Computational Approaches to Resolving the TGF-β Paradox in Cancer

Presented on March 16, 2011 from 4-5 pm in room G011 of the Molecular Science and Engineering Building.Runtime: 58:05 minutesTransforming growth factor β (TGF-β) signaling regulates a wide range of cellular and physiologic processes including proliferation, apoptosis, differentiation, migration, angiogenesis, and immune surveillance. During the early stages of epithelial tumorigenesis, TGF-β functions as a potent tumor suppressor primarily by inhibiting cell proliferation and by inducing apoptosis. However, the level of this cytokine, TGF-β, is often significantly elevated in malignant tissues and blood from cancer patients with poor prognosis. Accordingly, in the late phases of tumor progression, the role of TGF-β appears to become one of tumor promotion, apparently supporting growth, subverting the immune system, and also facilitating epithelial to mesenchymal transition (EMT), invasion and angiogenesis. This has created the widely held perception that TGF-β is simultaneously a tumor suppressor under one condition and a tumor promoter under another. But how does a single stimulus produce multiple contradictory results? This long-standing enigma of TGF-β biology remains poorly understood because the role of TGF-β on cancer is too complex for qualitative description. As a first step toward a quantitative explanation of such paradoxical roles of TGF-β in cancer, we have developed a dynamic model of the canonical TGF-β pathway via Smad transcription factors, the major intracellular mediators of the signaling cascades, based on reported experimental observations in the literature. By describing how an extracellular signal of the TGF-β ligand is sensed by receptors and transmitted into the nucleus through intracellular Smad proteins, the model yields quantitative insight into how TGF-β-induced responses can be modulated and regulated. The model also allows us to predict possible dynamic behavior of the Smad-mediated pathway in abnormal cells, and provides clues regarding possible mechanisms for explaining the seemingly contradictory roles of TGF-β during cancer progression. Based on the reported observations that TGF-β receptors are abnormally altered in a variety of human cancers, simulations of cancerous signaling using our model indicate that reduction in the levels of functional receptors may lead to altered TGF-β signaling behavior where tumor suppression characteristics are lost as a result of attenuated and nearly transient Smad retention in the nucleus. In particular, our dose-response results provide a potentially important characteristic of cancer, that is, cancer cells may require higher than normal levels of TGF-β in order to elicit nuclear Smad-mediated activity. These results have motivated the development of a macroscopic computational model of TGF-β regulation of prostate cell population from a control theory perspective to explain the paradoxical clinical observation that unusually high levels of TGF-β correlate with poor prognosis in prostate cancer. Our macroscopic model indicates that the observed elevated level of TGF-β is a consequence of acquired TGF-β resistance exhibited by the cancer cell, not the cause, because a putative TGF-β control system must secrete more TGF-β in a futile attempt to achieve the level of tumor suppression attainable with normal, responsive cells. If this hypothesis is validated, its most significant implication will be that the current approach of targeting TGF-β ligand therapeutically may have to be abandoned in favor of resensitizing the cells to the tumor suppressive effect of the TGF-β

A hybrid multiscale Monte Carlo algorithm (HyMSMC) to cope with disparity in time scales and species populations in intracellular networks

<p>Abstract</p> <p>Background</p> <p>The fundamental role that intrinsic stochasticity plays in cellular functions has been shown via numerous computational and experimental studies. In the face of such evidence, it is important that intracellular networks are simulated with stochastic algorithms that can capture molecular fluctuations. However, separation of time scales and disparity in species population, two common features of intracellular networks, make stochastic simulation of such networks computationally prohibitive. While recent work has addressed each of these challenges separately, a generic algorithm that can <it>simultaneously </it>tackle disparity in time scales <it>and </it>population scales in stochastic systems is currently lacking. In this paper, we propose the hybrid, multiscale Monte Carlo (HyMSMC) method that fills in this void.</p> <p>Results</p> <p>The proposed HyMSMC method blends stochastic singular perturbation concepts, to deal with potential stiffness, with a hybrid of exact and coarse-grained stochastic algorithms, to cope with separation in population sizes. In addition, we introduce the computational singular perturbation (CSP) method as a means of systematically partitioning fast and slow networks and computing relaxation times for convergence. We also propose a new criteria of convergence of fast networks to stochastic low-dimensional manifolds, which further accelerates the algorithm.</p> <p>Conclusion</p> <p>We use several prototype and biological examples, including a gene expression model displaying bistability, to demonstrate the efficiency, accuracy and applicability of the HyMSMC method. Bistable models serve as stringent tests for the success of multiscale MC methods and illustrate limitations of some literature methods.</p

Robust dynamic balance of AP-1 transcription factors in a neuronal gene regulatory network

<p>Abstract</p> <p>Background</p> <p>The octapeptide Angiotensin II is a key hormone that acts via its receptor AT1R in the brainstem to modulate the blood pressure control circuits and thus plays a central role in the cardiac and respiratory homeostasis. This modulation occurs via activation of a complex network of signaling proteins and transcription factors, leading to changes in levels of key genes and proteins. AT1R initiated activity in the nucleus tractus solitarius (NTS), which regulates blood pressure, has been the subject of extensive molecular analysis. But the adaptive network interactions in the NTS response to AT1R, plausibly related to the development of hypertension, are not understood.</p> <p>Results</p> <p>We developed and analyzed a mathematical model of AT1R-activated signaling kinases and a downstream gene regulatory network, with structural basis in our transcriptomic data analysis and literature. To our knowledge, our report presents the first computational model of this key regulatory network. Our simulations and analysis reveal a dynamic balance among distinct dimers of the AP-1 family of transcription factors. We investigated the robustness of this behavior to simultaneous perturbations in the network parameters using a novel multivariate approach that integrates global sensitivity analysis with decision-tree methods. Our analysis implicates a subset of Fos and Jun dependent mechanisms, with dynamic sensitivities shifting from Fos-regulating kinase (FRK)-mediated processes to those downstream of c-Jun N-terminal kinase (JNK). Decision-tree analysis indicated that while there may be a large combinatorial functional space feasible for neuronal states and parameters, the network behavior is constrained to a small set of AP-1 response profiles. Many of the paths through the combinatorial parameter space lead to a dynamic balance of AP-1 dimer forms, yielding a robust AP-1 response counteracting the biological variability.</p> <p>Conclusions</p> <p>Based on the simulation and analysis results, we demonstrate that a dynamic balance among distinct dimers of the AP-1 family of transcription factors underlies the robust activation of neuronal gene expression in the NTS response to AT1R activation. Such a differential sensitivity to limited set of mechanisms is likely to underlie the stable homeostatic physiological response.</p

Controllability analysis to identify manipulated variables for a glycosylation control strategy

N-linked glycans affect important end-use characteristics such as the bioactivity and efficacy of many therapeutic proteins, (including monoclonal antibodies), in vivo. However, achieving a precise glycan distribution during manufacturing can be challenging because glycosylation is a non-template driven cellular process, with the potential for significant uncontrolled variability in glycan distributions. As important as the glycan distribution is to the end-use performance of biopharmaceuticals, to date, no strategy exists for controlling glycosylation on-line. In this work, we present a controllability analysis for glycosylation as a first step toward establishing an online glycosylation control strategy. We first assessed the theoretically achievable extent to which the very complex process of glycosylation is controllable. Once theoretic controllability was established, we performed experiments to identify appropriate manipulated variables that can be used to direct the glycan distribution of an IgG1 to a desired state. We found that bioreactor process variables such as glucose and glutamine media concentration, temperature, pH, agitation rate, and dissolved oxygen (DO) had significant but small effects on the relative percentage of various glycans. This indicated that the IgG1 glycan distribution was generally robust to even large perturbations of typical bioreactor variables. Conversely, we found that media supplementation with manganese, galactose, and ammonia had significant and large effects on certain glycans. From this work, we determined that manganese can be used as a manipulated variable to increase the relative abundance of M5 and decrease FA2 glycans simultaneously, and galactose can be used as a manipulated variable to increase the relative abundance of FA2G1 and decrease FA2 and A2 simultaneously. As a final test, we applied machine learning algorithms to validate and enrich these findings from a data-centric point of view. The machine learning algorithms provided an avenue to discover unknown relationships and patterns that refined our findings and provided a framework to explore more variables

ADVANCED CONTROL OF GLYCOSYLATION AND TITER IN FED-BATCH MONOCLONAL ANTIBODY PRODUCTION

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Quantifying gene network connectivity in silico: Scalability and accuracy of a modular approach

Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene\u27s mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated

Systems analysis of circadian time-dependent neuronal epidermal growth factor receptor signaling

BACKGROUND: Identifying the gene regulatory networks governing physiological signal integration remains an important challenge in circadian biology. Epidermal growth factor receptor (EGFR) has been implicated in circadian function and is expressed in the suprachiasmatic nuclei (SCN), the core circadian pacemaker. The transcription networks downstream of EGFR in the SCN are unknown but, by analogy to other SCN inputs, we expect the response to EGFR activation to depend on circadian timing. RESULTS: We have undertaken a systems-level analysis of EGFR circadian time-dependent signaling in the SCN. We collected gene-expression profiles to study how the SCN response to EGFR activation depends on circadian timing. Mixed-model analysis of variance (ANOVA) was employed to identify genes with circadian time-dependent EGFR regulation. The expression data were integrated with transcription-factor binding predictions through gene group enrichment analyses to generate robust hypotheses about transcription-factors responsible for the circadian phase-dependent EGFR responses. CONCLUSION: The analysis results suggest that the transcriptional response to EGFR signaling in the SCN may be partly mediated by established transcription-factors regulated via EGFR transription-factors (AP1, Ets1, C/EBP), transcription-factors involved in circadian clock entrainment (CREB), and by core clock transcription-factors (Rorα). Quantitative real-time PCR measurements of several transcription-factor expression levels support a model in which circadian time-dependent EGFR responses are partly achieved by circadian regulation of upstream signaling components. Our study suggests an important role for EGFR signaling in SCN function and provides an example for gaining physiological insights through systems-level analysis

Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses

Deregulation of ErbB signaling plays a key role in the progression of multiple human cancers. To help understand ErbB signaling quantitatively, in this work we combine traditional experiments with computational modeling, building a model that describes how stimulation of all four ErbB receptors with epidermal growth factor (EGF) and heregulin (HRG) leads to activation of two critical downstream proteins, extracellular-signal-regulated kinase (ERK) and Akt. Model analysis and experimental validation show that (i) ErbB2 overexpression, which occurs in approximately 25% of all breast cancers, transforms transient EGF-induced signaling into sustained signaling, (ii) HRG-induced ERK activity is much more robust to the ERK cascade inhibitor U0126 than EGF-induced ERK activity, and (iii) phosphoinositol-3 kinase is a major regulator of post-peak but not pre-peak EGF-induced ERK activity. Sensitivity analysis leads to the hypothesis that ERK activation is robust to parameter perturbation at high ligand doses, while Akt activation is not