A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach

A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.     

Simulating Microdosimetry in a Virtual Hepatic Lobule

The liver plays a key role in removing harmful chemicals from the body and is therefore often the first tissue to suffer potentially adverse consequences. To protect public health it is necessary to quantitatively estimate the risk of long-term low dose exposure to environmental pollutants. Animal testing is the primary tool for extrapolating human risk but it is fraught with uncertainty, necessitating novel alternative approaches. Our goal is to integrate in vitro liver experiments with agent-based cellular models to simulate a spatially extended hepatic lobule. Here we describe a graphical model of the sinusoidal network that efficiently simulates portal to centrilobular mass transfer in the hepatic lobule. We analyzed the effects of vascular topology and metabolism on the cell-level distribution following oral exposure to chemicals. The spatial distribution of metabolically inactive chemicals was similar across different vascular networks and a baseline well-mixed compartment. When chemicals were rapidly metabolized, concentration heterogeneity of the parent compound increased across the vascular network. As a result, our spatially extended lobule generated greater variability in dose-dependent cellular responses, in this case apoptosis, than were observed in the classical well-mixed liver or in a parallel tubes model. The mass-balanced graphical approach to modeling the hepatic lobule is computationally efficient for simulating long-term exposure, modular for incorporating complex cellular interactions, and flexible for dealing with evolving tissues.     

Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data

Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemical signaling network whose dynamics are governed by a large set of coupled differential equations. We found that in a simulated signaling system, the method detects interactions with significant accuracy. We then analyzed a growth factor mediated signaling network in a human mammary epithelial cell line that we inferred from mass spectrometry data and observe a biologically interpretable, small-world structure of signaling nodes, as well as a catalog of predictions regarding the interactions among previously uncharacterized phosphotyrosine sites. For example, the calculation places a recently identified tumor suppressor pathway through ARHGEF7 and Scribble, in the context of growth factor signaling. Our findings suggest that maximum entropy derived network models are an important tool for interpreting quantitative proteomics data.     

Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression

We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.     

Microcirculation in the murine liver: a computational fluid dynamic model based on 3D reconstruction from in vivo microscopy

The liver is organized in hexagonal functional units – termed lobules – characterized by a rather peculiar blood microcirculation, due to the presence of a tangled network of capillaries – termed sinusoids. A better understanding of the hemodynamics that governs liver microcirculation is relevant to clinical and biological studies aimed at improving our management of liver diseases and transplantation.Herein, we built a CFD model of a 3D sinusoidal network, based on in vivo images of a physiological mouse liver obtained with a 2-photon microscope. The CFD model was developed with Fluent 16.0 (ANSYS Inc., Canonsburg, PA), particular care was taken in imposing the correct boundary conditions representing a physiological state. To account for the remaining branches of the sinusoids, a lumped parameter model was used to prescribe the correct pressure at each outlet. The effect of an adhered cell on local hemodynamics is also investigated for different occlusion degrees.The model here proposed accurately reproduces the fluid dynamics in a portion of the sinusoidal network in mouse liver. Mean velocities and mass flow rates are in agreement with literature values from in vivo measurements. Our approach provides details on local phenomena, hardly described by other computational studies, either focused on the macroscopic hepatic vasculature or based on homogeneous porous medium model.     

SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments

We present a statistical method SAINT-MS1 for scoring protein-protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range.     

Pore‐network modeling of biofilm evolution in porous media

The influence of bacterial biomass on hydraulic properties of porous media (bioclogging) has been explored as a viable means for optimizing subsurface bioremediation and microbial enhanced oil recovery. In this study, we present a pore network simulator for modeling biofilm evolution in porous media including hydrodynamics and nutrient transport based on coupling of advection transport with Fickian diffusion and a reaction term to account for nutrient consumption. Biofilm has non‐zero permeability permitting liquid flow and transport through the biofilm itself. To handle simultaneous mass transfer in both liquid and biofilm in a pore element, a dual‐diffusion mass transfer model is introduced. The influence of nutrient limitation on predicted results is explored. Nutrient concentration in the network is affected by diffusion coefficient for nutrient transfer across biofilm (compared to water/water diffusion coefficient) under advection dominated transport, represented by mass transport Péclet number >1. The model correctly predicts a dependence of rate of biomass accumulation on inlet concentration. Poor network connectivity shows a significantly large reduction of permeability, for a small biomass pore volume. Biotechnol. Bioeng. 2011;108: 2413–2423. © 2011 Wiley Periodicals, Inc.     

Safety of simultaneous resections of colorectal cancer and liver metastases

Liver resection is the only potentially curative method for patients with colorectal cancer metastases and 5-year survival rates are 20%-40%. Simultaneous resection of colorectal cancer and synchronous liver metastases has been recommended if minor hepatectomy is indicated. The purpose of this paper is to analyze the treatment of hepatic colorectal secondaries and to assess the safety of simultaneous and delayed liver resections and relations of morbidity to the extensiveness of hepatectomy and perioperative factors. Analyzed were 21 patients with liver metastases from colorectal cancer operated between 1997 and 1999 in the Clinical Hospital "Sestre milosrdnice". Operating time for simultaneous colorectal and liver resections was not significantly longer compared to liver resections alone. No significant difference in complication rate was found after simultaneous procedures and liver resection alone (38% vs. 31%). Complication rate after major liver resections was not significantly greater than after minor resections (38% vs. 31%). No statistically significant differences were found in operation time and blood replacement between patients who developed postoperative complications and those who did not. In conclusion, simultaneous resections of primary colorectal cancer and liver metastases may be considered safe. Morbidity rates are not significantly different from those after liver resections alone, nor depend significantly upon the extensiveness of liver resection, providing that the operation time and blood loss are within the range observed in this study.     

Carbofuran- and cypermethrin-induced histopathological alterations in the liver of Labeo rohita (Hamilton) and its recovery

Alterations in the liver histology of Labeo rohita were examined after exposure to different concentrations of carbofuran (0.06 and 0.15 mg L^?1) and cypermethrin (0.16 and 0.40 μl L^?1) for 28 days. Histological recovery was also studied by maintaining the intoxicated fish in a freshwater system for an additional 28 days. Major damages caused by carbofuran toxicity were diffuse necrosis, cordal disarrangement, individualization of hepatocytes, etc.; significant changes induced by cypermethrin were hyperplasia, disintegration of hepatic mass, focal coagulative necrosis, etc. In both cases, damages were dose‐dependent, with cypermethrin exhibiting more sensitivity than carbofuran. In all cases, recovery was prominent and rate of recovery was faster with carbofuran than when using cypermethrin.     

A Cellular Automata-Based Mathematical Model for Thymocyte Development

Intrathymic T cell development is an important process necessary for the normal formation of cell-mediated immune responses. Importantly, such a process depends on interactions of developing thymocytes with cellular and extracellular elements of the thymic microenvironment. Additionally, it includes a series of oriented and tunely regulated migration events, ultimately allowing mature cells to cross endothelial barriers and leave the organ. Herein we built a cellular automata-based mathematical model for thymocyte migration and development. The rules comprised in this model take into account the main stages of thymocyte development, two-dimensional sections of the normal thymic microenvironmental network, as well as the chemokines involved in intrathymic cell migration. Parameters of our computer simulations with further adjusted to results derived from previous experimental data using sub-lethally irradiated mice, in which thymus recovery can be evaluated. The model fitted with the increasing numbers of each CD4/CD8-defined thymocyte subset. It was further validated since it fitted with the times of permanence experimentally ascertained in each CD4/CD8-defined differentiation stage. Importantly, correlations using the whole mean volume of young normal adult mice revealed that the numbers of cells generated in silico with the mathematical model fall within the range of total thymocyte numbers seen in these animals. Furthermore, simulations made with a human thymic epithelial network using the same mathematical model generated similar profiles for temporal evolution of thymocyte developmental stages. Lastly, we provided in silico evidence that the thymus architecture is important in the thymocyte development, since changes in the epithelial network result in different theoretical profiles for T cell development/migration. This model likely can be used to predict thymocyte evolution following therapeutic strategies designed for recovery of the thymus in diseases coursing with thymus involution, such as some primary immunodeficiencies, acute infections, and malnutrition.     

Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

Essentially all biological processes depend on protein–protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.     

Fine needle aspiration cytology of focal nodular hyperplasia of the liver

In five cases, the fine needle aspiration (FNA) cytologic study of ultrasonically detected solitary liver tumors yielded a diagnosis of focal nodular hyperplasia. Cytomorphologically, the lesions were characterized by the presence of both abundant normal hepatocytes and numerous epithelial cells in ductal formations, clusters or tightly packed groups in the FNA samples. In two of the five cases, the cytologic diagnosis was confirmed by subsequent histologic studies; in the remaining three cases, the clinical data were consistent with focal nodular hyperplasia of the liver. All five patients were women, four of whom had used oral contraceptives for long periods of time (5 to 15 years), which has previously been linked to the development of focal nodular hyperplasia of the liver. Based on the findings in this study, FNA cytology should be adequate for making the differential diagnosis of focal nodular hyperplasia versus liver-cell adenoma when solitary liver tumors are detected in such cases; it can yield a morphologic diagnosis and facilitate the decision as to whether surgical intervention is required.     

Analysis of the postnatal growth of the mouse liver by estimating the hepatocyte count, their weight and ploidy

Changes in the total number of hepatocytes, their distribution by the ploidy classes, as well as changes in the protein content of the cells were studied in 0.5-6 month old mice. The data obtained made it possible to estimate quantitatively the contribution of different growth components: increase in cell number, hypertrophy and polyploidization of cells, to the total increase of the liver mass. From 2 weeks to 1 month, the liver mass is increased via polyploidization (by 70%) and hypertrophy (by 30%). From 1 to 2 months, the liver mass increases due to hyperplasia (by 65%) and polyploidization (35%). After 2 months, the liver growth is practically terminated. The calculated equivalent mass of the liver, i. e. derivative of all three growth components, coincides fairly well with the factual changes in the liver mass.     

Coexpression of IL-6 and soluble IL-6R causes nodular regenerative hyperplasia and adenomas of the liver.

Studies with tumor necrosis factor p55 receptor- and interleukin-6 (IL-6)-deficient mice have shown that IL-6 is required for hepatocyte proliferation and reconstitution of the liver mass after partial hepatectomy. The biological activities of IL-6 are potentiated when this cytokine binds soluble forms of its specific receptor subunit (sIL-6R) and the resulting complex interacts with the transmembrane signaling chain gp130. We show here that double transgenic mice expressing high levels of both human IL-6 and sIL-6R under the control of liver-specific promoters spontaneously develop nodules of hepatocellular hyperplasia around periportal spaces and present signs of sustained hepatocyte proliferation. The resulting picture is identical to that of human nodular regenerative hyperplasia, a condition frequently associated with immunological and myeloproliferative disorders. In high expressors, hyperplastic lesions progress with time into discrete liver adenomas. These data strongly suggest that the IL-6/sIL-6R complex is both a primary stimulus to hepatocyte proliferation and a pathogenic factor of hepatocellular transformation.     

A method for the inference of cytokine interaction networks

Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.     

Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases

Background

Appropriate definitionof neural network architecture prior to data analysis is crucialfor successful data mining. This can be challenging when the underlyingmodel of the data is unknown. The goal of this study was to determinewhether optimizing neural network architecture using genetic programmingas a machine learning strategy would improve the ability of neural networksto model and detect nonlinear interactions among genes in studiesof common human diseases.

Results

Using simulateddata, we show that a genetic programming optimized neural network approachis able to model gene-gene interactions as well as a traditionalback propagation neural network. Furthermore, the genetic programmingoptimized neural network is better than the traditional back propagationneural network approach in terms of predictive ability and powerto detect gene-gene interactions when non-functional polymorphismsare present.

Conclusion

This study suggeststhat a machine learning strategy for optimizing neural network architecturemay be preferable to traditional trial-and-error approaches forthe identification and characterization of gene-gene interactionsin common, complex human diseases.