The Self-Limiting Dynamics of TGF-β Signaling In Silico and In Vitro,with Negative Feedback through PPM1A Upregulation

The TGF-β/Smad signaling system decreases its activity through strong negative regulation. Several molecular mechanisms of negative regulation have been published, but the relative impact of each mechanism on the overall system is unknown. In this work, we used computational and experimental methods to assess multiple negative regulatory effects on Smad signaling in HaCaT cells. Previously reported negative regulatory effects were classified by time-scale: degradation of phosphorylated R-Smad and I-Smad-induced receptor degradation were slow-mode effects, and dephosphorylation of R-Smad was a fast-mode effect. We modeled combinations of these effects, but found no combination capable of explaining the observed dynamics of TGF-β/Smad signaling. We then proposed a negative feedback loop with upregulation of the phosphatase PPM1A. The resulting model was able to explain the dynamics of Smad signaling, under both short and long exposures to TGF-β. Consistent with this model, immuno-blots showed PPM1A levels to be significantly increased within 30 min after TGF-β stimulation. Lastly, our model was able to resolve an apparent contradiction in the published literature, concerning the dynamics of phosphorylated R-Smad degradation. We conclude that the dynamics of Smad negative regulation cannot be explained by the negative regulatory effects that had previously been modeled, and we provide evidence for a new negative feedback loop through PPM1A upregulation. This work shows that tight coupling of computational and experiments approaches can yield improved understanding of complex pathways.     

A dynamic mathematical model to clarify signaling circuitry underlying programmed cell death control in Arabidopsis disease resistance

Plant cells undergo programmed cell death in response to invading pathogens. This cell death limits the spread of the infection and triggers whole plant antimicrobial and immune responses. The signaling network connecting molecular recognition of pathogens to these responses is a prime target for manipulation in genetic engineering strategies designed to improve crop plant disease resistance. Moreover, as alterations to metabolism can be misinterpreted as pathogen infection, successful plant metabolic engineering will ultimately depend on controlling these signaling pathways to avoid inadvertent activation of cell death. Programmed cell death resulting from infection of Arabidopsis thaliana with Pseudomonas syringae bacterial pathogens was chosen as a model system. Signaling circuitry hypotheses in this model system were tested by construction of a differential-equations-based mathematical model. Model-based simulations of time evolution of signaling components matched experimental measurements of programmed cell death and associated signaling components obtained in a companion study. Simulation of systems-level consequences of mutations used in laboratory studies led to two major improvements in understanding of signaling circuitry: (1) Simulations supported experimental evidence that a negative feedback loop in salicylic acid biosynthesis postulated by others does not exist. (2) Simulations showed that a second negative regulatory circuit for which there was strong experimental support did not affect one of two pathways leading to programmed cell death. Simulations also generated testable predictions to guide future experiments. Additional testable hypotheses were generated by results of individually varying each model parameter over 2 orders of magnitude that predicted biologically important changes to system dynamics. These predictions will be tested in future laboratory studies designed to further elucidate the signaling network control structure.     

Mathematical Modelling of the MAP Kinase Pathway Using Proteomic Datasets

The advances in proteomics technologies offer an unprecedented opportunity and valuable resources to understand how living organisms execute necessary functions at systems levels. However, little work has been done up to date to utilize the highly accurate spatio-temporal dynamic proteome data generated by phosphoprotemics for mathematical modeling of complex cell signaling pathways. This work proposed a novel computational framework to develop mathematical models based on proteomic datasets. Using the MAP kinase pathway as the test system, we developed a mathematical model including the cytosolic and nuclear subsystems; and applied the genetic algorithm to infer unknown model parameters. Robustness property of the mathematical model was used as a criterion to select the appropriate rate constants from the estimated candidates. Quantitative information regarding the absolute protein concentrations was used to refine the mathematical model. We have demonstrated that the incorporation of more experimental data could significantly enhance both the simulation accuracy and robustness property of the proposed model. In addition, we used the MAP kinase pathway inhibited by phosphatases with different concentrations to predict the signal output influenced by different cellular conditions. Our predictions are in good agreement with the experimental observations when the MAP kinase pathway was inhibited by phosphatase PP2A and MKP3. The successful application of the proposed modeling framework to the MAP kinase pathway suggests that our method is very promising for developing accurate mathematical models and yielding insights into the regulatory mechanisms of complex cell signaling pathways.     

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.     

Modeling allosteric regulation of de novo pyrimidine biosynthesis in Escherichia coli

With the emergence of multifaceted bioinformatics-derived data, it is becoming possible to merge biochemical and physiological information to develop a new level of understanding of the metabolic complexity of the cell. The biosynthetic pathway of de novo pyrimidine nucleotide metabolism is an essential capability of all free-living cells, and it occupies a pivotal position relative to metabolic processes that are involved in the macromolecular synthesis of DNA, RNA and proteins, as well as energy production and cell division. This regulatory network in all enteric bacteria involves genetic, allosteric, and physiological control systems that need to be integrated into a coordinated set of metabolic checks and balances. Allosterically regulated pathways constitute an exciting and challenging biosynthetic system to be approached from a mathematical perspective. However, to date, a mathematical model quantifying the contribution of allostery in controlling the dynamics of metabolic pathways has not been proposed. In this study, a direct, rigorous mathematical model of the de novo biosynthesis of pyrimidine nucleotides is presented. We corroborate the simulations with experimental data available in the literature and validate it with derepression experiments done in our laboratory. The model is able to faithfully represent the dynamic changes in the intracellular nucleotide pools that occur during metabolic transitions of the de novo pyrimidine biosynthetic pathway and represents a step forward in understanding the role of allosteric regulation in metabolic control.     

Transient pulse formation in jasmonate signaling pathway

The jasmonate (JA) signaling pathway in plants is activated as defense response to a number of stresses like attacks by pests or pathogens and wounding by animals. Some recent experiments provide significant new knowledge on the molecular detail and connectivity of the pathway. The pathway has two major components in the form of feedback loops, one negative and the other positive. We construct a minimal mathematical model, incorporating the feedback loops, to study the dynamics of the JA signaling pathway. The model exhibits transient gene expression activity in the form of JA pulses in agreement with experimental observations. The dependence of the pulse amplitude, duration and peak time on the key parameters of the model is determined computationally. The deterministic and stochastic aspects of the pathway dynamics are investigated using both the full mathematical model and a reduced version of it. We also compare the mechanism of pulse formation with the known mechanisms of pulse generation in some bacterial and viral systems.     

Model predictions of MDM2 mediated cell regulation

In this work we present a mathematical approach to elucidate possible mechanisms involving mdm2 in the regulation of the cell cycle. It has been experimentally shown that the over-expression of MDM2 leads to decoupling of DNA synthesis with mitosis resulting in polyploidy cells with multiple copies of their genomes. The function of MDM2 that uncouples the DNA synthesis phase (S) and the Mitosis phase (M) is unclear. To answer this question, we first formulate a mathematical model of the dynamics of the cell cycle regulatory proteins during the DNA synthesis phase and mitosis. This model is then tested for bifurcation that produces period doubling cascades that we relate to the biological event of polyploidy. The model formulation, the underlying biology, and the bifurcation results to delineate the unknown function of MDM2 are presented. Based on reproducing known experimental result of polyploidy in MDM2 overexpressed cells, we propose several possible functions of mdm2, i.e., possible interactions with the other cell cycle regulating proteins that will result in uncoupling the S and M phases. We conclude that the most likely unknown function of MDM2 leading to the decoupling of the S and M phases is an obstruction of the activation of Cdc25C by MDM2.     

Model Predictions of MDM2 Mediated Cell Regulation

In this work we present a mathematical approach to elucidate possible mechanisms involving mdm2 in the regulation of the cell cycle. It has been experimentally shown that the over-expression of MDM2 leads to decoupling of DNA synthesis with mitosis resulting in polyploidy cells with multiple copies of their genomes. The function of MDM2 that decouples the DNA synthesis phase (S) and the Mitosis phase (M) is unclear. To answer this question, we first formulate a mathematical model of the dynamics of the cell cycle regulatory proteins during the DNA synthesis phase and mitosis. This model is then tested for bifurcation that produces period doubling cascades that we relate to the biological event of polyploidy. The model formulation, the underlying biology, and the bifurcation results to delineate the unknown function of MDM2 are presented. Based on reproducing known experimental result of polyploidy in MDM2 overexpressed cells, we propose several possible functions of mdm2, i.e., possible interactions with the other cell cycle regulating proteins that will result in decoupling the S and M phases. We conclude that the most likely unknown function of MDM2 leading to the decoupling of the S and M phases is an obstruction of the activation of cdc25C by MDM2.     

Characterization of the adaptive response and growth upon hyperosmotic shock in Saccharomyces cerevisiae

Molecular and physiological details of osmoadaptation in yeast Saccharomyces cerevisiae are well characterized. It is well known that a cell, upon osmotic shock, delays its growth, produces a compatible solute like glycerol in yeast to maintain the osmotic equilibrium. Many genes are regulated by the hyperosmolarity glycerol (HOG) singling pathway, some of which in turn control the carbon flux in the glycolytic pathway for glycerol synthesis and reduced growth. The whole process of survival of cells under hyperosmotic stress is controlled at multiple levels in signaling and metabolic pathways. To better understand the multi-level regulations in yeast to osmotic shock, a mathematical model is formulated which integrates the growth and the osmoadaptation process. The model included the HOG pathway which consists of Sho1 and Sln1 signaling branches, gene regulation, metabolism and cell growth on glucose and ethanol. Experiments were performed to characterize the effect of various concentrations of salt on the wild-type and mutant strains. The model was able to successfully predict the experimental observations for both the wild-type and mutant strains. Further, the model was used to analyze the effects of various regulatory mechanisms prevalent in the signaling and metabolic pathways which are essential in achieving optimum growth in a saline medium. The analysis demonstrated the relevance of the combined effects of regulation at several points in the signaling and metabolic pathways including activation of GPD1 and GPD2, inhibition of PYK and PDC1, closure of the Fps1 channel, volume effect on the glucose uptake rate, downregulation of ethanol synthesis and upregulation of ALD6 for acetate synthesis. The analysis demonstrated that these combined effects orchestrated the phenomena of adaptation to osmotic stress in yeast.     

A simple molecular mathematical model of mammalian hibernation

A simple model of the dynamics of the body temperature of a hibernating mammal is presented. Our model provides a good match to experimental data, showing the interruption of low-temperature torpor bouts with periodic interbout arousals (IBAs). In this paper we present a mathematical model of the molecules that participate in the initiation, regulation, and maintenance of the hibernating state. This model can be used to describe the role of regulatory molecules, signal transducers, downstream target enzymes, structural proteins, or metabolites. Because many of the biochemical mechanisms are unknown, this is a preliminary and largely phenomenological model that we hope will inspire further investigation.     

Analysis of cytokine dynamics in corneal allograft rejection

Motivated by the discovery of oscillations in tumour necrosis factor-alpha (TNF-alpha) concentration in the aqueous humour of rabbits undergoing corneal allograft rejection, a simple mathematical model is developed for the regulation of TNF-alpha, which incorporates both negative feedback and amplification pathways. Mathematical analysis reveals a surprisingly rich behavioural repertoire for this simple cytokine pathway, including excitability, threshold behaviour, hysteresis, oscillations and bistability. This suggests new possibilities for experimental demonstration, and reveals the potential contributions of nonlinear dynamics to understanding cytokine regulation.     

Coherent and robust modulation of a metabolic network by cytoskeletal organization and dynamics

In order to investigate the influence of cytoskeletal organization and dynamics on cellular biochemistry, a mathematical model was formulated based on our own experimental evidence. The model couples microtubular protein (MTP) dynamics to the glycolytic pathway and its branches: the Krebs cycle, ethanolic fermentation, and the pentose phosphate (PP) pathway. Results show that the flux through glycolysis coherently and coordinately increases or decreases with increased or decreased levels of polymerized MTP, respectively. The rates of individual enzymatic steps and metabolite concentrations change with the polymeric status of MTP throughout the metabolic network. Negative control is exerted by the PP pathway on the glycolytic flux, and the extent of inhibition depends inversely on the polymerization state of MTP, i.e. a high degree of polymerization relieves the negative control. The stability of the model's steady state dynamics for a wide range of variation of metabolic parameters increased with the degree of polymerized MTP. The findings indicate that the organization of the cytoskeleton bestows coherence and robustness to the coordination of cellular metabolism.     

miR-29a靶基因预测及其相关生物信息学分析

目的:通过对miR-29a进行靶基因预测及相关生物信息学分析,为miR-29a靶基因的实验验证提供数据支持,以期为深入研究miR-29a的生物学功能和调控机制提供理论指导。方法:利用PubMed检索miR-29a相关文章,通过miRBase在线工具分析miR-29a序列。应用TargetScan及miRNAda两种计算方法预测miR-29a靶基因并取其交集作为分析的基因集合,分别进行基因本体(gene ontology,GO)中的分子功能和生物学过程以及KEGG(Kyoto Encyclopedia of Genes and Genomes)生物通路富集分析。结果:(1)miR-29a序列在多物种间具有高度保守性。(2)两种方法预测miR-29a靶基因交集共191个。(3)miR-29a靶基因GO分子功能集中于转录因子活性、DNA结合和钙离子结合等(P0.05);miR-29a靶基因GO生物学过程集中于调控转录、细胞粘附、细胞增殖与凋亡等(P0.05);KEGG生物通路主要富集于PI3K-AKT信号通路、JAK-STAT信号通路、T细胞受体信号通路和胰岛素信号通路等信号转导通路,以及肺小细胞癌和子宫内膜癌等疾病通路(P0.05)。结论:miR-29a可能通过参与多个靶基因信号通路的调控,在机体的多种生理病理过程中发挥重要作用,是一个颇有研究价值的生物学靶标。     

Curcumin suppresses Janus kinase-STAT inflammatory signaling through activation of Src homology 2 domain-containing tyrosine phosphatase 2 in brain microglia

Curcumin has been strongly implicated as an anti-inflammatory agent, but the precise mechanisms of its action are largely unknown. In this study, we show that the inhibitory action of curcumin on Janus kinase (JAK)-STAT signaling can contribute to its anti-inflammatory activity in the brain. In both rat primary microglia and murine BV2 microglial cells, curcumin effectively suppressed the ganglioside-, LPS-, or IFN-gamma-stimulated induction of cyclooxygenase-2 and inducible NO synthase, important enzymes that mediate inflammatory processes. These anti-inflammatory effects appear to be due, at least in part, to the suppression of the JAK-STAT inflammatory signaling cascade. Curcumin markedly inhibited the phosphorylation of STAT1 and 3 as well as JAK1 and 2 in microglia activated with gangliosides, LPS, or IFN-gamma. Curcumin consistently suppressed not only NF binding to IFN-gamma-activated sequence/IFN-stimulated regulatory element, but also the expression of inflammation-associated genes, including ICAM-1 and monocyte chemoattractant protein 1, whose promoters contain STAT-binding elements. We further show that activation of Src homology 2 domain-containing protein tyrosine phosphatases (SHP)-2, a negative regulator of JAK activity, is likely to be one of the mechanisms underlying the curcumin-mediated inhibition of JAK-STAT signaling. Treatment of microglial cells with curcumin led to an increase in phosphorylation and association with JAK1/2 of SHP-2, which inhibit the initiation of JAK-STAT inflammatory signaling in activated microglia. Taken together, these data suggest curcumin suppresses JAK-STAT signaling via activation of SHP-2, thus attenuating inflammatory response of brain microglial cells.     

A Mathematical Model of the Enhancement of Tumor Vaccine Efficacy by Immunotherapy

TGF-β is an immunoregulatory protein that contributes to inadequate antitumor immune responses in cancer patients. Recent experimental data suggests that TGF-β inhibition alone, provides few clinical benefits, yet it can significantly amplify the anti-tumor immune response when combined with a tumor vaccine. We develop a mathematical model in order to gain insight into the cooperative interaction between anti-TGF-β and vaccine treatments. The mathematical model follows the dynamics of the tumor size, TGF-β concentration, activated cytotoxic effector cells, and regulatory T cells. Using numerical simulations and stability analysis, we study the following scenarios: a control case of no treatment, anti-TGF-β treatment, vaccine treatment, and combined anti-TGF-β vaccine treatments. We show that our model is capable of capturing the observed experimental results, and hence can be potentially used in designing future experiments involving this approach to immunotherapy.     

Systems biology of JAK-STAT signalling in human malignancies

Originally implicated in the regulation of survival, proliferation and differentiation of haematopoietic cells, the JAK-STAT pathway has also been linked to developmental processes, growth control and maintenance of homeostasis in a variety of other cells and tissues. Although it remains a complex system, its relative simplicity and the availability of molecular data makes it particularly attractive for modelling approaches. In this review, we will focus on JAK-STAT signalling in the context of cancer and present efforts to investigate signalling dynamics with the help of mathematical models. We describe the modelling workflow that realises a systems biology approach and give an example for interferon-γ signalling in pancreatic stellate cells.