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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
A protein network refinement method based on module discovery and biological information
The identification of essential proteins can help in understanding the
minimum requirements for cell survival and development. Network-based
centrality approaches are commonly used to identify essential proteins from
protein-protein interaction networks (PINs). Unfortunately, these approaches
are limited by the poor quality of the underlying PIN data. To overcome this
problem, researchers have focused on the prediction of essential proteins by
combining PINs with other biological data. In this paper, we proposed a network
refinement method based on module discovery and biological information to
obtain a higher quality PIN. First, to extract the maximal connected subgraph
in the PIN and to divide it into different modules by using Fast-unfolding
algorithm; then, to detect critical modules based on the homology information,
subcellular localization information and topology information within each
module, and to construct a more refined network (CM-PIN). To evaluate the
effectiveness of the proposed method, we used 10 typical network-based
centrality methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR) to compare the
overall performance of the CM-PIN with those the refined dynamic protein
network (RD-PIN). The experimental results showed that the CM-PIN was optimal
in terms of precision-recall curve, jackknife curve and other criteria, and can
help to identify essential proteins more accurately
Engineering simulations for cancer systems biology
Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
Molecular Mechanisms of Secreting Vesicle Biogenesis and Secretion in Chronic Degenerative Diseases
Regulated trafficking and secretion of insulin by the β cell of the endocrine pancreas is critical to maintain our body energy homeostasis. Disruption of these processes typically leads to hyperglycemia and the complications of diabetes. Compared to methods using anti-insulin or C-peptide antibodies, the fluorescent protein labeling approaches provide many advantages in live-cell, real time format with dynamic spatial and temporal monitoring. Previous studies from our lab demonstrated that by fusing a GFP within the C peptide of mouse proinsulin (Ins-C-GFP) insulin secretory granule targeting, trafficking and exocytosis could be monitored in live cells. Confocal microscopy and western blot results showed over 85% of the Ins-C-GFP can be targeted to insulin granules, with highly efficient proteolytic processing to mature insulin and C-GFP.
Our present project aims to establish the minimum molecular determinants within human proinsulin required for its targeting to secretory granules. In order to do this, we designed a viral shuttle plasmid containing only the signal peptide, the first 5 residues of the B chain, followed by a monomeric GFP(B5), chemically synthesized with restriction sites for highly efficient and systematic chimeric and point mutagenesis. Confocal microscopy and 3-D reconstruction experiments revealed that the B5 vector was successfully expressed and nearly all of the fluorescent protein appeared within the ER(5 transfections; 72 cells), whereas the full-length hIns-C-emGFP vector efficiently targets insulin secretory granules. The results make it unlikely that the first five residues of the B chain are sufficient for human proinsulin targeting to secretory granules. The results also suggest that the middle of the C peptide is not necessary for human proinsulin targeting. We are presently characterizing a construct with the signal peptide alone without any insulin B chain residues (B0). By systematically adding back segments from hIns-C-emGFP to B5 or B0 and the following systematic point mutagenesis, we aim to establish the minimal segments and the precise residue(s) or motif(s) of human proinsulin required for targeting to secretory granules
Beyond element-wise interactions: identifying complex interactions in biological processes
Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations.
Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction.
Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem
Signalling ballet in space and time.
Although we have amassed extensive catalogues of signalling network components, our understanding of the spatiotemporal control of emergent network structures has lagged behind. Dynamic behaviour is starting to be explored throughout the genome, but analysis of spatial behaviours is still confined to individual proteins. The challenge is to reveal how cells integrate temporal and spatial information to determine specific biological functions. Key findings are the discovery of molecular signalling machines such as Ras nanoclusters, spatial activity gradients and flexible network circuitries that involve transcriptional feedback. They reveal design principles of spatiotemporal organization that are crucial for network function and cell fate decisions
A compendium of Caenorhabditis elegans regulatory transcription factors: a resource for mapping transcription regulatory networks
Background
Transcription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes.
Results
By computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks.
Conclusion
wTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function
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