Modelling and analysis of structure in cellular signalling systems

Cellular signalling is an important area of study in biology. Signalling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals. Collections of pathways form signalling networks, and interactions between pathways in a network, known as cross-talk, enables further complex signalling behaviours. Increasingly, computational modelling and analysis is required to handle the complexity of such systems. While there are several computational modelling approaches for signalling pathways, none make cross-talk explicit. We present a modular modelling framework for pathways and their cross-talk. Networks are formed by composing pathways: different cross-talks result from different synchronisations of reactions between, and overlaps of, the pathways. We formalise five types of cross-talk and give approaches to reason about possible cross-talks in a network. The complementary problem is how to handle unstructured signalling networks, i.e. networks with no explicit notion of pathways or cross-talk. We present an approach to better understand unstructured signalling networks by modelling them as a set of signal flows through the network. We introduce the Reaction Minimal Paths (RMP) algorithm that computes the set of signal flows in a model. To the best of our knowledge, current algorithms cannot guarantee both correctness and completeness of the set of signal flows in a model. The RMP algorithm is the first. Finally, the RMP algorithm suffers from the well-known state space explosion problem. We use suitable partial order reduction algorithms to improve the efficiency of this algorithm

Modular modelling of signalling pathways and their crosstalk

Signalling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals. Collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signalling behaviours. While there are several formal modelling approaches for signalling pathways, none make cross-talk explicit; the aim of this paper is to define and categorise cross-talk in a rigorous way. We define a modular approach to pathway and network modelling, based on the module construct in the PRISM modelling language, and a set of generic signalling modules. Five different types of cross-talk are defined according to various biologically meaningful combinations of variable sharing, synchronisation labels and reaction renaming. The approach is illustrated with a case-study analysis of cross-talk between the TGF-β, WNT and MAPK pathways

Modelling and analysis of biochemical signalling pathway cross-talk

Signalling pathways are abstractions that help life scientists structure the coordination of cellular activity. Cross-talk between pathways accounts for many of the complex behaviours exhibited by signalling pathways and is often critical in producing the correct signal-response relationship. Formal models of signalling pathways and cross-talk in particular can aid understanding and drive experimentation. We define an approach to modelling based on the concept that a pathway is the (synchronising) parallel composition of instances of generic modules (with internal and external labels). Pathways are then composed by (synchronising) parallel composition and renaming; different types of cross-talk result from different combinations of synchronisation and renaming. We define a number of generic modules in PRISM and five types of cross-talk: signal flow, substrate availability, receptor function, gene expression and intracellular communication. We show that Continuous Stochastic Logic properties can both detect and distinguish the types of cross-talk. The approach is illustrated with small examples and an analysis of the cross-talk between the TGF-b/BMP, WNT and MAPK pathways

Centralized Modularity of N-Linked Glycosylation Pathways in Mammalian Cells

Glycosylation is a highly complex process to produce a diverse repertoire of cellular glycans that are attached to proteins and lipids. Glycans are involved in fundamental biological processes, including protein folding and clearance, cell proliferation and apoptosis, development, immune responses, and pathogenesis. One of the major types of glycans, N-linked glycans, is formed by sequential attachments of monosaccharides to proteins by a limited number of enzymes. Many of these enzymes can accept multiple N-linked glycans as substrates, thereby generating a large number of glycan intermediates and their intermingled pathways. Motivated by the quantitative methods developed in complex network research, we investigated the large-scale organization of such N-linked glycosylation pathways in mammalian cells. The N-linked glycosylation pathways are extremely modular, and are composed of cohesive topological modules that directly branch from a common upstream pathway of glycan synthesis. This unique structural property allows the glycan production between modules to be controlled by the upstream region. Although the enzymes act on multiple glycan substrates, indicating cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis may be confined within a moderate range by transcription-level control. The findings of the present study provide experimentally-testable predictions for glycosylation processes, and may be applicable to therapeutic glycoprotein engineering

From Network Structure to Dynamics and Back Again: Relating dynamical stability and connection topology in biological complex systems

The recent discovery of universal principles underlying many complex networks occurring across a wide range of length scales in the biological world has spurred physicists in trying to understand such features using techniques from statistical physics and non-linear dynamics. In this paper, we look at a few examples of biological networks to see how similar questions can come up in very different contexts. We review some of our recent work that looks at how network structure (e.g., its connection topology) can dictate the nature of its dynamics, and conversely, how dynamical considerations constrain the network structure. We also see how networks occurring in nature can evolve to modular configurations as a result of simultaneously trying to satisfy multiple structural and dynamical constraints. The resulting optimal networks possess hubs and have heterogeneous degree distribution similar to those seen in biological systems.Comment: 15 pages, 6 figures, to appear in Proceedings of "Dynamics On and Of Complex Networks", ECSS'07 Satellite Workshop, Dresden, Oct 1-5, 200

Cross-Talk Categorisations in Data-Driven Models of Signalling Networks: A System-Level View

Data-driven models of signalling networks are becoming increasingly important in systems biology in order to reflect the dynamic patterns of signalling activities in a context-specific manner. State-of-the-art approaches for categorising and detecting signalling cross-talks may not be suitable for such models since they rely on static topologies of cell signalling networks and prior biological knowledge. In this chapter, we review state-of-the-art approaches that categorise all possible cross-talks in signalling networks and propose a novel categorisation specific to data-driven network models. Considering such models as undirected networks, we propose two categories of signalling cross-talks between any two given signalling pathways. In a Type-I cross-talk, a signalling link {gi ,gj } connects two signalling pathways, where gi and gj are signalling nodes that belong to two distinct pathways. In a Type-II cross-talk, two signalling links {gi ,gj } and {gj ,gk } meet at the intersection of two signalling pathways at a shared signalling node gj . We compared our categorisation approach with others and found that all the types of cross-talks defined by those approaches can be mapped to Type-I and Type-II cross-talks when underlying signalling activities are considered as non-causal relationships. Next, we provided a simple but intuitive algorithm called XDaMoSiN (cross-talks in data-driven models of signalling networks) to detect both Type-I and Type-II cross-talks between any two given signalling pathways in a data-driven network model. By detecting cross-talks in such network models, our approach can be used to analyse and decipher latent mechanisms of various cell phenotypes, such as cancer or acquired drug resistance, that may evolve due to the highly adaptable and dynamic nature of signal transduction networks

A predictive computational model reveals that GIV/girdin serves as a tunable valve for EGFR-stimulated cyclic AMP signals.

Cellular levels of the versatile second messenger cyclic (c)AMP are regulated by the antagonistic actions of the canonical G protein → adenylyl cyclase pathway that is initiated by G-protein-coupled receptors (GPCRs) and attenuated by phosphodiesterases (PDEs). Dysregulated cAMP signaling drives many diseases; for example, its low levels facilitate numerous sinister properties of cancer cells. Recently, an alternative paradigm for cAMP signaling has emerged in which growth factor-receptor tyrosine kinases (RTKs; e.g., EGFR) access and modulate G proteins via a cytosolic guanine-nucleotide exchange modulator (GEM), GIV/girdin; dysregulation of this pathway is frequently encountered in cancers. In this study, we present a network-based compartmental model for the paradigm of GEM-facilitated cross-talk between RTKs and G proteins and how that impacts cellular cAMP. Our model predicts that cross-talk between GIV, Gαs, and Gαi proteins dampens ligand-stimulated cAMP dynamics. This prediction was experimentally verified by measuring cAMP levels in cells under different conditions. We further predict that the direct proportionality of cAMP concentration as a function of receptor number and the inverse proportionality of cAMP concentration as a function of PDE concentration are both altered by GIV levels. Taking these results together, our model reveals that GIV acts as a tunable control valve that regulates cAMP flux after growth factor stimulation. For a given stimulus, when GIV levels are high, cAMP levels are low, and vice versa. In doing so, GIV modulates cAMP via mechanisms distinct from the two most often targeted classes of cAMP modulators, GPCRs and PDEs

Advocating the need of a systems biology approach for personalised prognosis and treatment of B-CLL patients

The clinical course of B-CLL is heterogeneous. This heterogeneity leads to a clinical dilemma: can we identify those patients who will benefit from early treatment and predict the survival? In recent years, mathematical modelling has contributed significantly in understanding the complexity of diseases. In order to build a mathematical model for determining prognosis of B-CLL one has to identify, characterise and quantify key molecules involved in the disease. Here we discuss the need and role of mathematical modelling in predicting B-CLL disease pathogenesis and suggest a new systems biology approach for a personalised therapy of B-CLL patients

An integrative computational model for intestinal tissue renewal

Objectives\ud \ud The luminal surface of the gut is lined with a monolayer of epithelial cells that acts as a nutrient absorptive engine and protective barrier. To maintain its integrity and functionality, the epithelium is renewed every few days. Theoretical models are powerful tools that can be used to test hypotheses concerning the regulation of this renewal process, to investigate how its dysfunction can lead to loss of homeostasis and neoplasia, and to identify potential therapeutic interventions. Here we propose a new multiscale model for crypt dynamics that links phenomena occurring at the subcellular, cellular and tissue levels of organisation.\ud \ud Methods\ud \ud At the subcellular level, deterministic models characterise molecular networks, such as cell-cycle control and Wnt signalling. The output of these models determines the behaviour of each epithelial cell in response to intra-, inter- and extracellular cues. The modular nature of the model enables us to easily modify individual assumptions and analyse their effects on the system as a whole.\ud \ud Results\ud \ud We perform virtual microdissection and labelling-index experiments, evaluate the impact of various model extensions, obtain new insight into clonal expansion in the crypt, and compare our predictions with recent mitochondrial DNA mutation data. \ud \ud Conclusions\ud \ud We demonstrate that relaxing the assumption that stem-cell positions are fixed enables clonal expansion and niche succession to occur. We also predict that the presence of extracellular factors near the base of the crypt alone suffices to explain the observed spatial variation in nuclear beta-catenin levels along the crypt axis