of a variety of measures for characterizing network properties at different levels. Global analysis provides summary measures such as diameter, clustering coefficients, and degree distribution that describe the network as a whole, whereas local properties, such as the occurrences of motifs and graphlets allow us to focus on specific phenomena within the network.

RNA binding proteins (RBPs) are emerging as multifunctional entities that act on the mRNA biogenesis pathway from transcription initiation through translation and decay. Association of RBPs with mRNAs through untranslated sequence elements has been proposed to constitute a mechanism that allows for the coordination of gene expression at the post-transcriptional level, defining post-transcriptional operons (Keene, 2002). We have recently characterized the mRNA interactome of two human mRNA binding proteins (Gama-Carvalho, 2006).

Suffix trees are by far the most important data structure in
stringology, with myriads of applications in fields like
bioinformatics and information retrieval. Classical representations of
suffix trees require O(n \log n) bits of space, for a string of size
n. This is considerably more than the n \log_2\sigma bits needed for
the string itself, where \sigma is the alphabet size. The size of
suffix trees has been a barrier to their wider adoption in practice.
Recent compressed suffix tree representations require just the space

Dynamic Energy Budget (DEB) theory, developed by Bas Kooijman at the Department of Theoretical Biology in the Free University of Amsterdam is the first general biological theory at the organism level since the theory of evolution. It is a mathematical theory, comprising all taxonomic groups, with extensive empirical testing and already several practical applications, namely in toxicology (where its use is recommended by ISO and OECD), environmental engineering and biological engineering.

The present talk deals with dynamical processes observed at the organismal level in conditions close to real-world environments. The relatively small amount of data and replicates available in such experiments poses specific challenges to the design, deployment and application of integrated computational tools for data management and analysis. They are exemplified by microcosm studies of phototrophic biofilms and in-vivo circadian rhythms of body temperature in mammalians.

Modularity has become in recent years a widely accepted feature of biological networks. However, it seems to mean different things in different networks and even within the same type of network. This poses a challenge to the development of methods to partition networks into functionally meaning entities. I will discuss in my talk modularity in the context of protein interaction networks, from method development to evolutionary studies.

Many algorithms have been proposed to date for the problem of finding biologically significant motifs in promoter regions. They can be classified into two large families: combinatorial methods and probabilistic methods. Probabilistic methods have been used more extensively, since they require less input from the user, and their output is easier to interpret. Combinatorial methods have the potential to identify hard to detect motifs, but their output is much harder to interpret, since it may consist of hundreds or thousands of motifs.

MicroRNAs are short (20-22) nucleotide non-coding RNAs involved in post-transcriptional regulation of gene expression. One of the essential requirements to understand the function of a microRNA is to know the genes it regulates, the so-called targets of the microRNA. It is believed that in plants most target sites present a near perfect complementarity to the sequence of the microRNA. However, in animals most target sites present lower number of complementary nucleotides. It is therefore difficult to design target prediction methods that simultaneously have high specificity and sensitivity.