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Embedding Sustainable Development in Organizations Through an Integrated Management Systems Approach

Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs. ; "DeYeastLibrary – Designer yeast strain library optimized for metabolic engineering applications", Ref.ERA-IB-2/0003/2013, funded by national funds through FCT/MCTES, DD-DeCaf and SHIKIFACTORY100, both funded by the European Union through the Horizon 2020 research and innovation programme (grant agreements no. 686070 and 814408). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors acknowledge the use of computing facilities within the scope of the Search-ON2: Revitalization of HPC infrastructure of UMinho" project (NORTE-07-0162-FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 – O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF). VV also thanks funding from FCT/MCTES for the PhD studentship with reference SFRH/BD/118657/2016. ...

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge. ; This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04 469/2020 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 -Programa Operacional Regional do Norte. Fernando Cruz holds a doctoral fellowship (SFRH/BD/139198/2018) funded by the FCT. This study was supported by the European Commission through project SHIKIFACTORY100 -Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408). The submitted manuscript has been created by UChicago Argonne, LLC as Operator of Argonne National Laboratory (`Argonne') under Contract No. DE-AC02-06CH11357 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. ...

Advanced Internet services increasingly rely on many components to implement their functionality. These composite services have three important features: they are expensive to deploy, components need to be placed intelligently close to the users to improve quality of experience and they will potentially consume significant amounts of bandwidth. This paper presents Triptych, a multi-objective optimisation framework that tries to optimise according these three dimensions to help the three main stakeholders in the Internet ecosystem: users, application providers and network providers. Triptych implements evolutionary computation approaches for this complex problem, which simultaneously optimise service deployment costs, latency-based user utility and network congestion. These algorithms provide possible operating points, bringing important tools for network managements and resource allocation. A large set of simulations under different scenarios are provided to validate the algorithms. ; This work has been supported by the US Army Research Laboratory and the UK Ministry of Defence (agreement number W911NF-16-3-0001) and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 761699 (5G-MEDIA). ...

25 páginas, 7 figuras, 2 tablas.-- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited ; Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their posttranslational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM's ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge ; JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Peer reviewed

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems. ; JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ...

Bees generally use different botanical sources of resins for the production of propolis. The elucidation of botanical sources of propolis and identification of the effects of seasonality on the chemical profile of propolis are recognized as two important aspects in the development of a high quality product. Thus, our objective was to elucidate the botanical source and identify the effect of the seasons on the chemical profile of propolis produced in southern Brazil. The chemical profile of the samples was analysed by spectrophotometric and chromatographic techniques and the results were coupled to multivariate analysis. Field observation of the foraging behaviour of Apis mellifera L. revealed its preference for the Baccharis dracunculifolia DC. species. p-Coumaric acid, 3, 4-dicaffeoylquinic acid, 3, 5-dicaffeoylquinic acid, drupanin, and artepillin C which were identified in both plant and propolis samples. Moreover, higher artepillin C amounts have been detected in samples collected in the summer and autumn, while the main compounds of p-coumaric acid and quercetin were available in spring and winter sampled propolis, respectively. Baccharis dracunculifolia has been identified as a plant species preferred by A. mellifera in foraging resin for the production of propolis in southern Brazil. The contents of balsam, total phenolic compounds, and flavonoids varied significantly over the seasons, with values above the minimum required by the legislation, thus assuring a quality pattern for this biomass.

Bees generally use different botanical sources of resins for the production of propolis. The elucidation of botanical sources of propolis and identification of the effects of seasonality on the chemical profile of propolis are recognized as two important aspects in the development of a high quality product. Thus, our objective was to elucidate the botanical source and identify the effect of the seasons on the chemical profile of propolis produced in southern Brazil. The chemical profile of the samples was analysed by spectrophotometric and chromatographic techniques and the results were coupled to multivariate analysis. Field observation of the foraging behaviour of Apis mellifera L. revealed its preference for the Baccharis dracunculifolia DC. species. p-Coumaric acid, 3, 4-dicaffeoylquinic acid, 3, 5-dicaffeoylquinic acid, drupanin, and artepillin C which were identified in both plant and propolis samples. Moreover, higher artepillin C amounts have been detected in samples collected in the summer and autumn, while the main compounds of p-coumaric acid and quercetin were available in spring and winter sampled propolis, respectively. Baccharis dracunculifolia has been identified as a plant species preferred by A. mellifera in foraging resin for the production of propolis in southern Brazil. The contents of balsam, total phenolic compounds, and flavonoids varied significantly over the seasons, with values above the minimum required by the legislation, thus assuring a quality pattern for this biomass. ; The financial support by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) is acknowledged (Process n° 473494/2013-2). This study was partially funded by Project PropMine, funded by the agreement between Portuguese Fundação para a Ciência e a Tecnologia and Brazilian CNPq (Process n° 490383/2013-0). The owners of the Apiário Real® Com-pany are also acknowledged for their logistical support and the supply of propolis samples. The authors also wish to thank the "Financiamento do Plano ...

ABSTRACT: Bees generally use different botanical sources of resins for the production of propolis. The elucidation of botanical sources of propolis and identification of the effects of seasonality on the chemical profile of propolis are recognized as two important aspects in the development of a high quality product. Thus, our objective was to elucidate the botanical source and identify the effect of the seasons on the chemical profile of propolis produced in southern Brazil. The chemical profile of the samples was analysed by spectrophotometric and chromatographic techniques and the results were coupled to multivariate analysis. Field observation of the foraging behaviour of Apis mellifera L. revealed its preference for the Baccharis dracunculifolia DC. species. p-Coumaric acid, 3, 4-dicaffeoylquinic acid, 3, 5-dicaffeoylquinic acid, drupanin, and artepillin C which were identified in both plant and propolis samples. Moreover, higher artepillin C amounts have been detected in samples collected in the summer and autumn, while the main compounds of p-coumaric acid and quercetin were available in spring and winter sampled propolis, respectively. Baccharis dracunculifolia has been identified as a plant species preferred by A. mellifera in foraging resin for the production of propolis in southern Brazil. The contents of balsam, total phenolic compounds, and flavonoids varied significantly over the seasons, with values above the minimum required by the legislation, thus assuring a quality pattern for this biomass.

Marine biodiversity is expressed through the huge variety of vertebrate and invertebrate species inhabiting intertidal to deep-sea environments. The extraordinary variety of â forms and functionsâ  exhibited by marine animals suggests they are a promising source of bioactive molecules and provides potential inspiration for different biomimetic approaches. This diversity is familiar to biologists and has led to intensive investigation of metabolites, polysaccharides, and other compounds. However, marine collagens are less well-known. This review will provide detailed insight into the diversity of collagens present in marine species in terms of their genetics, structure, properties, and physiology. In the last part of the review the focus will be on the most common marine collagen sources and on the latest advances in the development of innovative materials exploiting, or inspired by, marine collagens. ; The authors are grateful for the financial support from European Union, under the scope of European Regional Development Fund((ERDF) through the POCTEP project 0687_NOVOMAR_1_P and Structured Project NORTE-01- 0145-FEDER-000021 and from the Portuguese Foundation for Science and Technology (FCT), under the scope of the BiogenInk project (M-ERA-NET2/0022/2016) and from the European Cooperation in Science & Technology program (EU COST). Grant title: "Stem cells of marine/aquatic inverte brates: from basic research to innovative applications" (MARISTEM). MSR acknowledges FCT for the Ph.D. scholarship ...

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60\% of combinations. However, 20\% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194). ...

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies. ; A.D. and E.G. were Marie-Curie Early Stage Researchers supported by the European Union's Horizon 2020 research and innovation program (675585 Marie-Curie ITN "SymBioSys") to J.S.R. A.D. was funded by German Federal Ministry of Education and Research (Bundesministerium fur Bildung und € Forschung BMBF) MSCoreSys research initiative research core SMART-CARE (031L0212A). This work was further supported by the JRC for Computational Biomedicine which was partially funded by Bayer AG, and the Medical Research Council (MC_UU_12022/6 to C.F. and M.S.). The Novo Nordisk Foundation Center for Protein Research is supported by Novo Nordisk Foundation grant number NNF14CC0001. J.V.O. was funded by a grant from Danish Council for Independent Research (8020-00100B) to partly support K.B.E. who was also supported in part by the Lundbeck Foundation (R193-2015-243). R.K. ...