The silicon stable isotope distribution along the GEOVIDE section (GEOTRACES GA-01) of the North Atlantic Ocean

The stable isotope composition of dissolved silicon in seawater (δ30SiDSi) was examined at 10 stations along the GEOVIDE section (GEOTRACES GA-01), spanning the North Atlantic Ocean (40–60∘ N) and Labrador Sea. Variations in δ30SiDSi below 500 m were closely tied to the distribution of water masses. Higher δ30SiDSi values are associated with intermediate and deep water masses of northern Atlantic or Arctic Ocean origin, whilst lower δ30SiDSi values are associated with DSi-rich waters sourced ultimately from the Southern Ocean. Correspondingly, the lowest δ30SiDSi values were observed in the deep and abyssal eastern North Atlantic, where dense southern-sourced waters dominate. The extent to which the spreading of water masses influences the δ30SiDSi distribution is marked clearly by Labrador Sea Water (LSW), whose high δ30SiDSi signature is visible not only within its region of formation within the Labrador and Irminger seas, but also throughout the mid-depth western and eastern North Atlantic Ocean. Both δ30SiDSi and hydrographic parameters document the circulation of LSW into the eastern North Atlantic, where it overlies southern-sourced Lower Deep Water. The GEOVIDE δ30SiDSi distribution thus provides a clear view of the direct interaction between subpolar/polar water masses of northern and southern origin, and allow examination of the extent to which these far-field signals influence the local δ30SiDSi distribution

A new representation in evolutionary algorithms for the optimization of bioprocesses

Evolutionary Algorithms (EAs) have been used to achieve optimal feedforward control in a number of fed-batch fermentation processes. Typically, the optimization purpose is to set the optimal feeding trajectory, being the feeding profile over time given by a piecewise linear function, in order to reduce the number of parameters to the optimization algorithm. In this work, a novel representation scheme for the encoding of the feeding trajectory over time is proposed. Each gene in the variable sized chromosome has two components: a time label and the real value of the variable. The new approach is compared with a traditional real-valued EA, with chromosomes of constant size and fixed discretization steps. Three distinct case studies are presented, taken from previous work from the authors and literature, all considering the optimization of fed-batch fermentation processes. The experimental results show that the proposed approach is capable of results better or at the same level of quality of the best traditional EAs and is able to automatically evolve the best discretization steps for each case, thus simplifying the EA's setup.Fundação para a Ciência e Tecnologia (FCT) - 59899/EIA/POSC/2004

Removing zero Lyapunov exponents in volume-preserving flows

Baraviera and Bonatti proved that it is possible to perturb, in the c^1 topology, a volume-preserving and partial hyperbolic diffeomorphism in order to obtain a non-zero sum of all the Lyapunov exponents in the central direction. In this article we obtain the analogous result for volume-preserving flows.Comment: 10 page

Ab initio study of electron transport in dry poly(G)-poly(C) A-DNA strands

The bias-dependent transport properties of short poly(G)-poly(C) A-DNA strands attached to Au electrodes are investigated with first principles electronic transport methods. By using the non- equilibrium Green's function approach combined with self-interaction corrected density functional theory, we calculate the fully self-consistent coherent I-V curve of various double-strand polymeric DNA fragments. We show that electronic wave-function localization, induced either by the native electrical dipole and/or by the electrostatic disorder originating from the first few water solvation layers, drastically suppresses the magnitude of the elastic conductance of A-DNA oligonucleotides. We then argue that electron transport through DNA is the result of sequence-specific short-range tunneling across a few bases combined with general diffusive/inelastic processes.Comment: 15 pages, 13 figures, 1 tabl

A systems biology approach for the optimization of recombinant protein production in E. coli

Escherichia coli has been the organism of choice for the production of many recombinant proteins with high therapeutic value. However, while the research on molecular biology has allowed the development of very strong promoters, there are still several phenomena associated with this process that have hampered the full use of that promoter strength, namely the aerobic acetate production associated with high specific growth rates. The presence of acetate is known to reduce both biomass yield on the chosen carbon source and protein productivity while totally inhibiting growth when present at high concentrations due to its toxic effect. While there have been several studies covering the recombinant protein production process with the bacterium Escherichia coli, including genome-scale analysis of the transcriptome, proteome, fluxome or metabolome, there has been a lack of an integrative approach that is able to combine genomic and physiological information about those processes with high-throughput analysis. Also, the existence of genome-scale models that cover both stoichiometry and regulation of some pathways has not been taken into account in genome-scale data analysis and for the consequent formulation of hypothesis and development of new strategies for improving the performance of the process. In our group, a high-cell density fed-batch process for recombinant protein production in E. coli is being studied, giving particular relevance to acetate production. A systematic approach is being used, by first compiling the existing knowledge about this phenomenon, extending existing genome-scale models to accommodate that knowledge, derive hypothesis in silico that are then tested by using genome-scale analysis of the omes. A reliable fermentation process was developed to be able to reproducibly study this phenomenon in different strains in order to reduce external variances to a minimum

On-line calculation of CTR and OTR during high-cell density recombinant E. coli fed-batch fermentation: MS calibration, on-line data acquisition, analysis and integration

During a high-cell density fed-batch fermentation of recombinant E. coli, both oxygen and carbon dioxide transfer rates (OTR and CTR) were calculated on-line from inlet and exhaust gas composition measurements obtained with Mass Spectrometry (MS) and from the culture weight. These rates, together with on-line measurements of acetate concentration by Flow Injection Analysis, were used to implement an adaptive control law in a real fermentation. For MS calibration, a new method was adapted, where several gas mixtures were used, their composition being chosen from the analysis of the expected experimental space. A calibration factor was then calculated by linear regression that correlated the pressure values obtained in the MS for a given mass to charge ratio with the mixture composition in oxygen, carbon dioxide and nitrogen. During the fermentation, 12 MS and weight data points (corresponding to approximately 3 minutes) were acquired in a developed LabVIEW subroutine where a C embedded window performed data analysis by statistical significance assessment to exclude potential outliers. Afterwards, the noise was partially eliminated by applying a moving average filter and MS raw data was converted to molar fractions, according to the calculated calibration factors. CTR and OTR values are then computed from inlet and exhaust gas composition and reactor weight. This LabVIEW subroutine was then integrated in a supervisory programme, together with the measurements of other equipments, acquired by serial ports or analog input and using string interpretation or the standard Windows Dynamic Data Exchange (DDE) protocol

Model-based adaptive control of acetate concentration during the production of recombinant proteins with E. coli

A model-based adaptive linearizing control law was derived for the regulation of the acetate concentration during the fed-batch fermentation of recombinant proteins with high cell density culture of Escherichia coli growing on glucose. An unstructured model for the growth was applied to the major metabolic pathways: oxidative growth on glucose, fermentative growth on glucose, oxidative growth on acetate, and maintenance. A model order reduction method was used to allow the development of the control algorithm without the knowledge of the kinetic structure being necessary. The non-linear model was subjected to transformations in order to obtain a linear behaviour for the control loop when a non-linear control is applied. The control law requires on-line acetate and carbon dioxide and oxygen transfer rates measurements. Acetate measurements are achieved with a developed Flow Injection Analysis (FIA) physical-chemical method. The gas transfer rates are calculated from gas analysis data obtained with a Mass Spectrometer (MS) connected to the exhaust gas line of the fermenter and also to the inlet aeration line. These calculations, as well as the implementation of the control law were performed through a MATLAB script embedded in a LABView program that also acquired data from the FIA system and other relevant state variables from the fermenter Digital Control Unit. Copyright 2002 IFACAgência de Inovação - PROTEXPRESS.Fundação para a Ciência e a Tecnologia (FCT) – PRAXIS XXI/BD/16961/98

Optimization of fed-batch fermentation processes with bio-inspired algorithms

The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.The work is partially funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects Ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/ES/UI0752/2011

Optimisation Methods for Improving Fed-batch Cultivation of E. Coli Producing Recombinant Proteins

Two optimisation techniques for the fed-batch cultivation of high cell density Escherichia coli producing recombinant proteins were compared. An unstructured model for the growth, based on the General State Space Dynamical Model [1] was used to represent the four major metabolic pathways: oxidative growth on glucose, fermentative growth on glucose, oxidative growth on acetate, and maintenance. The dilution rate (dependent on the substrate feed rate) was chosen as the input variable. Recombinant protein production is known to be proportional, in our system, to the biomass concentration. Thus, biomass productivity was chosen as the criterion to be maximized. The two methods compared were a first order gradient method based on Pontryagin’s minimum principle and a stochastic method based on the biological principle of natural evolution, using a genetic algorithm. The former method revealed less efficient concerning to the computed maximum, and dependence on good initial values