Juilee Thakar

Boolean network simulations for life scientists.

Modern life sciences research increasingly relies on computational solutions, from large scale data analyses to theoretical modeling. Within the theoretical models Boolean networks occupy an increasing role as they are eminently suited at mapping biological observations and hypotheses into a mathematical formalism. The conceptual underpinnings of Boolean modeling are very accessible even without a background in quantitative sciences, yet it allows life scientists to describe and explore a wide range of surprisingly complex phenomena. In this paper we provide a clear overview of the concepts used in Boolean simulations, present a software library that can perform these simulations based on simple text inputs and give three case studies. The large scale simulations in these case studies demonstrate the Boolean paradigms and their applicability as well as the advanced features and complex use cases that our software package allows. Our software is distributed via a liberal Open Source license and is freely accessible from http://booleannet.googlecode.com

Modeling Systems-Level Regulation of Host Immune Responses

Many pathogens are able to manipulate the signaling pathways responsible for the generation of host immune responses. Here we examine and model a respiratory infection system in which disruption of host immune functions or of bacterial factors changes the dynamics of the infection. We synthesize the network of interactions between host immune components and two closely related bacteria in the genus Bordetellae. We incorporate existing experimental information on the timing of immune regulatory events into a discrete dynamic model, and verify the model by comparing the effects of simulated disruptions to the experimental outcome of knockout mutations. Our model indicates that the infection time course of both Bordetellae can be separated into three distinct phases based on the most active immune processes. We compare and discuss the effect of the species-specific virulence factors on disrupting the immune response during their infection of naive, antibody-treated, diseased, or convalescent hosts. Our model offers predictions regarding cytokine regulation, key immune components, and clearance of secondary infections; we experimentally validate two of these predictions. This type of modeling provides new insights into the virulence, pathogenesis, and host adaptation of disease-causing microorganisms and allows systems-level analysis that is not always possible using traditional methods.

Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations

Enrichment analysis of gene sets is a popular approach that provides a functional interpretation of genome-wide expression data. Existing tests are affected by inter-gene correlations, resulting in a high Type I error. The most widely used test, Gene Set Enrichment Analysis, relies on computationally intensive permutations of sample labels to generate a null distribution that preserves gene–gene correlations. A more recent approach, CAMERA, attempts to correct for these correlations by estimating a variance inflation factor directly from the data. Although these methods generate P-values for detecting gene set activity, they are unable to produce confidence intervals or allow for post hoc comparisons. We have developed a new computational framework for Quantitative Set Analysis of Gene Expression (QuSAGE). QuSAGE accounts for inter-gene correlations, improves the estimation of the variance inflation factor and, rather than evaluating the deviation from a null hypothesis with a P-value, it quantifies gene-set activity with a complete probability density function. From this probability density function, P-values and confidence intervals can be extracted and post hoc analysis can be carried out while maintaining statistical traceability. Compared with Gene Set Enrichment Analysis and CAMERA, QuSAGE exhibits better sensitivity and specificity on real data profiling the response to interferon therapy (in chronic Hepatitis C virus patients) and Influenza A virus infection. QuSAGE is available as an R package, which includes the core functions for the method as well as functions to plot and visualize the results.

Generating super-shedders: co-infection increases bacterial load and egg production of a gastrointestinal helminth

Co-infection by multiple parasites is common within individuals. Interactions between co-infecting parasites include resource competition, direct competition and immune-mediated interactions and each are likely to alter the dynamics of single parasites. We posit that co-infection is a driver of variation in parasite establishment and growth, ultimately altering the production of parasite transmission stages. To test this hypothesis, three different treatment groups of laboratory mice were infected with the gastrointestinal helminth Heligmosomoides polygyrus, the respiratory bacterial pathogen Bordetella bronchiseptica lux+ or co-infected with both parasites. To follow co-infection simultaneously, self-bioluminescent bacteria were used to quantify infection in vivo and in real-time, while helminth egg production was monitored in real-time using faecal samples. Co-infection resulted in high bacterial loads early in the infection (within the first 5 days) that could cause host mortality. Co-infection also produced helminth ‘super-shedders’; individuals that chronically shed the helminth eggs in larger than average numbers. Our study shows that co-infection may be one of the underlying mechanisms for the often-observed high variance in parasite load and shedding rates, and should thus be taken into consideration for disease management and control. Further, using self-bioluminescent bacterial reporters allowed quantification of the progression of infection within the whole animal of the same individuals at a fine temporal scale (daily) and significantly reduced the number of animals used (by 85%) compared with experiments that do not use in vivo techniques. Thus, we present bioluminescent imaging as a novel, non-invasive tool offering great potential to be taken forward into other applications of infectious disease ecology.

Constraint-based network model of pathogen-immune system interactions

Pathogenic bacteria such as Bordetella bronchiseptica modulate host immune responses to enable their establishment and persistence; however, the immune response is generally successful in clearing these bacteria. Here, we model the dynamic outcome of the interplay between host immune components and B. bronchiseptica virulence factors. The model extends our previously published interaction network of B. bronchiseptica and includes the existing experimental information on the relative timing of IL10 and IFNγ activation in the form of qualitative inequalities. The current model improves the previous one in two directions: (i) by augmenting the network with new nodes with specific function in T helper cell differentiation and effector mechanisms and (ii) by using a dynamic approach that allows us to quantify node states and mechanisms revealed to be important from our previous model. The model makes predictions about the time scales of each process, the activity thresholds of each node and novel regulatory interactions. For example, the model predicts that the activity threshold of IL4 is higher than that of IL12 and that pro-inflammatory cytokines regulate the activity of Th2 cells. Some of these predictions are supported by the literature, and many can serve as targets of future experiments.

Bee-eaters (Merops orientalis) respond to what a predator can see

Abstract. Two sets of experiments are reported that show that the small green bee-eater (Merops orientalis, a small tropical bird) can appreciate what a predator can or cannot see. Bee-eaters avoid entering the nest in the presence of a potential nest predator. In the first set of experiments bee-eaters entered the nest more frequently when the predator was unable to see the nest from its position, as compared to an approximately equidistant position from which the nest could be seen. In the second set of experiments bee-eaters entered the nest more frequently when the predator was looking away from the nest. The angle of gaze from the nest was associated significantly positively with the probability of entering the nest whereas the angle from the bird was not. Birds showed considerable flexibility as well as individual variation in the possible methods of judging the predators position and direction of gaze.

Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions

The biomolecules inside or near cells form a complex interacting system. Cellular phenotypes and behaviors arise from the totality of interactions among the components of this system. A fruitful way of modeling interacting biomolecular systems is by network‐based dynamic models that characterize each component by a state variable, and describe the change in the state variables due to the interactions in the system. Dynamic models can capture the stable state patterns of this interacting system and can connect them to different cell fates or behaviors. A Boolean or logic model characterizes each biomolecule by a binary state variable that relates the abundance of that molecule to a threshold abundance necessary for downstream processes. The regulation of this state variable is described in a parameter free manner, making Boolean modeling a practical choice for systems whose kinetic parameters have not been determined. Boolean models integrate the body of knowledge regarding the components and interactions of biomolecular systems, and capture the systems dynamic repertoire, for example the existence of multiple cell fates. These models were used for a variety of systems and led to important insights and predictions. Boolean models serve as an efficient exploratory model, a guide for follow‐up experiments, and as a foundation for more quantitative models. WIREs Syst Biol Med 2014, 6:353–369. doi: 10.1002/wsbm.1273

Computational and Experimental Analysis Reveals a Requirement for Eosinophil-Derived IL-13 for the Development of Allergic Airway Responses in C57BL/6 Mice

Eosinophils are found in the lungs of humans with allergic asthma, as well as in the lungs of animals in models of this disease. Increasing evidence suggests that these cells are integral to the development of allergic asthma in C57BL/6 mice. However, the specific function of eosinophils that is required for this event is not known. In this study, we experimentally validate a dynamic computational model and perform follow-up experimental observations to determine the mechanism of eosinophil modulation of T cell recruitment to the lung during development of allergic asthma. We find that eosinophils deficient in IL-13 were unable to rescue airway hyperresponsiveness, T cell recruitment to the lungs, and Th2 cytokine/chemokine production in ΔdblGATA eosinophil-deficient mice, even if Th2 cells were present. However, eosinophil-derived IL-13 alone was unable to rescue allergic asthma responses in the absence of competence of other IL-13–producing cells. We further computationally investigate the role of other cell types in the production of IL-13, which led to the various predictions including early and late pulses of IL-13 during airway hyperresponsiveness. These experiments suggest that eosinophils and T cells have an interdependent relationship, centered on IL-13, which regulates T cell recruitment to the lung and development of allergic asthma.

Boolean models of within-host immune interactions.

The role of various immune cells and intra-cellular components involved in immune responses has been elucidated. We describe how this information can be assembled in the form of causal interaction networks and how the dynamics of these networks can be described by qualitative/semi-qualitative modeling methods even in the absence of knowledge about kinetic constants. Recent models analyze signaling induced by the epidermal growth factor, the stimuli leading to pathological conditions, pathogen induced cellular interactions, and the intra-cellular and cellular signaling involved in the regulation of T cell responses. The models make testable predictions regarding yet undetected interactions, process durations and strengths, and novel therapeutic targets, several of which have been experimentally validated.

Network Model of Immune Responses Reveals Key Effectors to Single and Co-infection Dynamics by a Respiratory Bacterium and a Gastrointestinal Helminth

Co-infections alter the host immune response but how the systemic and local processes at the site of infection interact is still unclear. The majority of studies on co-infections concentrate on one of the infecting species, an immune function or group of cells and often focus on the initial phase of the infection. Here, we used a combination of experiments and mathematical modelling to investigate the network of immune responses against single and co-infections with the respiratory bacterium Bordetella bronchiseptica and the gastrointestinal helminth Trichostrongylus retortaeformis. Our goal was to identify representative mediators and functions that could capture the essence of the host immune response as a whole, and to assess how their relative contribution dynamically changed over time and between single and co-infected individuals. Network-based discrete dynamic models of single infections were built using current knowledge of bacterial and helminth immunology; the two single infection models were combined into a co-infection model that was then verified by our empirical findings. Simulations showed that a T helper cell mediated antibody and neutrophil response led to phagocytosis and clearance of B. bronchiseptica from the lungs. This was consistent in single and co-infection with no significant delay induced by the helminth. In contrast, T. retortaeformis intensity decreased faster when co-infected with the bacterium. Simulations suggested that the robust recruitment of neutrophils in the co-infection, added to the activation of IgG and eosinophil driven reduction of larvae, which also played an important role in single infection, contributed to this fast clearance. Perturbation analysis of the models, through the knockout of individual nodes (immune cells), identified the cells critical to parasite persistence and clearance both in single and co-infections. Our integrated approach captured the within-host immuno-dynamics of bacteria-helminth infection and identified key components that can be crucial for explaining individual variability between single and co-infections in natural populations.