Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization

[EN] Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological coin portents. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. (C) 2016 IFAC (International Federation Of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1). Y.B. thanks grant FP/2013-3242 of Universitat Politecnica de Valencia and Becas Iberoamerica of Santander Group, Spain 2015. G.R.M. thanks the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. We are grateful to Dr. C,Bauerl and Dr, D. Provencio at the SB2CLab for their help in plasmid construction and getting experimental data. Also to Dr. V. Monedero at IATACSIC for allowing us to use the POLARstar plate reader at his lab,Boada-Acosta, YF.; Vignoni, A.; Reynoso Meza, G.; Picó, J. (2016). Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization. IFAC-PapersOnLine. 49(26):77-82. https://doi.org/10.1016/j.ifacol.2016.12.106S7782492

Magnetic resonance lung function – a breakthrough for lung imaging and functional assessment? A phantom study and clinical trial

BACKGROUND: Chronic lung diseases are a major issue in public health. A serial pulmonary assessment using imaging techniques free of ionizing radiation and which provides early information on local function impairment would therefore be a considerably important development. Magnetic resonance imaging (MRI) is a powerful tool for the static and dynamic imaging of many organs. Its application in lung imaging however, has been limited due to the low water content of the lung and the artefacts evident at air-tissue interfaces. Many attempts have been made to visualize local ventilation using the inhalation of hyperpolarized gases or gadolinium aerosol responding to MRI. None of these methods are applicable for broad clinical use as they require specific equipment. METHODS: We have shown previously that low-field MRI can be used for static imaging of the lung. Here we show that mathematical processing of data derived from serial MRI scans during the respiratory cycle produces good quality images of local ventilation without any contrast agent. A phantom study and investigations in 85 patients were performed. RESULTS: The phantom study proved our theoretical considerations. In 99 patient investigations good correlation (r = 0.8; p ≤ 0.001) was seen for pulmonary function tests and MR ventilation measurements. Small ventilation defects were visualized. CONCLUSION: With this method, ventilation defects can be diagnosed long before any imaging or pulmonary function test will indicate disease. This surprisingly simple approach could easily be incorporated in clinical routine and may be a breakthrough for lung imaging and functional assessment

Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression

Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled, or heuristic approximations of the underlying regulatory mechanisms. We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence, as a function of transcription factor concentrations and their DNA-binding specificities. It uses statistical thermodynamics theory to model not only protein-DNA interaction, but also the effect of DNA-bound activators and repressors on gene expression. In addition, the model incorporates mechanistic features such as synergistic effect of multiple activators, short range repression, and cooperativity in transcription factor-DNA binding, allowing us to systematically evaluate the significance of these features in the context of available expression data. Using this model on segmentation-related enhancers in Drosophila, we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone. We find clear support for the phenomenon of short-range repression, where repressors do not directly interact with the basal transcriptional machinery. We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution, and a noticeable fraction of these undergo lineage-specific changes. Our implementation of the model, called GEMSTAT, is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences

Current Mathematical Methods Used in QSAR/QSPR Studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future

Brown seaweeds as a source of anti-hyaluronidase compounds

Hyaluronidase enzymes disrupt hyaluronic acid, causing angiogenesis, tumor invasiveness, metastasis, inflammation, and skin aging. Phlorotannin, alginate and fucoidan were extracted successively from the blade and stipe samples of two Sargassum seaweeds and Eisenia arborea thallus. These were evaluated for their in vitro anti-hyaluronidase and antioxidant activities (DPPH and reducing power assay). The crude phlorotannin content was highest in the blade of S. tenerrimum (22.405 +/- 3.6 mu g/mg), followed by S. vulgare blade (18.385 +/- 3.29 mu g/mg) with lower amounts in the stipe portion. The highest yield of alginate and fucoidans was obtained from the blade samples of S. vulgare (0.322 +/- 0.38 and 0.198 +/- 0.016%), followed by S. tenerrimum (0.090 +/- 0.01 and 0.063 +/- 0.005%) and E. arborea thallus (0.047 +/- 0.008 and 0.032 +/- 0.003%). The sulfate content was higher in fucoidan than alginate extracted from the stipe regions of the seaweeds. Phlorotannin, fucoidan and alginate from S. vulgare, S. tenerrimum, and E. arborea possessed anti-hyaluronidase activity as evident by a decrease in the N-acetylglucosamine release. The highest anti-hyaluronidase activity was achieved in the extract of S. tennerimum blade (37.67 +/- 2.3% inhibition) due to its high phlorotannin content. Alginate and fucoidan extracted from the stipes of Sargassum species possess higher bioactivities than the blade samples. The FTIR study ascertained that alginate with a high guluronic acid and high sulfated fucoidan were extracted from the stipe samples compared to the blade samples. This increased viscosity and promoted bioactivity respectively. Further studies to evaluate the emulsifying and viscosity properties of these compounds are required before they can be considered for commercial applications.DL 57/2016info:eu-repo/semantics/publishedVersio