Measurement set selection of parameter estimation in biological system modelling - a case study of signal transduction pathways

Parameter estimation is a challenging problem for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy and the cost of experiments high. Accurate recovery of parameters depends on the quality and quantity of measurement data. It is therefore important to know which measurements to be taken when and how through optimal experimental design (OED). In this paper a method was proposed to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory was used to examine the number of necessary measurement variables. The priority of each measurement variable was determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method were shown through an example of signal pathway model

Determine measurement set for parameter estimation in biological systems modeling

Parameter estimation is challenging for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy, and the cost of experiments is high. Accurate recovery of parameters depend on the quantity and quality of measurement data. It is therefore important to know what measurements to be taken, when and how through optimal experimental design (OED). In this paper we present a method to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory is introduced to examine the number of necessary measurement variables. The priority of each measurement variable is determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method are illustrated through an example of a signal pathway model

Sensitivity analysis and experimental design of a stiff signal transduction pathway model

Sensitivity analysis is normally used to analyze how sensitive a system is with respect to the change of parameters or initial conditions and is perhaps best known in systems biology via the formalism of metabolic control analysis [1, 2]. The nuclear factor B (NF-B) signalling pathway is an important cellular signalling pathway, of which protein phosphorylation is a major factor controlling the activation of further downstream events. The NF-κB proteins regulate numerous genes that play important roles in inter- and intra-cellular signalling, cellular stress responses, cell growth, survival, and apoptosis. As such, its specificity and its role in the temporal control of gene expression are of crucial physiological interest

Robustness analysis of signaling transduction networks based on Monte-Carlo method

The dynamic behaviors of cell system were deep ly affected by structural complexity of cell signal transduction networks and uncertainty of kinetics parameters. How to quantitatively determinate the relation between system behaviors and parameters variations was an important p roblem of systems biology. In order to study robustness of NF - κB signal transduction networks, the parameters of system model were assigned to subject to stochastic distributions. Then, robustness of system output signal NF - κBn with respect to 64 parameters variations and amp litude variation of step input signal IKK was studied by means of Monte - Carlo method. The simulation results demonstrate that the oscillation behavior of system output signal NF - κBn is closely relative to 6 key rate constantswhose robustness isweak, and the amp litude variation of step input signal IKKmakes a great impact on the oscillation behavior of system output

Structural transformation of natural graphite by passage of an electric current

Transmission electron microscopy is used to investigate the effect of passage of an electric current on the structure of a natural graphite. It is shown that the passage of a current can produce structures apparently consisting of hollow three-dimensional graphitic shells bounded by curved and faceted planes, typically made up of 2 graphene layers. A high degree of alignment is often found between these structures, in contrast to the material produced when synthetic graphite is treated in a similar way. The transformed carbon frequently contains small bilayer nanotubes, which are sometimes seamlessly joined to the larger graphene structures. In other cases the nanotubes are encapsulated inside larger graphene structures. A possible mechanism for the formation of these encapsulated tubes is proposed. Studies of the kind described here may help to understand the failure of graphene devices by Joule heating. The transformed carbon might also have a number of potential applications

Nucleolin as Activator of Human Papillomavirus Type 18 Oncogene Transcription in Cervical Cancer

High risk human papillomaviruses (HPVs) are central to the development of cervical cancer and the deregulated expression of high risk HPV oncogenes is a critical event in this process. Here, we find that the cell protein nucleolin binds in a sequence-specific manner to the HPV18 enhancer. The DNA binding activity of nucleolin is primarily S phase specific, much like the transcription of the E6 and E7 oncoproteins of HPV18 in cervical cancer cells. Antisense inactivation of nucleolin blocks E6 and E7 oncogene transcription and selectively decreases HPV18+ cervical cancer cell growth. Furthermore, nucleolin controls the chromatin structure of the HPV18 enhancer. In contrast, HPV16 oncogene transcription and proliferation rates of HPV16+ SiHa cervical cancer cells are independent of nucleolin activity. Moreover, nucleolin expression is altered in HPV18+ precancerous and cancerous tissue from the cervix uteri. Whereas nucleolin was homogeneously distributed in the nuclei of normal epithelial cells, it showed a speckled nuclear phenotype in HPV18+ carcinomas. Thus, the host cell protein nucleolin is directly linked to HPV18-induced cervical carcinogenesis

Recent experimental results in sub- and near-barrier heavy ion fusion reactions

Recent advances obtained in the field of near and sub-barrier heavy-ion fusion reactions are reviewed. Emphasis is given to the results obtained in the last decade, and focus will be mainly on the experimental work performed concerning the influence of transfer channels on fusion cross sections and the hindrance phenomenon far below the barrier. Indeed, early data of sub-barrier fusion taught us that cross sections may strongly depend on the low-energy collective modes of the colliding nuclei, and, possibly, on couplings to transfer channels. The coupled-channels (CC) model has been quite successful in the interpretation of the experimental evidences. Fusion barrier distributions often yield the fingerprint of the relevant coupled channels. Recent results obtained by using radioactive beams are reported. At deep sub-barrier energies, the slope of the excitation function in a semi-logarithmic plot keeps increasing in many cases and standard CC calculations over-predict the cross sections. This was named a hindrance phenomenon, and its physical origin is still a matter of debate. Recent theoretical developments suggest that this effect, at least partially, may be a consequence of the Pauli exclusion principle. The hindrance may have far-reaching consequences in astrophysics where fusion of light systems determines stellar evolution during the carbon and oxygen burning stages, and yields important information for exotic reactions that take place in the inner crust of accreting neutron stars.Comment: 40 pages, 63 figures, review paper accepted for EPJ

Impaired high-density lipoprotein function in patients with heart failure

Background: We recently showed that, in patients with heart failure, lower high‐density lipoprotein (HDL) cholesterol concentration was a strong predictor of death or hospitalization for heart failure. In a follow‐up study, we suggested that this association could be partly explained by HDL proteome composition. However, whether the emerging concept of HDL function contributes to the prognosis of patients with heart failure has not been addressed. Methods and Results: We measured 3 key protective HDL function metrics, namely, cholesterol efflux, antioxidative capacity, and anti‐inflammatory capacity, at baseline and after 9 months in 446 randomly selected patients with heart failure from BIOSTAT‐CHF (A Systems Biology Study to Tailored Treatment in Chronic Heart Failure). Additionally, the relationship between HDL functionality and HDL proteome composition was determined in 86 patients with heart failure. From baseline to 9 months, HDL cholesterol concentrations were unchanged, but HDL cholesterol efflux and anti‐inflammatory capacity declined (both P<0.001). In contrast, antioxidative capacity increased (P<0.001). Higher HDL cholesterol efflux was associated with lower mortality after adjusting for BIOSTAT‐CHF risk models and log HDL cholesterol (hazard ratio, 0.81; 95% CI, 0.71–0.92; P=0.001). Other functionality measures were not associated with outcome. Several HDL proteins correlated with HDL functionality, mainly with cholesterol efflux. Apolipoprotein A1 emerged as the main protein associated with all 3 HDL functionality measures. Conclusions: Better HDL cholesterol efflux at baseline was associated with lower mortality during follow‐up, independent of HDL cholesterol. HDL cholesterol efflux and anti‐inflammatory capacity declined during follow‐up in patients with heart failure. Measures of HDL function may provide clinical information in addition to HDL cholesterol concentration in patients with heart failure

Direct Measurements of the Branching Fractions for D0→K−e+νeD^0 \to K^-e^+\nu_e and D0→π−e+νeD^0 \to \pi^-e^+\nu_e and Determinations of the Form Factors f+K(0)f_{+}^{K}(0) and f+π(0)f^{\pi}_{+}(0)

The absolute branching fractions for the decays $D^0 \to K^-e ^+\nu_e$ and $D^0 \to \pi^-e^+\nu_e$ are determined using $7584\pm 198 \pm 341$ singly tagged $\bar D^0$ sample from the data collected around 3.773 GeV with the BES-II detector at the BEPC. In the system recoiling against the singly tagged $\bar D^0$ meson, $104.0\pm 10.9$ events for $D^0 \to K^-e ^+\nu_e$ and $9.0 \pm 3.6$ events for $D^0 \to \pi^-e^+\nu_e$ decays are observed. Those yield the absolute branching fractions to be $BF(D^0 \to K^-e^+\nu_e)=(3.82 \pm 0.40\pm 0.27)$ and $BF(D^0 \to \pi^-e^+\nu_e)=(0.33 \pm 0.13\pm 0.03)$. The vector form factors are determined to be $|f^K_+(0)| = 0.78 \pm 0.04 \pm 0.03$ and $|f^{\pi}_+(0)| = 0.73 \pm 0.14 \pm 0.06$. The ratio of the two form factors is measured to be $|f^{\pi}_+(0)/f^K_+(0)|= 0.93 \pm 0.19 \pm 0.07$.Comment: 6 pages, 5 figure