combining first-principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, Fundação para a Ciência e Tecnologia (FCT).Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on threelayered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power.publishersversionpublishe

Immunostaining Protocol: P-Smad2 (Xenograft and Mice)

Metastasis depends on a gene program expressed by the tumor microenvironment upon TGF-beta stimulation. CRC (Colorectal cancer) cell lines did not induce robust stromal TGF- beta responses when injected into nude mice as shown by lack of p- SMAD2 accumulation in tumor-associated stromal cells. To enforce high TGF-beta signaling in xenografts, we engineered CRC cell lines to secrete active TGF-beta. Subcutaneous tumors obtained from HT29-M6TGF-β, KM12L4aTGF-β cells and SW48TGF-β cells contained abundant p-SMAD2+ stromal cells

Measurement of the 77Se(n,¿) cross section up to 200 keV at the n_TOF facility at CERN

The 77Se(n,¿) reaction is of importance for 77Se abundance during the slow neutron capture process in massive stars. We have performed a new measurement of the 77Se radiative neutron capture cross section at the Neutron Time-of-Flight facility at CERN. Resonance capture kernels were derived up to 51 keV and cross sections up to 200 keV. Maxwellian-averaged cross sections were calculated for stellar temperatures between kT=5keV and kT=100keV, with uncertainties between 4.2% and 5.7%. Our results lead to substantial decreases of 14% and 19% in 77Se abundances produced through the slow neutron capture process in selected stellar models of 15M¿ and 2M¿, respectively, compared to using previous recommendation of the cross section.This work was supported by the UK Science and Facilities Council (ST/M006085/1), the MSMT of the Czech Republic, the Charles University UNCE/SCI/013 project, the European Research Council ERC-2015-StG No. 677497, and by the funding agencies of the participating institutes. In line with the principles that apply to scientific publishing and the CERN policy in matters of scientific publications, the n_TOF Col- laboration recognizes the work of Y. Kopatch and V. Furman (JINR, Russia), who have contributed to the experiment used to obtain the results described in this paper.Article signat per 131 autors/es: N. V. Sosnin , C. Lederer-Woods, M. Krtiˇcka, R. Garg, M. Dietz, M. Bacak, M. Barbagallo, U. Battino, S. Cristallo, L. A. Damone, M. Diakaki, S. Heinitz, D. Macina, M. Mastromarco, F. Mingrone, A. St. J. Murphy, G. Tagliente, S. Valenta, D. Vescovi, O. Aberle, V. Alcayne, S. Amaducci, J. Andrzejewski, L. Audouin, V. Bécares, V. Babiano-Suarez, F. Beˇcváˇr, G. Bellia, E. Berthoumieux, J. Billowes, D. Bosnar, A. Brown, M. Busso, M. Caamaño, L. Caballero, F. Calviño, M. Calviani, D. Cano-Ott, A. Casanovas, F. Cerutti, Y. H. Chen, E. Chiaveri, N. Colonna, G. Cortés, M. A. Cortés-Giraldo, L. Cosentino, C. Domingo-Pardo, R. Dressler, E. Dupont, I. Durán, Z. Eleme, B. Fernández-Domínguez, A. Ferrari, P. Finocchiaro, K. Göbel, A. Gawlik-Rami˛ega, S. Gilardoni, T. Glodariu, I. F. Gonçalves, E. González-Romero, C. Guerrero, F. Gunsing, H. Harada, J. Heyse, D. G. Jenkins, E. Jericha, F. Käppeler, Y. Kadi, A. Kimura, N. Kivel, M. Kokkoris, D. Kurtulgil, I. Ladarescu, H. Leeb, J. Lerendegui-Marco, S. Lo Meo, S. J. Lonsdale, A. Manna, T. Martínez, A. Masi, C. Massimi, P. Mastinu, F. Matteucci, E. A. Maugeri, A. Mazzone, E. Mendoza, A. Mengoni, V. Michalopoulou, P. M. Milazzo, A. Musumarra, A. Negret, R. Nolte, F. Ogállar, A. Oprea, N. Patronis, A. Pavlik, J. Perkowski, L. Piersanti, I. Porras, J. Praena, J. M. Quesada, D. Radeck, D. Ramos-Doval, T. Rauscher, R. Reifarth, D. Rochman, C. Rubbia, M. Sabaté-Gilarte, A. Saxena, P. Schillebeeckx, D. Schumann, A. G. Smith, A. Stamatopoulos, J. L. Tain, T. Talip, A. Tarifeño-Saldivia, L. Tassan-Got, P. Torres-Sánchez, A. Tsinganis, J. Ulrich, S. Urlass, G. Vannini, V. Variale, P. Vaz, A. Ventura, V. Vlachoudis, R. Vlastou, A. Wallner, P. J. Woods,T. Wright, and P. Žugec.Postprint (published version

Immunostaining Protocol: P-Stat3 (Xenograft and Mice)

We sought to understand the mechanisms behind the potent effect of stromal TGF-beta program on the capacity of colorectal cancer (CRC) cells to initiate metastasis. We discovered that mice subcutaneous tumors and metastases generated in the context of a TGF-beta activated microenvironment displayed prominent accumulation of p-STAT3 in CRC cells compared with those derived from control cells. STAT3 signaling depended on GP130 as shown by strong reduction of epithelial p STAT3 levels upon GP130 shRNA-mediated knockdown in CRC cells

Constraints on the dipole photon strength for the odd uranium isotopes

Nuclear level densities (NLDs) and photon strength functions (PSFs), also called ¿ -ray or radiation strength functions, represent average properties of the nucleus in the regime of excitation where individual levels and transition probabilities by ¿ decay are not readily accessible by experimental or theoretical means. They are key ingredients for statistical calculations of the reaction cross sections involving ¿ rays via the Hauser-Feshbach approach [1], like inelastic scattering or neutron capture reactions.Peer ReviewedAquest article té 124 autors/autores J. Moreno-Soto, S. Valenta, E. Berthoumieux, A. Chebboubi, M. Diakaki, W. Dridi, E. Dupont, F. Gunsing, M. Krticka, O. Litaize, O. Serot, O. Aberle, V. Alcayne, S. Amaducci, J. Andrzejewski, L. Audouin, V. Bécares, V. Babiano-Suarez, M. Bacak, M. Barbagallo, Th. Benedikt, S. Bennett, J. Billowes, D. Bosnar, A. Brown, M. Busso, M. Caamaño, L. Caballero-Ontanaya, F. Calviño, M. Calviani, D. Cano-Ott, A. Casanovas, F. Cerutti, E. Chiaveri, N. Colonna, G. Cortés, M. A. Cortés-Giraldo, L. Cosentino, Cristallo, L. A. Damone, P. J. Davies, M. Dietz, C. Domingo-Pardo, R. Dressler, Q. Ducasse, I. Durán, Z. Eleme, B. Fernández-Domínguez, A. Ferrari, P. Finocchiaro, V. Furman, K. Göbel, A. Gawlik-Rami, S. Gilardoni, I. F. Gonçalves, E. González-RomeroC. Guerrero, S. Heinitz, J. Heyse, D. G. Jenkins, A. Junghans, F. Käppeler, Y. Kadi, A. Kimura, I. Knapová, M. Kokkoris, Y. Kopatch, D. Kurtulgil, I. Ladarescu, C. Lampoudis, C. Lederer-Woods, S. J. Lonsdale, D. Macina, A. Manna, T. Martínez, A. Masi, C. Massimi, P. Mastinu, M. Mastromarco, E. A. Maugeri, A. Mazzone, E. Mendoza, A. Mengoni, V. Michalopoulou, P. M. Milazzo, F. MingroneA. Musumarra, A. Negret, R. Nolte, F. Ogállar, A. Oprea, N. Patronis, A. Pavlik, J. Perkowski, L. Piersanti, C. Petrone, E. Pirovano, I. Porras, J. Praena, J. M. Quesada, D. Ramos-Doval, T. Rauscher, R. Reifarth, D. Rochman, M. Sabaté-Gilarte, A. Saxena, P. Schillebeeckx, D. Schumann, A. Sekhar, A. G. Smith, N. V. Sosnin, P. Sprung, A. Stamatopoulos, G. Tagliente, J. L. Tain, A. Tarifeño-Saldivia, L. Tassan-Got, P. Torres-Sánchez, A. Tsinganis, J. Ulrich, S. Urlass, G. Vannini, V. Variale, P. Vaz, A. Ventura, D. Vescovi, V. Vlachoudis, R. Vlastou, A. Wallner, P. J. Woods, T. Wright, P. ŽugecPostprint (published version

Towards a quantitative prediction of the fluxome from the proteome

Item does not contain fulltextThe promise of proteomics and fluxomics is limited by our current inability to integrate these two levels of cellular organization. Here we present the derivation, experimental parameterization, and appraisal of flux functions that enable the quantitative prediction of changes in metabolic fluxes from changes in enzyme levels. We based our derivation on the hypothesis that, in the determination of steady-state flux changes, the direct proportionality between enzyme concentrations and reaction rates is principal, whereas the complexity of enzyme-metabolite interactions is secondary and can be described using an approximate kinetic format. The quality of the agreement between predicted and experimental fluxes in Lactococcus lactis, supports our hypothesis and demonstrates the need and usefulness of approximative descriptions in the study of complex biological systems. Importantly, these flux functions are scalable to genome-wide networks, and thus drastically expand the capabilities of flux prediction for metabolic engineering efforts beyond those conferred by the currently used constraints-based models.1 mei 201

Exploration of alternative optimal flux distributions.

<p>Three alternative flux distributions that maximize the number of reactions whose flux is consistent with their gene expression, for the toy pathway in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002988#pcbi-1002988-g001" target="_blank">Figure 1</a>, are shown in the central column. The panels to the left and right summarize the enzyme modulations that force the MILP solver to find the flux distributions in the central column. The modulations and resulting flux distributions are organized by rows. The leftmost column summarizes the reactions whose inactivation result in the finding of the flux distribution in the central column. Note that there are multiple reactions that when forced to be inactive each give rise to the same flux distribution in the central column. Note furthermore that the flux distribution in the third row cannot be found by the inactivation of a reaction. The rightmost column summarizes the reactions that when forced to carry a flux in the indicated directions, enable finding the flux distributions in the central column. Like with the inactivation of reactions, there are multiple reactions that when forced to carry flux in the indicated direction give rise to the same flux distribution. The flux distribution in the second row of the central column cannot be found by forcing any reaction to be active.</p

EXploration of Alternative Metabolic Optima (EXAMO).

<p>Environment-specific metabolic models are built in a two-step process. First, gene expression measurements are integrated with the original, environment-independent model. The exploration of alternative flux distributions that maximize the agreement score yields two sets of reactions: zero- and high-frequency reactions. Second, zero-frequency reactions are deleted and the Model Building Algorithm <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002988#pcbi.1002988-Jerby1" target="_blank">[26]</a> is used to reduce the network with the constraint that all high-frequency reactions should be able to carry flux.</p

Possible flux distributions in the toy metabolic network.

<p>The toy metabolic network in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002988#pcbi-1002988-g001" target="_blank">Figure 1</a> supports eight different flux distributions, all of which are consistent with the model's stoichiometric and thermodynamic constraints.</p

Predicted tricarboxylic acid cycle fluxes in YPEtOH cultures.

<p>The network of mitochondrial and cytosolic tricarboxylic acid cycle reactions is shown. The thickness of the arrows is drawn in proportion to the predicted fluxes. Acetaldehyde is predicted to be the main energy source and grey arrows indicate an alternative route through which ethanol could be used as energy source. In the cytosol a network of non-TCA reactions (glutamate decarboxylase, 4-aminobutyrate transaminase, succinate semialdehyde dehydrogenase, and aspartate transaminase) that convert oxaloacetate and glutamate into succinate and asparate with concomitant production of and NADPH is shown as a single arrow. Metabolite abbreviations: AcAld (acetaldehyde), Asp (aspartate), Cit (citrate), Fum (fumarate), Glu (glutamate), Glx (glyoxylate), Mal (malate), Oaa (oxaloacetate), Succ (succinate).</p