Additive functions in boolean models of gene regulatory network modules

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.     

Building functional modules from molecular interactions

The main reaction pathways in the living cell are carried out by functional modules--namely, macromolecular machines with compact structure or ensembles that change their composition and/or organization during function. Modules define themselves by spatial sequestration, chemical specificity and a characteristic time domain within which their function proceeds. On receiving a specific input, modules go through functional cycles, with phases of increasing and decreasing complexity of molecular interactions. Here, we discuss how such modules are formed and the experimental and theoretical approaches that can be used to investigate them, using examples from polynucleotide-protein interactions, vesicle transport and signal transduction to illustrate the underlying principles. Further progress in this field, where systems biology and biochemistry meet, will depend on iterative validation of the experimental and theoretical approaches.     

Loss of protein interactions and regulatory divergence in yeast whole-genome duplicates

Whole-genome duplications are important for the growth of genome complexity. We investigated various factors involved in the evolution of yeast whole-genome duplicates (ohnologs) making emphasis on the analysis of protein interactions. We found that ohnologs have a lower number of protein interactions compared with small-scale duplicates and singletons (by about − 40%). The loss of interactions was proportional to their initial number and independent of ohnolog position in the protein interaction network. A faster evolving member of an ohnolog pair has a lower number of interactions compared to its counterpart. The Gene Ontology mapping of non-overlapping and overlapping interactants of paired ohnologs reveals a sharp asymmetry in GO terms related to regulation. The fraction of these terms is much higher in non-overlapping interactants (compared to overlapping interactants and total dataset). Network clustering coefficient is lower in ohnologs, yet they show an increased density of protein interactions restricted within the whole ohnologs set. These facts suggest that subfunctionalization (or subneofunctionalization) reflected in the loss of protein interactions was a prevailing process in the divergence of ohnologs, which distinguishes them from small-scale duplicates. The loss of protein interactions was associated with the regulatory divergence between the members of an ohnolog pair. A small-scale modularity (reflected in clustering coefficient) probably was not important for ohnologs retention, yet a larger-scale modularity could be involved in their evolution.     

Nucleotide sequence of the yeast regulatory gene GAL80

The GAL80 gene in Saccharomyces cerevisiae encodes a negative regulatory protein for the set of inducible genes involving metabolism of galactose and melibiose. We have determined the nucleotide sequence of GAL80 and its flanking regions and assigned the 5' end of its mRNA to the sequence. The deduced coding sequence for GAL80 protein contains 1305 nucleotides and the calculated molecular weight of the peptide chain is 48309. The 5' end of the GAL80 mRNA maps about 67 nucleotides upstream from the translation initiating ATG. We have also determined the nucleotide sequence of uninducible alleles GAL80S-0, GAL80S-1 and GAL80S-2, and found single base substitution in each of these mutant genes which would lead to alteration of amino acid in GAL80 protein.