Background: The study of genome-scale metabolic models and their underlying networks is one of the most important fields in systems biology. The complexity of these models and their description makes the use of computational tools an essential elemen
t in their research. Therefore there is a strong need of efficient and versatile computational tools for the research in this area. Results: In this manuscript we present PyNetMet, a Python library of tools to work with networks and metabolic models. These are open-source free tools for use in a Python platform, which adds considerably versatility to them when compared with their desktop software similars. On the other hand these tools allow one to work with different standards of metabolic models (OptGene and SBML) and the fact that they are programmed in Python opens the possibility of efficient integration with any other already existing Python tool. Conclusions: PyNetMet is, therefore, a collection of computational tools that will facilitate the research work with metabolic models and networks.
A wide range of applications and research has been done with genome-scale metabolic models. In this work we describe a methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in ord
er to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.
Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one persons work in order to satisfactory collect, a
nalyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed.
In this contribution, a design of a synthetic calibration genetic circuit to characterize the relative strength of different sensing promoters is proposed and its specifications and performance are analyzed via an effective mathematical model. Our ca
librator device possesses certain novel and useful features like modularity (and thus the possibility of being used in many different biological contexts), simplicity, being based on a single cell, high sensitivity and fast response. To uncover the critical model parameters and the corresponding parameter domain at which the calibrator performance will be optimal, a sensitivity analysis of the model parameters was carried out over a given range of sensing protein concentrations (acting as input). Our analysis suggests that the half saturation constants for repression, sensing and difference in binding cooperativity (Hill coefficients) for repression are the key to the performance of the proposed device. They furthermore are determinant for the sensing speed of the device, showing that it is possible to produce detectable differences in the repression protein concentrations and in turn in the corresponding fluorescence in less than two hours. This analysis paves the way for the design, experimental construction and validation of a new family of functional genetic circuits for the purpose of calibrating promoters.
In this work we study the radiative decay of dynamically generated $J^P=oh^-$ charm baryons into the ground state $J^P=oh^+$ baryons. Since different theoretical interpretations of these baryonic resonances, and in particular of the $Lambda_c(2595)$,
give different predictions, a precise experimental measurement of these decays would be an important step for understanding their nature.
We calculate the radiative decay width of the $psi(3770)$ into the dynamically generated scalar resonance X(3700) which is predicted in a previous paper. The results show that it is possible that the upgraded BEPC-II facility will generate enough sta
tistics in order to observe this decay and thus confirm the existence of the X(3700).
In two recent reactions by Belle producing $Dbar D$ and $Dbar D^*$ meson pairs, peaks above threshold have been measured in the differential cross sections, possibly indicating new resonances in these channels. We want to study such reactions from th
e point of view that the $D$ meson pairs are produced from already known or predicted resonances below threshold. Our study shows that the peak in the $Dbar D^*$ production is not likely to be caused by the X(3872) resonance, but the peak seen in $Dbar D$ invariant mass can be well described if the $Dbar D$ pair comes from the already predicted scalar X(3700) resonance.
We analyze two recent reactions of Belle, producing $Dbar D$ and $Dbar D^*$ states that have an enhancement of the invariant $Dbar D$, $Dbar D^*$ mass distribution close to threshold, from the point of view that they might be indicative of the existe
nce of a hidden charm scalar and an axial vector meson states below $Dbar D$ or $Dbar D^*$ thresholds, respectively. We conclude that the data is compatible with the existing prediction of a hidden charm scalar meson with mass around 3700 MeV, though other possibilities cannot be discarded. The peak seen in the $Dbar D^*$ spectrum above threshold is, however, unlikely to be due to a threshold enhancement produced by the presence, below threshold, of the hidden charm axial vector meson X(3872).
In this presentation I explain our framework for dynamically generating resonances from the meson meson interaction. Our model generates many poles in the T-matrix which are associated with known states, while at the same time new states are predicted.
We present the formalism for the decay of dynamically generated scalar mesons with open- or hidden-charm and give results for the decay of D^*_{s0} (2317) to gamma D_s^* plus that of a hidden charm scalar meson state predicted by the theory around 3700 MeV decaying into gamma J/psi.
