EMPLOYING QUANTITATIVE SYSTEMS PHARMACOLOGY TO CHARACTERIZE DIFFERENCES IN IGF1 AND INSULIN SIGNALING PATHWAYS IN BREAST CANCER

Insulin and insulin-like growth factor I (IGF1) have been shown to influence cancer risk and progression through poorly understood mechanisms. Here, new insights on the mechanisms of differential MAPK and Akt activation are revealed by an iterative quantitative systems pharmacology approach. In the first iteration, I combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array of 21 breast cancer cell lines treated with a time course of IGF1 and insulin, I constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. Both of the knock-down perturbations caused phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. In the second iteration, mechanistic representation of IGF1 and insulin dual signaling cascades by a set of ODEs is generated by rule-based modeling. The mechanistic network modeling provided a framework to elucidate experimental targets downstream of two receptors, which were treated as indistinguishable in previous models. The model included cascades of both mitogen-activated protein kinase (MAPK) and Akt signaling, as well as the crosstalk and feedback loops in between. The parameter perturbation scanning employed for seven different models of seven cell lines yielded new experimental hypotheses on how differential responses of MAPK and Akt originate. Complementary to the first iteration, the results in this part suggested that regulation of insulin receptor substrate 1 (IRS1) is critical in inducing differential MAPK or Akt activation. Compensation and activating feedback mechanisms collectively depressed the efficacy of anti-IGF1R/InsR therapies. With the quantitative systems pharmacologic approach, the networks of signal transduction constructed in this thesis are aimed to discern novel downstream components of the IGF1R/InsR system, and to direct patients with suitable tumor subclasses to efficient personalized clinical interventions

Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

<p>Abstract</p> <p>Background</p> <p>Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors.</p> <p>Methods</p> <p>In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4)-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS) stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology.</p> <p>Results</p> <p>Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4 signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology), but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes.</p> <p>Conclusions</p> <p>Overall, our <it>in silico </it>study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.</p

Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics.

BACKGROUND: The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. RESULTS: Our analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization. CONCLUSIONS: The insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension

<p>Abstract</p> <p>Background</p> <p>Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher <it>et al</it>. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in <it>C. elegans </it>and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC) fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze <it>in silico </it>models incorporating the use of model checking approach.</p> <p>Results</p> <p>A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules – Rule I and Rule II – to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from <it>in silico </it>and <it>in vivo </it>experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of <it>lin-15ko; lin-12ko </it>double mutants). More insights are also suggested.</p> <p>Conclusion</p> <p>The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.</p

Modeling The Spatiotemporal Dynamics Of Cells In The Lung

Multiple research problems related to the lung involve a need to take into account the spatiotemporal dynamics of the underlying component cells. Two such problems involve better understanding the nature of the allergic inflammatory response to explore what might cause chronic inflammatory diseases such as asthma, and determining the rules underlying stem cells used to engraft decellularized lung scaffolds in the hopes of growing new lungs for transplantation. For both problems, we model the systems computationally using agent-based modeling, a tool that enables us to capture these spatiotemporal dynamics by modeling any biological system as a collection of agents (cells) interacting with each other and within their environment. This allows to test the most important pieces of biological systems together rather than in isolation, and thus rapidly derive biological insights from resulting complex behavior that could not have been predicted beforehand, which we can then use to guide wet lab experimentation. For the allergic response, we hypothesized that stimulation of the allergic response with antigen results in a response with formal similarity to a muscle twitch or an action potential, with an inflammatory phase followed by a resolution phase that returns the system to baseline. We prepared an agent-based model (ABM) of the allergic inflammatory response and determined that antigen stimulation indeed results in a twitch-like response. To determine what might cause chronic inflammatory diseases where the twitch presumably cannot resolve back to baseline, we then tested multiple potential defects to the model. We observed that while most of these potential changes lessen the magnitude of the response but do not affect its overall behavior, extending the lifespan of activated pro-inflammatory cells such as neutrophils and eosinophil results in a prolonged inflammatory response that does not resolve to baseline. Finally, we performed a series of experiments involving continual antigen stimulation in mice, determining that there is evidence in the cytokine, cellular and physiologic (mechanical) response consistent with our hypothesis of a finite twitch and an associated refractory period. For stem cells, we made a 3-D ABM of a decellularized scaffold section seeded with a generic stem cell type. We then programmed in different sets of rules that could conceivably underlie the cell\u27s behavior, and observed the change in engraftment patterns in the scaffold over selected timepoints. We compared the change in those patterns against the change in experimental scaffold images seeded with C10 epithelial cells and mesenchymal stem cells, two cell types whose behaviors are not well understood, in order to determine which rulesets more closely match each cell type. Our model indicates that C10s are more likely to survive on regions of higher substrate while MSCs are more likely to proliferate on regions of higher substrate

Quantification of signaling networks

Studies in living system in the past several decades have generated qualitative understanding of the molecular interactions resulting in large networks. These networks were essentially deciphered by breaking the components of a cell through a reductionist approach. Biological networks comprising of interactions between genes, proteins and metabolites co-ordinate in the regulation of cellular processes. However, understanding the cellular function also requires quantitative information including network dynamics, which results due to an inherent design principle embedded in the network. Interactions within the network are well organized to form a definite regulatory structure, which in turn exhibits different emergent properties. The property of the network helps the cell to achieve the desired phenotypic state in a controlled manner. The dynamics of the network or the relationship between network structure and cellular behavior cannot be understood intuitively from the interaction map of the network. Computational methods can now be employed to study these networks at system level. The field of systems biology looks at integrating the interaction maps obtained through molecular biological approach. Various studies at the system level have been reported for pathways namely chemotactic response in bacteria, cell cycle and osmotic signaling in yeast, growth factor stimulated signaling pathways in mammals. This review focuses on understanding signaling networks with the help of mathematical models

Unraveling the Complex Regulatory Relationships between Metabolism and Signal Transduction in Breast Cancer.

Almost all cancer cells exhibit a metabolic phenotype characterized by high rates of glucose uptake known as the Warburg effect. Metabolic changes that are representative of distinct stages of breast cancer may suggest dependence by cancer cells on certain metabolic processes that are not relevant to normal cells. Importantly, these differences may help identify therapeutic targets that are non-lethal to normal cells. In this thesis, I present a set of models and methodologies developed using both experimental and theoretical approaches to investigate the complex intracellular signaling and metabolic networks associated with distinct stages of breast cancer. First, a detailed literature search was used to construct a logic network model of the PI3K signaling pathway, which is known to play an important regulatory role in glucose metabolism. Comparisons of experimental and simulated results suggest that the network model is well constructed but some regulatory crosstalk with MAPK requires additional refinement. Targeted therapeutic inhibitors frequently induce off-target effects. This thesis also explored the role of retroactivity in generating off-target effects in signaling networks using a computational model. The simulation results suggest that the kinetics governing covalently modified cycles in a cascade are more important for propagating an upstream off-target effect via retroactivity than the binding affinity of a drug to a targeted protein, which is a commonly optimized property in drug development. Finally, 13C tracer experiments were used to infer relative glucose and glutamine derived flux in cell lines representing distinct stages of breast cancer. Steady state metabolic flux analysis was also used to computationally fit the absolute TCA cycle flux in these cell lines. Variations in acetyl-CoA, citrate, pyruvate, and alpha-ketoglutarate flux were identified. A particularly important finding was a large reductive carboxylation flux from alpha-ketoglutarate to citrate in SUM-149 cells. Together, the models presented in this thesis provide a framework for identifying mechanistic drivers of the Warburg effect, which may represent important therapeutic targets for modulating cancer proliferation and progression.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98062/1/mlwynn_1.pd

Dynamic DNA Nanotechnology for Probing Single Nucleotide Variants and DNA Modifications

In the last decades, various DNA hybridization probes have been developed that attempt to conquer the challenge of single-nucleotide-variants (SNVs) detection. Even though a powerful toolbox including the toehold-exchange reaction, the dynamic ‘sink’ design, and the polymerase chain reaction (PCR) has been built, it still faces practical problems. For example, the natural DNA is usually in double-stranded form whereas most hybridization probes aim for single-stranded targets; the concentration of extracted DNA samples is totally unknown thus may lay outside the optimal design of probes/primers. To achieve ultra-high sensitivity and specificity, expensive and sophisticated machines such as digital droplet PCR and next-generation-sequencing may be inapplicable in rural areas. Therefore, the quantitative PCR method is still the gold standard for clinical tests. Thus motivated, my PhD career was mainly focused on the fundamental understanding of the challenges in SNVs discrimination and developing robust, versatile, and user-friendly probes/strategies. In this thesis, Chapter 1 provides a general introduction of dynamic DNA nanotechnology and its representative applications in discriminating SNVs. Chapter 2 to 4 describe three completed projects that aim to understand the thermodynamic and kinetic properties of strand displacement reactions and to circumvent the challenges of discriminating SNVs through finely tuned probes/assays

Identification of Potent EGFR Inhibitors from TCM Database@Taiwan

Overexpression of epidermal growth factor receptor (EGFR) has been associated with cancer. Targeted inhibition of the EGFR pathway has been shown to limit proliferation of cancerous cells. Hence, we employed Traditional Chinese Medicine Database (TCM Database@Taiwan) (http://tcm.cmu.edu.tw) to identify potential EGFR inhibitor. Multiple Linear Regression (MLR), Support Vector Machine (SVM), Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarities Indices Analysis (CoMSIA) models were generated using a training set of EGFR ligands of known inhibitory activities. The top four TCM candidates based on DockScore were 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid, and all had higher binding affinities than the control Iressa®. The TCM candidates had interactions with Asp855, Lys716, and Lys728, all which are residues of the protein kinase binding site. Validated MLR (r² = 0.7858) and SVM (r² = 0.8754) models predicted good bioactivity for the TCM candidates. In addition, the TCM candidates contoured well to the 3D-Quantitative Structure-Activity Relationship (3D-QSAR) map derived from the CoMFA (q² = 0.721, r² = 0.986) and CoMSIA (q² = 0.662, r² = 0.988) models. The steric field, hydrophobic field, and H-bond of the 3D-QSAR map were well matched by each TCM candidate. Molecular docking indicated that all TCM candidates formed H-bonds within the EGFR protein kinase domain. Based on the different structures, H-bonds were formed at either Asp855 or Lys716/Lys728. The compounds remained stable throughout molecular dynamics (MD) simulation. Based on the results of this study, 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid are suggested to be potential EGFR inhibitors.National Science Council of Taiwan (NSC 99-2221-E-039-013-)Committee on Chinese Medicine and Pharmacy (CCMP100-RD-030)China Medical University (CMU98-TCM)China Medical University (CMU99-TCM)China Medical University (CMU99-S-02)China Medical University (CMU99-ASIA-25)China Medical University (CMU99-ASIA-26)China Medical University (CMU99-ASIA-27)China Medical University (CMU99-ASIA-28)Asia UniversityTaiwan Department of Health. Clinical Trial and Research Center of Excellence (DOH100-TD-B-111-004)Taiwan Department of Health. Cancer Research Center of Excellence (DOH100-TD-C-111-005

Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling

Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than forty identified network components, and accords well with previous experimental results at both the pathway and whole cell physiological level. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway or of K+ efflux through slowly activating K+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited.Comment: 17 pages, 8 figure