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1.  What do aquaporin knockout studies tell us about fluid transport in epithelia? 
The Journal of membrane biology  2013;246(4):297-305.
The investigation of near-isosmotic water transport in epithelia goes back over 100 years; however debates over mechanism and pathway still remain. Aquaporin (AQP) knockouts have been used by various research groups to test the hypothesis of an osmotic mechanism, as well as to explore the paracellular vs transcellular pathway debate. Non-proportional reductions in the water permeability of a water-transporting epithelial cell (e.g. a reduction of around 80–90%) compared to the reduction in overall water transport rate in the knockout animal (e.g. a reduction of 50–60%) are commonly found. This non-proportionality has led to controversy over whether AQP knockout studies support or contradict the osmotic mechanism. Arguments raised for and against an interpretation supporting the osmotic mechanism typically have partially-specified, implicit or incorrect assumptions. We present a simple mathematical model of the osmotic mechanism with clear assumptions and, for models based on this mechanism, establish a baseline prediction of AQP knockout studies. We allow for deviations from isotonic/isosmotic conditions and utilize dimensional analysis to reduce the number of parameters that must be considered independently. This enables a single prediction curve to be used for multiple epithelial systems. We find that a simple, transcellular-only osmotic mechanism sufficiently predicts the results of knockout studies and find criticisms of this mechanism to be overstated. We note, however, that AQP knockout studies do not give sufficient information to definitively rule out an additional paracellular pathway.
PMCID: PMC3622118  PMID: 23430220
aquaporin knockouts; epithelial transport; osmosis
2.  Gene network inference and visualization tools for biologists: application to new human transcriptome datasets 
Nucleic Acids Research  2011;40(6):2377-2398.
Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.
PMCID: PMC3315333  PMID: 22121215
3.  Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks 
PLoS ONE  2013;8(8):e72103.
We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.
PMCID: PMC3743784  PMID: 23967277
4.  Modelling the effects of calcium waves and oscillations on saliva secretion 
An understanding of Ca2+ signalling in saliva-secreting acinar cells is important, as Ca2+ is the second messenger linking stimulation of cells to production of saliva. Ca2+ signals effect secretion via the ion channels located both apically and basolaterally in the cell. By approximating Ca2+ waves with periodic functions on the apical and basolateral membranes, we isolate individual wave properties and investigate them for their effect on fluid secretion in a mathematical model of the acinar cell. Mean Ca2+ concentration is found to be the most significant property in signalling secretion. Wave speed was found to encode a range of secretion rates. Ca2+ oscillation frequency and amplitude had little effect on fluid secretion.
PMCID: PMC3361516  PMID: 22521411
mathematical model; parotid acinar cell; oscillation frequency; Ca2+ wave speed; calcium signalling
5.  Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence 
BMC Genomics  2013;14:102.
Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours.
Biclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models.
The increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.
PMCID: PMC3598775  PMID: 23405961
Biclustering; Gene expression profiles; Tumour classification; Survival prediction; Breast cancer
6.  Efficiency of primary saliva secretion: an analysis of parameter dependence in dynamic single-cell and acinus models, with application to aquaporin knockout studies 
The Journal of Membrane Biology  2012;245(1):29-50.
Secretion from the salivary glands is driven by osmosis following the establishment of osmotic gradients between the lumen, the cell and the interstitium by active ion transport. We consider a dynamic model of osmotically-driven primary saliva secretion, and use singular perturbation approaches and scaling assumptions to reduce the model. Our analysis shows that isosmotic secretion is the most efficient secretion regime, and that this holds for single isolated cells and for multiple cells assembled into an acinus. For typical parameter variations, we rule out any significant synergistic effect on total water secretion of an acinar arrangement of cells about a single shared lumen. Conditions for the attainment of isosmotic secretion are considered, and we derive an expression for how the concentration gradient between the interstitium and the lumen scales with water and chloride transport parameters. Aquaporin knockout studies are interpreted in the context of our analysis and further investigated using simulations of transport efficiency with different membrane water permeabilities. We conclude that recent claims that aquaporin knockout studies can be interpreted as evidence against a simple osmotic mechanism are not supported by our work. Many of the results that we obtain are independent of specific transporter details, and our analysis can be easily extended to apply to models that use other proposed ionic mechanisms of saliva secretion.
PMCID: PMC3364221  PMID: 22258315
7.  Cell Cycle Gene Networks Are Associated with Melanoma Prognosis 
PLoS ONE  2012;7(4):e34247.
Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways.
Methodology/Principal Findings
Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro.
This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.
PMCID: PMC3335030  PMID: 22536322
9.  A Bayesian Search for Transcriptional Motifs 
PLoS ONE  2010;5(11):e13897.
Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at:
PMCID: PMC2987817  PMID: 21124986
10.  Computational biology of cardiac myocytes: proposed standards for the physiome 
The Journal of experimental biology  2007;210(Pt 9):1576-1583.
Predicting information about human physiology and pathophysiology from genomic data is a compelling, but unfulfilled goal of post-genomic biology. This is the aim of the so-called Physiome Project and is, undeniably, an ambitious goal. Yet if we can exploit even a small proportion of the rich and varied experimental data currently available, significant insights into clinically important aspects of human physiology will follow. To achieve this requires the integration of data from disparate sources into a common framework. Extrapolation of available data across species, laboratory techniques and conditions requires a quantitative approach. Mathematical models allow us to integrate molecular information into cellular, tissue and organ-level, and ultimately clinically relevant scales. In this paper we argue that biophysically detailed computational modelling provides the essential tool for this process and, furthermore, that an appropriate framework for annotating, databasing and critiquing these models will be essential for the development of integrative computational biology.
PMCID: PMC2866297  PMID: 17449822
physiome; mathematical modelling; cardiac; multi-scale
11.  A mathematical model of fluid secretion from a parotid acinar cell 
Journal of theoretical biology  2007;248(1):64-80.
Salivary fluid secretion is crucial for preventing problems such as dryness of mouth, difficulty with mastication and swallowing, as well as oral pain and dental cavities. Fluid flow is driven primarily by the transepithelial movement of chloride and sodium ions into the parotid acinus lumen. The activation of Cl− channels is calcium dependent, with the average elevated calcium concentration during calcium oscillations increasing the conductance of the channels, leading to an outflow of Cl−. The accumulation of NaCl in the lumen drives water flow by osmosis. We construct a mathematical model of the calcium concentration oscillations and couple this to a model for Cl− efflux. We also construct a model governing fluid flow in an isolated parotid acinar cell, which includes a description of the rate of change of intracellular ion concentrations, cell volume, membrane potential and water flow rate. We find that [Ca2+] oscillations lead to oscillations in fluid flow, and that the rate of fluid flow is regulated by the average calcium concentration and not the frequency of the oscillations.
PMCID: PMC2001236  PMID: 17559884
Mathematical model; Salivary fluid secretion; Parotid acinar cells; intracellular calcium; Cl− channels and fluxes

Results 1-11 (11)