One common effect of tumor promoters is increased tight junction (TJ) permeability. TJs are responsible for paracellular permeability and integrity of the barrier function. Occludin is one of the main proteins responsible for TJ structure. This study tested the effects of physiological levels of phenol, ammonia, primary bile acids (cholic acid, CA, and chenodeoxycholic acid, CDCA), and secondary bile acids (lithocholic acid, LCA, and deoxycholic acid, DCA) on paracellular permeability using a Caco-2 cell model. Paracellular permeability of Caco-2 monolayers was assessed by transepithelial electrical resistance (TER) and the apical to basolateral flux of [14C]-mannitol. Secondary, but not primary, bile acids increased permeability as reflected by significantly decreased TER and increased mannitol flux. Both phenol and ammonia also increased permeability. The primary bile acid CA significantly increased occludin expression (P < 0.05), whereas CDCA had no significant effect on occludin expression as compared to the negative control. The secondary bile acids DCA and LCA significantly increased occludin expression (P < 0.05), whereas phenol had no significant effect on the protein expression as compared to the negative control. This suggests that the increased permeability observed with LCA, DCA, phenol, and ammonia was not related to an effect on occludin expression. In conclusion, phenol, ammonia, and secondary bile acids were shown to increase paracellular permeability and reduce epithelial barrier function at doses typical of levels found in fecal samples. The results contribute to the evidence these gut microflora-generated products have tumor-promoting activity.

This article is a critical review of computational techniques used to model, analyse and simulate signalling networks. We propose a conceptual framework, and discuss the role of signalling networks in three major areas: signal transduction, cellular rhythms and cell-to-cell communication. In order to avoid an overly abstract and general discussion, we focus on three case studies in the areas of receptor signalling and kinase cascades, cell-cycle regulation and wound healing. We report on a variety of modelling techniques and associated tools, in addition to the traditional approach based on ordinary differential equations (ODEs), which provide a range of descriptive and analytical powers. As the field matures, we expect a wider uptake of these alternative approaches for several reasons, including the need to take into account low protein copy numbers and noise and the great complexity of cellular organisation. An advantage offered by many of these alternative techniques, which have their origins in computing science, is the ability to perform sophisticated model analysis which can better relate predicted behaviour and observations.

The cell division cycle is a fundamental process of cell biology and a detailed understanding of its function, regulation and other underlying mechanisms is critical to many applications in biotechnology and medicine. Since a comprehensive analysis of the molecular mechanisms involved is too complex to be performed intuitively, mathematical and computational modelling techniques are essential. This paper is a review and analysis of recent approaches attempting to model cell cycle regulation by means of protein-protein interaction networks.

We have constructed a semi-quantitative computational dynamic systems model of the activation of Src at mitosis based on protein interactions described in the literature. Through numerical simulation and bifurcation analysis we show that Src regulation involves a bistable switch, a pattern increasingly recognised as essential to biochemical signalling. The switch is operated by the tyrosine kinase CSK, which itself is involved in a negative feedback loop with Src. Negative feedback generates an excitable system, which produces transient activation of Src. One of the system parameters, which is linked to the cyclin dependent kinase cdc2, controls excitability via a second bistable switch. This topology allows for differentiated responses to a multitude of signals. The model offers explanations for the existence of the positive and negative feedback loops involving protein tyrosine phosphatase alpha (PTPalpha) and translocation of CSK and predicts a specific relationship between Src phosphorylation and activity.

Src family kinases (SFKs) interact with a number of cellular receptors. They participate in diverse signaling pathways and cellular functions. Most of the receptors involved in SFK signaling are characterized by similar modes of regulation. This computational study discusses a general kinetic model of SFK-receptor interaction. The analysis of the model reveals three major ways of SFK activation: release of inhibition by C-terminal Src kinase, weakening of the inhibitory intramolecular phosphotyrosine-SH2 interaction, and amplification of a stimulating kinase activity. The SFK model was then extended to simulate interaction with growth factor and T-cell receptors. The modular SFK signaling system was shown to adapt to the requirements of specific signaling contexts and yield qualitatively different responses in the different simulated environments. The model also provides a systematic overview of the major interactions between SFKs and various cellular signaling systems and identifies their common properties.