Parameter estimate of signal transduction pathways

BACKGROUND: The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway. RESULTS: Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions. CONCLUSION: Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data

ELISA-BASE: an integrated bioinformatics tool for analyzing and tracking ELISA microarray data

Summary:ELISA-BASE is an open source database for capturing, organizing and analyzing enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Software Environment (BASE) database system

A proteomics platform combining depletion, multi-lectin affinity chromatography (M-LAC), and isoelectric focusing to study the breast cancer proteome

Contains fulltext : 95592.pdf (publisher's version ) (Open Access)The discovery of breast cancer associated plasma/serum biomarkers is important for early diagnosis, disease mechanism elucidation, and determination of treatment strategy for the disease. In this study of serum samples, a multidimensional fractionation platform combined with mass spectrometric analysis were used to achieve the identification of medium to lower abundance proteins, as well as to simultaneously detect glycan and abundance changes. Immuno-affinity depletion and multi-lectin chromatography (M-LAC) were integrated into an automated HPLC platform to remove high abundance protein and fractionate glycoproteins. The collected glycoproteomes were then subjected to isoelectric focusing (IEF) separation by a digital ProteomeChip (dPC), followed by in-gel digestion and LC-MS analysis using an Orbitrap mass spectrometer. As a result, the total number of identified proteins increased significantly when the IEF fractionation step was included as part of the platform. Relevant proteins with biological and disease significance were observed and the dynamic range of the serum proteome measurement was extended. In addition, potential glycan changes were indicated by comparing proteins in control and cancer samples in terms of their affinity to the multi-lectin column (M-LAC) and the pI profiles in IEF separation. In conclusion, a proteomics platform including high abundance protein depletion, lectin affinity fractionation, IEF separation, and LC-MS analysis has been applied to discover breast cancer-associated proteins. The following candidates, thrombospondin-1 and 5, alpha-1B-glycoprotein, serum amyloid P-component, and tenascin-X, were selected as promising examples of the use of this platform. They show potential abundance and glycan changes and will be further investigated in future studies