An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes

During liver regeneration, quiescent hepatocytes re-enter the cell cycle to proliferate and compensate for lost tissue. Multiple signals including hepatocyte growth factor, epidermal growth factor, tumor necrosis factor?α, interleukin-6, insulin and transforming growth factor?β orchestrate these responses and are integrated during the G(1) phase of the cell cycle. To investigate how these inputs influence DNA synthesis as a measure for proliferation, we established a large-scale integrated logical model connecting multiple signaling pathways and the cell cycle. We constructed our model based upon established literature knowledge, and successively improved and validated its structure using hepatocyte-specific literature as well as experimental DNA synthesis data. Model analyses showed that activation of the mitogen-activated protein kinase and phosphatidylinositol?3-kinase pathways was sufficient and necessary for triggering DNA synthesis. In addition, we identified key species in these pathways that mediate DNA replication. Our model predicted oncogenic mutations that were compared with the COSMIC database, and proposed intervention targets to block hepatocyte growth factor-induced DNA synthesis, which we validated experimentally. Our integrative approach demonstrates that, despite the complexity and size of the underlying interlaced network, logical modeling enables an integrative understanding of signaling-controlled proliferation at the cellular level, and thus can provide intervention strategies for distinct perturbation scenarios at various regulatory levels.     

Implementing conventional logic unconventionally: photochromic molecular populations as registers and logic gates

In this paper we detail experimental methods to implement registers, logic gates and logic circuits using populations of photochromic molecules exposed to sequences of light pulses. Photochromic molecules are molecules with two or more stable states that can be switched reversibly between states by illuminating with appropriate wavelengths of radiation. Registers are implemented by using the concentration of molecules in each state in a given sample to represent an integer value. The register's value can then be read using the intensity of a fluorescence signal from the sample. Logic gates have been implemented using a register with inputs in the form of light pulses to implement 1-input/1-output and 2-input/1-output logic gates. A proof of concept logic circuit is also demonstrated; coupled with the software workflow describe the transition from a circuit design to the corresponding sequence of light pulses.     

Oxidative DNA damage background estimated by a system model of base excision repair

Human DNA can be damaged by natural metabolism through free radical production. It has been suggested that the equilibrium between innate damage and cellular DNA repair results in an oxidative DNA damage background that potentially contributes to disease and aging. Efforts to quantitatively characterize the human oxidative DNA damage background level, based on measuring 8-oxoguanine lesions as a biomarker, have led to estimates that vary over three to four orders of magnitude, depending on the method of measurement. We applied a previously developed and validated quantitative pathway model of human DNA base excision repair, integrating experimentally determined endogenous damage rates and model parameters from multiple sources. Our estimates of at most 100 8-oxoguanine lesions per cell are consistent with the low end of data from biochemical and cell biology experiments, a result robust to model limitations and parameter variation. Our findings show the power of quantitative system modeling to interpret composite experimental data and make biologically and physiologically relevant predictions for complex human DNA repair pathway mechanisms and capacity.     

Mathematical analysis of the oncornavirus maturation process (virion RNA conversion and morphological condensation)

The rate of the maturation process of avian myeloblastosis virus experimentally estimated on the basis of genomic viral RNA conversion and morphological transition of virions was mathematically analysed. Three mathematical models were suggested and fitted to experimental data. It was found that: (a) model of simple kinetics (Model 1) does not agree with experimental data. Therefore, two hypotheses were considered in further mathematical modelling: (b) virions are identical in time of budding: maturation is dependent on the presence of a virion component which is degraded with time (Model 2). This model agrees with experimental data in all stages of the maturation process. (c) Virions are released from cells at different stages of assembly (Model 3). This model differs from experimental data especially in early stages of maturation. The hypothesis used for the construction of Model 2 seems to be the most plausible to explain the maturation process and is in agreement with data of murine leukemia virus maturation which was found to be accomplished by cleavage of p70 precursor protein.     

Microscopic Mechanism for Experimentally Observed Anomalous Elasticity of DNA in Two Dimensions

By exploring a recent model in which DNA bending elasticity, described by the wormlike chain model, is coupled to basepair denaturation, we demonstrate that small denaturation bubbles lead to anomalies in the flexibility of DNA at the nanometric scale, when confined in two dimensions (2D), as reported in atomic-force microscopy experiments. Our model yields very good fits to experimental data and quantitative predictions that can be tested experimentally. Although such anomalies exist when DNA fluctuates freely in three dimensions (3D), they are too weak to be detected. Interactions between bases in the helical double-stranded DNA are modified by electrostatic adsorption on a 2D substrate, which facilitates local denaturation. This work reconciles the apparent discrepancy between observed 2D and 3D DNA elastic properties and points out that conclusions about the 3D properties of DNA (and its companion proteins and enzymes) do not directly follow from 2D experiments by atomic-force microscopy.     

Monte carlo simulation of base and nucleotide excision repair of clustered DNA damage sites. II. Comparisons of model predictions to measured data

Clustered damage sites other than double-strand breaks (DSBs) have the potential to contribute to deleterious effects of ionizing radiation, such as cell killing and mutagenesis. In the companion article (Semenenko et al., Radiat. Res. 164, 180-193, 2005), a general Monte Carlo framework to simulate key steps in the base and nucleotide excision repair of DNA damage other than DSBs is proposed. In this article, model predictions are compared to measured data for selected low-and high-LET radiations. The Monte Carlo model reproduces experimental observations for the formation of enzymatic DSBs in Escherichia coli and cells of two Chinese hamster cell lines (V79 and xrs5). Comparisons of model predictions with experimental values for low-LET radiation suggest that an inhibition of DNA backbone incision at the sites of base damage by opposing strand breaks is active over longer distances between the damaged base and the strand break in hamster cells (8 bp) compared to E. coli (3 bp). Model estimates for the induction of point mutations in the human hypoxanthine guanine phosphoribosyl transferase (HPRT) gene by ionizing radiation are of the same order of magnitude as the measured mutation frequencies. Trends in the mutation frequency for low- and high-LET radiation are predicted correctly by the model. The agreement between selected experimental data sets and simulation results provides some confidence in postulated mechanisms for excision repair of DNA damage other than DSBs and suggests that the proposed Monte Carlo scheme is useful for predicting repair outcomes.     

Mixture models with multiple levels, with application to the analysis of multifactor gene expression data

Model-based clustering is a popular tool for summarizing high-dimensional data. With the number of high-throughput large-scale gene expression studies still on the rise, the need for effective data- summarizing tools has never been greater. By grouping genes according to a common experimental expression profile, we may gain new insight into the biological pathways that steer biological processes of interest. Clustering of gene profiles can also assist in assigning functions to genes that have not yet been functionally annotated. In this paper, we propose 2 model selection procedures for model-based clustering. Model selection in model-based clustering has to date focused on the identification of data dimensions that are relevant for clustering. However, in more complex data structures, with multiple experimental factors, such an approach does not provide easily interpreted clustering outcomes. We propose a mixture model with multiple levels, , that provides sparse representations both "within" and "between" cluster profiles. We explore various flexible "within-cluster" parameterizations and discuss how efficient parameterizations can greatly enhance the objective interpretability of the generated clusters. Moreover, we allow for a sparse "between-cluster" representation with a different number of clusters at different levels of an experimental factor of interest. This enhances interpretability of clusters generated in multiple-factor contexts. Interpretable cluster profiles can assist in detecting biologically relevant groups of genes that may be missed with less efficient parameterizations. We use our multilevel mixture model to mine a proliferating cell line expression data set for annotational context and regulatory motifs. We also investigate the performance of the multilevel clustering approach on several simulated data sets.     

Sources of Variability in a Synthetic Gene Oscillator

Synthetic gene oscillators are small, engineered genetic circuits that produce periodic variations in target protein expression. Like other gene circuits, synthetic gene oscillators are noisy and exhibit fluctuations in amplitude and period. Understanding the origins of such variability is key to building predictive models that can guide the rational design of synthetic circuits. Here, we developed a method for determining the impact of different sources of noise in genetic oscillators by measuring the variability in oscillation amplitude and correlations between sister cells. We first used a combination of microfluidic devices and time-lapse fluorescence microscopy to track oscillations in cell lineages across many generations. We found that oscillation amplitude exhibited high cell-to-cell variability, while sister cells remained strongly correlated for many minutes after cell division. To understand how such variability arises, we constructed a computational model that identified the impact of various noise sources across the lineage of an initial cell. When each source of noise was appropriately tuned the model reproduced the experimentally observed amplitude variability and correlations, and accurately predicted outcomes under novel experimental conditions. Our combination of computational modeling and time-lapse data analysis provides a general way to examine the sources of variability in dynamic gene circuits.     

Regulation of Macronuclear DNA Content in Tetrahymena thermophila*†

Synopsis.
Unequal macronuclear division in Tetrahymena thermophila introduces variance into G1 macronuclei; unless eliminated such variance would result in continuous variation in DNA content. Analysis of G1 and G2 macronuclear variances reveals that the added variance is eliminated by action on the extremes of macronuclear DNA content. In this model (Model II), macronuclei with small amounts of DNA have an additional complete S phase, while those with large amounts of DNA skip S. From available data, chromatin extrusion is shown not to contribute significantly, if at all, to the elimination of variance. Computer simulations utilizing haploid subunits indicate that model II predictions apply reasonably well to experimental data in terms of coefficients of variation, mean DNA content, and frequency of additional and skipped S phases. The simulations reveal also that within certain constraints, particularly the thresholds for additional and skipped S phases, macronuclear assortment is unaffected by Model II regulation. The relationships between Model II and other aspects of the cell cycle are briefly discussed.     

Perturbation analysis analyzed—mathematical modeling of intact and perturbed gene regulatory circuits for animal development

Gene regulatory networks for animal development are the underlying mechanisms controlling cell fate specification and differentiation. The architecture of gene regulatory circuits determines their information processing properties and their developmental function. It is a major task to derive realistic network models from exceedingly advanced high throughput experimental data. Here we use mathematical modeling to study the dynamics of gene regulatory circuits to advance the ability to infer regulatory connections and logic function from experimental data. This study is guided by experimental methodologies that are commonly used to study gene regulatory networks that control cell fate specification. We study the effect of a perturbation of an input on the level of its downstream genes and compare between the cis-regulatory execution of OR and AND logics. Circuits that initiate gene activation and circuits that lock on the expression of genes are analyzed. The model improves our ability to analyze experimental data and construct from it the network topology. The model also illuminates information processing properties of gene regulatory circuits for animal development.     

Cyclin and DNA Distributed Cell Cycle Model for GS-NS0 Cells

Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins—key molecules that regulate cell cycle transition—have been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model’s use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.     

An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228

Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism.     

Magnesium-cationic dummy atom molecules enhance representation of DNA polymerase beta in molecular dynamics simulations: improved accuracy in studies of structural features and mutational effects

Human DNA polymerase beta (pol beta) fills gaps in DNA as part of base excision DNA repair. Due to its small size it is a convenient model enzyme for other DNA polymerases. Its active site contains two Mg(2+) ions, of which one binds an incoming dNTP and one catalyzes its condensation with the DNA primer strand. Simulating such binuclear metalloenzymes accurately but computationally efficiently is a challenging task. Here, we present a magnesium-cationic dummy atom approach that can easily be implemented in molecular mechanical force fields such as the ENZYMIX or the AMBER force fields. All properties investigated here, namely, structure and energetics of both Michaelis complexes and transition state (TS) complexes were represented more accurately using the magnesium-cationic dummy atom model than using the traditional one-atom representation for Mg(2+) ions. The improved agreement between calculated free energies of binding of TS models to different pol beta variants and the experimentally determined activation free energies indicates that this model will be useful in studying mutational effects on catalytic efficiency and fidelity of DNA polymerases. The model should also have broad applicability to the modeling of other magnesium-containing proteins.     

Mathematical model of viral kinetics in vitro estimates the number of E2-CD81 complexes necessary for hepatitis C virus entry

Interaction between the hepatitis C virus (HCV) envelope protein E2 and the host receptor CD81 is essential for HCV entry into target cells. The number of E2-CD81 complexes necessary for HCV entry has remained difficult to estimate experimentally. Using the recently developed cell culture systems that allow persistent HCV infection in vitro, the dependence of HCV entry and kinetics on CD81 expression has been measured. We reasoned that analysis of the latter experiments using a mathematical model of viral kinetics may yield estimates of the number of E2-CD81 complexes necessary for HCV entry. Here, we constructed a mathematical model of HCV viral kinetics in vitro, in which we accounted explicitly for the dependence of HCV entry on CD81 expression. Model predictions of viral kinetics are in quantitative agreement with experimental observations. Specifically, our model predicts triphasic viral kinetics in vitro, where the first phase is characterized by cell proliferation, the second by the infection of susceptible cells and the third by the growth of cells refractory to infection. By fitting model predictions to the above data, we were able to estimate the threshold number of E2-CD81 complexes necessary for HCV entry into human hepatoma-derived cells. We found that depending on the E2-CD81 binding affinity, between 1 and 13 E2-CD81 complexes are necessary for HCV entry. With this estimate, our model captured data from independent experiments that employed different HCV clones and cells with distinct CD81 expression levels, indicating that the estimate is robust. Our study thus quantifies the molecular requirements of HCV entry and suggests guidelines for intervention strategies that target the E2-CD81 interaction. Further, our model presents a framework for quantitative analyses of cell culture studies now extensively employed to investigate HCV infection.