Evolutionary computation for predicting optimal reaction knockouts and enzyme modulation strategies

One of the main purposes of Metabolic Engineering is the quantitative prediction of cell behaviour under selected genetic modifications. These methods can then be used to support adequate strain optimization algorithms in a outer layer. The purpose of the present study is to explore methods in which dynamical models provide for phenotype simulation methods, that will be used as a basis for strain optimization algorithms to indicate enzyme under/over expression or deletion of a few reactions as to maximize the production of compounds with industrial interest. This work details the developed optimization algorithms, based on Evolutionary Computation approaches, to enhance the production of a target metabolite by finding an adequate set of reaction deletions or by changing the levels of expression of a set of enzymes. To properly evaluate the strains, the ratio of the flux value associated with the target metabolite divided by the wild-type counterpart was employed as a fitness function. The devised algorithms were applied to the maximization of Serine production by Escherichia coli, using a dynamic kinetic model of the central carbon metabolism. In this case study, the proposed algorithms reached a set of solutions with higher quality, as compared to the ones described in the literature using distinct optimization techniques.This work is funded by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011. The work is also partially funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT within project ref. COMPETE FCOMP-01-0124- FEDER-015079. PEs work is supported by a PhD grant FCT SFRH/BD/51016/2010 from the Portuguese FCT

Nanometric pitch in modulated structures of twist-bend nematic liquid crystals

The extended Frank elastic energy density is used to investigate the existence of a stable periodically modulate structure that appears as a ground state exhibiting a twist-bend molecular arrangement. For an unbounded sample, we show that the twist-bend nematic phase $N_{TB}$ is characterized by a heliconical structure with a pitch in the nano-metric range, in agreement with experimental results. For a sample of finite thickness, we show that the wave vector of the stable periodic structure depends not only on the elastic parameters but also on the anchoring energy, easy axis direction, and the thickness of the sample.Comment: 11 page

The 3D numerical simulation of near-source ground motion during the Marsica earthquake, central Italy, 100 years later

In this paper we show 3D physics-based numerical simulations of ground motion during one of the most devastating earthquakes in the recent Italian history, occurred on Jan 13, 1915, Marsica, Central Italy. The results provide a realistic estimate of the earthquake ground motion and fit reasonably well both the geodetic measurements of permanent ground settlement, and the observed macroseismic distribution of damage. In addition, these results provide a very useful benchmark to improve the current knowledge of near-source earthquake ground motion, including evaluation of the best distance metrics to describe the spatial variability of the peak values of ground motion, the relative importance of fault normal vs fault parallel components, the conditions under which vertical ground motion may prevail, as well as the adequacy of 1D vs 3D modelling of site amplification effects

A software tool for the simulation and optimization of dynamic metabolic models

In Systems Biology, there is a growing need for simulation and optimization tools for the prediction of the phenotypical behavior of microorganisms. In this paper, an open-source software platform is proposed to provide support for research in Metabolic Engineering, by implementing tools that enable the simulation and optimization of dynamic metabolic models using ordinary differential equations. Its main functionalities are related with (i) phenotype simulation of both wild type and mutant strains under given environmental conditions and (ii) strain optimization tackling tasks such as gene knockout selection or the definition of the optimal level of enzyme expression, given appropriate objective functions. The central carbon metabolism of E. coli was used as a case study, to illustrate the main features of the software

Evolutionary approaches for strain optimization using dynamic models under a metabolic engineering perspective

One of the purposes of Systems Biology is the quantitative modeling of biochemical networks. In this effort, the use of dynamical mathematical models provides for powerful tools in the prediction of the phenotypical behavior of microorganisms under distinct environmental conditions or subject to genetic modifications. The purpose of the present study is to explore a computational environment where dynamical models are used to support simulation and optimization tasks. These will be used to study the effects of two distinct types of modifications over metabolic models: deleting a few reactions (knockouts) and changing the values of reaction kinetic parameters. In the former case, we aim to reach an optimal knockout set, under a defined objective function. In the latter, the same objective function is used, but the aim is to optimize the values of certain enzymatic kinetic coefficients. In both cases, we seek for the best model modifications that might lead to a desired impact on the concentration of chemical species in a metabolic pathway. This concept was tested by trying to maximize the production of dihydroxyacetone phosphate, using Evolutionary Computation approaches. As a case study, the central carbon metabolism of Escherichia coli is considered. A dynamical model based on ordinary differential equations is used to perform the simulations. The results validate the main features of the approach

Molecular dysfunction and phenotypic derangement in diabetic cardiomyopathy

The high incidence and poor prognosis of heart failure (HF) patients affected with diabetes (DM) is in part related to a specific cardiac remodeling currently recognized as diabetic cardiomyopathy (DCM). This cardiac frame occurs regardless of the presence of coronary artery diseases (CAD) and it can account for 15-20% of the total diabetic population. The pathogenesis of DCM remains controversial, and several molecular and cellular alterations including myocardial hypertrophy, interstitial fibrosis, oxidative stress and vascular inflammation, have been postulated. The main cardio-vascular alterations associated with hyperglycemia comprise endothelial dysfunction, adverse effects of circulating free fatty acids (FFA) and increased systemic inflammation. High glucose concentrations lead to a loss of mitochondrial networks, increased reactive oxygen species (ROS), endothelial nitric oxide synthase (eNOS) activation and a reduction in cGMP production related to protein kinase G (PKG) activity. Current mechanisms enhance the collagen deposition with subsequent increased myocardial stiffness. Several concerns regarding the exact role of DCM in HF development such as having an appearance as either dilated or as a concentric phenotype and whether diabetes could be considered a causal factor or a comorbidity in HF, remain to be clarified. In this review, we sought to explain the different DCM subtypes and the underlying pathophysiological mechanisms. Therefore, the traditional and new molecular and signal alterations and their relationship with macroscopic structural abnormalities are described

A 3D printed drug delivery implant formed from a dynamic supramolecular polyurethane formulation

Using a novel molecular design approach, we have prepared a thermo-responsive supramolecular polyurethane as a matrix material for use in drug eluting implants. The dynamic supramolecular polyurethane (SPU) is able to self-assemble through hydrogen bonding and π-π stacking interactions, resulting in an addressable polymer network with a relatively low processing temperature. The mechanical properties of the SPU demonstrated the material was self-supporting, stiff, yet flexible thus making it suitable for hot-melt extrusion processing, inclusive of related 3D printing approaches. Cell-based toxicity assays revealed the SPU to be non-toxic and therefore a viable candidate as a biocompatible polymer for implant applications. To this end, the SPU was formulated with paracetamol (16 %w/w) and 4 wt% or 8 wt% poly(ethylene glycol) (PEG) as an excipient and hot melt extruded at 100 °C to afford a 3D printed prototype implant to explore the extended drug release required for an implant and the potential manipulation of the release profile. Furthermore, rheological, infra-red spectroscopy, powder X-ray diffraction and scanning electron microscopy studies revealed the chemical and physical properties and compatibility of the formulation components. Successful release of paracetamol was achieved from in vitro dissolution studies and it was predicted that the drug would be released over a period of up to 8.5 months with hydrophilic PEG being able to influence the release rate. This extended release time is consistent with applications of this novel dynamic polymer as a drug eluting implant matrix