Search tips
Search criteria

Results 1-11 (11)

Clipboard (0)

Select a Filter Below

Year of Publication
Document Types
1.  Controllability Analysis of Protein Glycosylation in Cho Cells 
PLoS ONE  2014;9(2):e87973.
To function as intended in vivo, a majority of biopharmaceuticals require specific glycan distributions. 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. However, before expending the significant amount of effort and expense required to develop and implement on-line control strategies to address the problem of glycosylation heterogeneity, it is imperative to assess first the extent to which the very complex process of glycosylation is controllable, thereby establishing what is theoretically achievable prior to any experimental attempts. In this work, we present a novel methodology for assessing the output controllability of glycosylation, a prototypical example of an extremely high-dimensional and very non-linear system. We first discuss a method for obtaining the process gain matrix for glycosylation that involves performing model simulations and data analysis systematically and judiciously according to a statistical design of experiments (DOE) scheme and then employing Analysis of Variance (ANOVA) to determine the elements of process gain matrix from the resulting simulation data. We then discuss how to use the resulting high-dimensional gain matrix to assess controllability. The utility of this method is demonstrated with a practical example where we assess the controllability of various classes of glycans and of specific glycoforms that are typically found in recombinant biologics produced with Chinese Hamster Ovary (CHO) cells. In addition to providing useful insight into the extent to which on-line glycosylation control is achievable in actual manufacturing processes, the results also have important implications for genetically engineering cell lines design for enhanced glycosylation controllability.
PMCID: PMC3912168  PMID: 24498415
2.  Emergence of bimodal cell population responses from the interplay between analog single-cell signaling and protein expression noise 
BMC Systems Biology  2012;6:109.
Cell-to-cell variability in protein expression can be large, and its propagation through signaling networks affects biological outcomes. Here, we apply deterministic and probabilistic models and biochemical measurements to study how network topologies and cell-to-cell protein abundance variations interact to shape signaling responses.
We observe bimodal distributions of extracellular signal-regulated kinase (ERK) responses to epidermal growth factor (EGF) stimulation, which are generally thought to indicate bistable or ultrasensitive signaling behavior in single cells. Surprisingly, we find that a simple MAPK/ERK-cascade model with negative feedback that displays graded, analog ERK responses at a single cell level can explain the experimentally observed bimodality at the cell population level. Model analysis suggests that a conversion of graded input–output responses in single cells to digital responses at the population level is caused by a broad distribution of ERK pathway activation thresholds brought about by cell-to-cell variability in protein expression.
Our results show that bimodal signaling response distributions do not necessarily imply digital (ultrasensitive or bistable) single cell signaling, and the interplay between protein expression noise and network topologies can bring about digital population responses from analog single cell dose responses. Thus, cells can retain the benefits of robustness arising from negative feedback, while simultaneously generating population-level on/off responses that are thought to be critical for regulating cell fate decisions.
PMCID: PMC3484110  PMID: 22920937
3.  A control engineering approach to understanding the TGF-β paradox in cancer 
TGF-β, a key cytokine that regulates diverse cellular processes, including proliferation and apoptosis, appears to function paradoxically as a tumour suppressor in normal cells, and as a tumour promoter in cancer cells, but the mechanisms underlying such contradictory roles remain unknown. In particular, given that this cytokine is primarily a tumour suppressor, the conundrum of the unusually high level of TGF-β observed in the primary cancer tissue and blood samples of cancer patients with the worst prognosis, remains unresolved. To provide a quantitative explanation of these paradoxical observations, we present, from a control theory perspective, a mechanistic model of TGF-β-driven regulation of cell homeostasis. Analysis of the overall system model yields quantitative insight into how cell population is regulated, enabling us to propose a plausible explanation for the paradox: with the tumour suppressor role of TGF-β unchanged from normal to cancer cells, we demonstrate that the observed increased level of TGF-β is an effect of cancer cell phenotypic progression (specifically, acquired TGF-β resistance), not the cause. We are thus able to explain precisely why the clinically observed correlation between elevated TGF-β levels and poor prognosis is in fact consistent with TGF-β's original (and unchanged) role as a tumour suppressor.
PMCID: PMC3350743  PMID: 22188767
TGF-β; cancer; control theory; tissue homeostasis
4.  Probabilistic modeling and analysis of the effects of extra-cellular matrix density on the sizes, shapes, and locations of integrin clusters in adherent cells 
BMC Biophysics  2011;4:15.
Regulation of integrin binding to the specific complementary sites on extra-cellular matrix (ECM) proteins plays a major role in cell adhesion and migration. In addition to regulating single integrin-ligand bonds by affinity modulation, cells regulate their adhesiveness by forming integrin clusters. Although it is clear that cells exhibit different adhesion and migration behaviors on surfaces coated with different concentrations of ECM proteins, it is not clear if this response is mediated by changes in the availability of integrin binding sites or by differential intracellular signaling that may affect integrin binding and clustering.
To quantify how the concentration of ECM affects integrin clustering, we seeded cells expressing the integrin αIIbβ3 on different concentrations of the complementary ECM protein fibrinogen (Fg) and measured the resulting integrin cluster properties. We observed heterogeneity in the properties of integrin clusters, and to characterize this population heterogeneity we use a probabilistic modeling approach to quantify changes to the distributions of integrin cluster size, shape, and location.
Our results indicate that in response to increasing ECM density cells form smaller integrin clusters that are less elongated and closer to the cell periphery. These results suggest that cells can sense the availability of ECM binding sites and consequently regulate integrin clustering as a function of ECM density.
PMCID: PMC3179437  PMID: 21827670
5.  Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics 
Cell  2010;141(5):884-896.
Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell fate decisions, although signals propagate through shared pathways. Using modeling and experiments, we unravel how EGF and HRG generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands.
PMCID: PMC2888034  PMID: 20493519
6.  Robust dynamic balance of AP-1 transcription factors in a neuronal gene regulatory network 
BMC Systems Biology  2010;4:171.
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.
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.
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.
PMCID: PMC3019179  PMID: 21167049
7.  Quantifying gene network connectivity in silico: Scalability and accuracy of a modular approach 
Systems biology  2006;153(4):236-246.
Large, complex datasets 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’s 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 analyzing 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, we are able to evaluate the accuracy of the modular approach and its sensitivity to key assumptions.
PMCID: PMC2346590  PMID: 16986625
gene networks; reverse engineering; modular approach; connection coefficients
8.  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.
PMCID: PMC2132449  PMID: 18004277
breast cancer; kinetic analysis; mathematical modeling; receptor tyrosine kinases
9.  A hybrid multiscale Monte Carlo algorithm (HyMSMC) to cope with disparity in time scales and species populations in intracellular networks 
BMC Bioinformatics  2007;8:175.
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 simultaneously tackle disparity in time scales and 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.
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.
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.
PMCID: PMC1894989  PMID: 17524148
10.  Elucidating the digital control mechanism for DNA damage repair with the p53–Mdm2 system: single cell data analysis and ensemble modelling 
Recent experimental evidence about DNA damage response using the p53–Mdm2 system has raised some fundamental questions about the control mechanism employed. In response to DNA damage, an ensemble of cells shows a damped oscillation in p53 expression whose amplitude increases with increased DNA damage—consistent with ‘analogue’ control. Recent experimental results, however, show that the single cell response is a series of discrete pulses in p53; and with increase in DNA damage, neither the height nor the duration of the pulses change, but the mean number of pulses increase—consistent with ‘digital’ control. Here we present a system engineering model that uses published data to elucidate this mechanism and resolve the dilemma of how digital behaviour at the single cell level can manifest as analogue ensemble behaviour. First, we develop a dynamic model of the p53–Mdm2 system that produces non-oscillatory responses to a stress signal. Second, we develop a probability model of the distribution of pulses in a cell population, and combine the two with the simplest digital control algorithm to show how oscillatory responses whose amplitudes grow with DNA damage can arise from single cell behaviour in which each single pulse response is independent of the extent of DNA damage. A stochastic simulation of the hypothesized control mechanism reproduces experimental observations remarkably well.
PMCID: PMC1618486  PMID: 16849229
DNA damage response; p53–Mdm2 system; control systems engineering; systems biology; statistical data analysis; dynamic modelling
11.  Systems analysis of circadian time-dependent neuronal epidermal growth factor receptor signaling 
Genome Biology  2006;7(6):R48.
A systems level analysis of circadian time-dependent signaling via the epidermal growth factor receptor in the suprachiasmatic nucleus suggests several transcription factors that mediate the transcriptional response to epidermal growth factor receptor signaling.
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.
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.
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.
PMCID: PMC1779538  PMID: 16784547

Results 1-11 (11)