Reconstruction and logical modeling of glucose repression signaling pathways in Saccharomyces cerevisiae

Background  

In the yeast Saccharomyces cerevisiae, the presence of high levels of glucose leads to an array of down-regulatory effects known as glucose repression. This process is complex due to the presence of feedback loops and crosstalk between different pathways, complicating the use of intuitive approaches to analyze the system.     

Deciphering the shape and deformation of secondary structures through local conformation analysis

Background  

Protein deformation has been extensively analysed through global methods based on RMSD, torsion angles and Principal Components Analysis calculations. Here we use a local approach, able to distinguish among the different backbone conformations within loops, α-helices and β-strands, to address the question of secondary structures' shape variation within proteins and deformation at interface upon complexation.     

Computing paths and cycles in biological interaction graphs

Background  

Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.     

A Dictyostelium chalone uses G proteins to regulate proliferation

Background  

Several studies have shown that organ size, and the proliferation of tumor metastases, may be regulated by negative feedback loops in which autocrine secreted factors called chalones inhibit proliferation. However, very little is known about chalones, and how cells sense them. We previously identified two secreted proteins, AprA and CfaD, which act as chalones in Dictyostelium. Cells lacking AprA or CfaD proliferate faster than wild-type cells, and adding recombinant AprA or CfaD to cells slows their proliferation.     

Ab initio modeling of small proteins by iterative TASSER simulations

Background  

Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins.     

Prediction of calcium-binding sites by combining loop-modeling with machine learning

Background  

Protein ligand-binding sites in the apo state exhibit structural flexibility. This flexibility often frustrates methods for structure-based recognition of these sites because it leads to the absence of electron density for these critical regions, particularly when they are in surface loops. Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information.     

Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction

Background  

Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction.     

A steady state analysis indicates that negative feedback regulation of PTP1B by Akt elicits bistability in insulin-stimulated GLUT4 translocation

Background  

The phenomenon of switch-like response to graded input signal is the theme involved in various signaling pathways in living systems. Positive feedback loops or double negative feedback loops embedded with nonlinearity exhibit these switch-like bistable responses. Such feedback regulations exist in insulin signaling pathway as well.     

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Background  

The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.     

Robustness from flexibility in the fungal circadian clock

Background  

Robustness is a central property of living systems, enabling function to be maintained against environmental perturbations. A key challenge is to identify the structures in biological circuits that confer system-level properties such as robustness. Circadian clocks allow organisms to adapt to the predictable changes of the 24-hour day/night cycle by generating endogenous rhythms that can be entrained to the external cycle. In all organisms, the clock circuits typically comprise multiple interlocked feedback loops controlling the rhythmic expression of key genes. Previously, we showed that such architectures increase the flexibility of the clock's rhythmic behaviour. We now test the relationship between flexibility and robustness, using a mathematical model of the circuit controlling conidiation in the fungus Neurospora crassa.     

Molecular models for intrastrand DNA G-quadruplexes

Background  

Independent surveys of human gene promoter regions have demonstrated an overrepresentation of G₃X _n1G3X _n2G₃X _n3G₃ motifs which are known to be capable of forming intrastrand quadruple helix structures. In spite of the widely recognized importance of G-quadruplex structures in gene regulation and growing interest around this unusual DNA structure, there are at present only few such structures available in the Nucleic Acid Database. In the present work we generate by molecular modeling feasible G-quadruplex structures which may be useful for interpretation of experimental data.     

Group I introns and associated homing endonuclease genes reveals a clinal structure for <Emphasis Type="Italic">Porphyra spiralis</Emphasis> var. <Emphasis Type="Italic">amplifolia</Emphasis> (Bangiales,Rhodophyta) along the Eastern coast of South America

Background  

Group I introns are found in the nuclear small subunit ribosomal RNA gene (SSU rDNA) of some species of the genus Porphyra (Bangiales, Rhodophyta). Size polymorphisms in group I introns has been interpreted as the result of the degeneration of homing endonuclease genes (HEG) inserted in peripheral loops of intron paired elements. In this study, intron size polymorphisms were characterized for different Porphyra spiralis var. amplifolia (PSA) populations on the Southern Brazilian coast, and were used to infer genetic relationships and genetic structure of these PSA populations, in addition to cox 2-3 and rbc L-S regions. Introns of different sizes were tested qualitatively for in vitro self-splicing.     

Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli

Background  

RNA viruses are responsible for a variety of illnesses among people, including but not limited to the common cold, the flu, HIV, and ebola. Developing new drugs and new strategies for treating diseases caused by these viruses can be an expensive and time-consuming process. Mathematical modeling may be used to elucidate host-pathogen interactions and highlight potential targets for drug development, as well providing the basis for optimizing patient treatment strategies. The purpose of this work was to determine whether a genome-scale modeling approach could be used to understand how metabolism is impacted by the host-pathogen interaction during a viral infection. Escherichia coli/MS2 was used as the host-pathogen model system as MS2 is easy to work with, harmless to humans, but shares many features with eukaryotic viruses. In addition, the genome-scale metabolic model of E. coli is the most comprehensive model at this time.     

Coverage statistics for sequence census methods

Background  

We study the statistical properties of fragment coverage in genome sequencing experiments. In an extension of the classic Lander-Waterman model, we consider the effect of the length distribution of fragments. We also introduce a coding of the shape of the coverage depth function as a tree and explain how this can be used to detect regions with anomalous coverage. This modeling perspective is especially germane to current high-throughput sequencing experiments, where both sample preparation protocols and sequencing technology particulars can affect fragment length distributions.     

An empirical analysis of training protocols for probabilistic gene finders

Background  

Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters.     

Predicting functional sites with an automated algorithm suitable for heterogeneous datasets

Background  

In a previous report (La et al., Proteins, 2005), we have demonstrated that the identification of phylogenetic motifs, protein sequence fragments conserving the overall familial phylogeny, represent a promising approach for sequence/function annotation. Across a structurally and functionally heterogeneous dataset, phylogenetic motifs have been demonstrated to correspond to a wide variety of functional site archetypes, including those defined by surface loops, active site clefts, and less exposed regions. However, in our original demonstration of the technique, phylogenetic motif identification is dependent upon a manually determined similarity threshold, prohibiting large-scale application of the technique.