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Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-y, Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality., We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503)., info:eu-repo/semantics/publishedVersion

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories., We would like to acknowledge the support of R. Mattel and F. Stein from the Proteomics Core Facility and the Genomics Core Facility at the European Molecular Biology Laboratory (EMBL Heidelberg, Germany). This study was supported by national funds through FCT/MCTES (Portugal, Ref. ERA-IB-2/0003/2013) and BMBF (Germany, Grant number: 031A343A, Ref. ERA-IB-2/0003/2013). The Portuguese Foundation for Science and Technology (FCT) supported HL through grant ref. PD/BD/52336/2013. FCT also supported this study under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and through the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462). Open Access funding enabled and organized by Projekt DEAL., info:eu-repo/semantics/publishedVersion

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of 13C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function., COMET center acib: Next Generation Bioproduction, which is funded by BMK, BMDW, SFG, Standortagentur Tirol, Government of Lower Austria and Vienna Business Agency in the framework of COMET - Competence Centers for Excellent Technologies. The COMET-Funding Program is managed by the Austrian Research Promotion Agency FFG; D.S., J.S., M.W., M.H., D. E.R. This work has also been supported by the PhD program BioToP of the Austrian Science Fund (FWF Project W1224), info:eu-repo/semantics/publishedVersion

Background: As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins. Results: Here SamPler, a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm’s parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters. Conclusions: The potential of this methodology is demonstrated with four genome functional annotations performed in merlin, an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool., This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UID/BIO/ 04469] unit and COMPETE 2020 [POCI-01-0145-FEDER-006684] 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 thank the project DDDeCaF - Bioinformatics Services for Data-Driven Design of Cell Factories and Communities, Ref. H2020-LEIT-BIO-2015-1 686070–1, funded by the European Commission., info:eu-repo/semantics/publishedVersion

In the last years, Salmonella has been extensively studied not only due to its importance as a pathogen, but also as a host to produce pharmaceutical compounds. However, the full exploitation of Salmonella as a platform for bioproduct delivery has been hampered by the lack of information about its metabolism. Genome-scale metabolic models can be valuable tools to delineate metabolic engineering strategies as long as they closely represent the actual metabolism of the target organism. In the present study, a 13C-MFA approach was applied to map the fluxes at the central carbon pathways of S. typhimurium LT2 growing at glucose-limited chemostat cultures. The experiments were carried out in a 2L bioreactor, using defined medium enriched with 20% 13C-labeled glucose. Metabolic flux distributions in central carbon pathways of S. typhimurium LT2 were estimated using OpenFLUX2 based on the labeling pattern of biomass protein hydrolysates together with biomass composition. The results suggested that pentose phosphate is used to catabolize glucose, with minor fluxes through glycolysis. In silico simulations, using Optflux and pFBA as simulation method, allowed to study the performance of the genome-scale metabolic model. In general, the accuracy of in silico simulations was improved by the superimposition of estimated intracellular fluxes to the existing genome-scale metabolic model, showing a better fitting to the experimental extracellular fluxes, whereas the intracellular fluxes of pentose phosphate and anaplerotic reactions were poorly described.

In the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behavior at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights into other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.

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., info:eu-repo/semantics/publishedVersion

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis., “Fundação para a Ciência e a Tecnologia” (FCT) [Contract PTDC /BII-BIO/28216/2017] and by Programa Operacional Regional de Lisboa 2020 [LISBOA-01-0145-FEDER-022231], through the Biodata.pt Research Infrastructure. Funding received by iBB-Institute for Bioengineering and Biosciences from FCT [Contract UIDB/04565/2020], info:eu-repo/semantics/publishedVersion

O processo de fabricacao de alimentos produz grande quantidade de residuos orgânicos. Uma industria de extracao de aromas, por exemplo, pode gerar, anualmente, 104 e 92 toneladas de residuo de erva-mate e guarana, respectivamente. Uma alternativa para o reaproveitamento desse material lignocelulosico seria utilizalos na producao de biocompositos com micelio fungico atuando como ligante das particulas do substrato. Sendo assim, objetivou-se nesse trabalho a producao de biocompositos a partir de Pleurotus sajor-caju utilizando os residuos de erva-mate e guarana na proporcao (1:1) (m/m), verificando a influencia da fracao de inoculo 10, 20 e 30% e da temperatura de secagem de 40 e 60°C. Para cada condicao testada foi avaliado o tempo de crescimento micelial, o teor de umidade inicial, a velocidade inicial de secagem, a resistencia a compressao, absorcao de umidade do ar e absorcao de agua. A condicao determinada foi a de 10% de inoculo e secagem a 60°C, pois apresentou maior resistencia a compressao (0,094 MPa), menor absorcao de agua (91%), maior velocidade de secagem (27,7 g/dia). Os biocompositos utilizam o crescimento biologico e nao processos de fabricacao com intenso desperdicio de energia, ainda exigem apenas residuos orgânicos de baixo custo como materia-prima, podendo preencher geometrias complexas apresentando potencial para a confeccao de embalagens, pois sao materiais seguros que apresentaram boa resistencia a compressao e baixa absorcao de umidade do ar e de agua. Palavras-chave: Pleurotus sajor-caju, residuos industriais, cultivo solido, biocompositos.

Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines., This work has been supported the Federal Ministry for Digital and Economic Affairs (bmwd), the Federal Ministry for Transport, Innovation and Technology (bmvit), the Styrian Business Promotion Agency SFG, the Standortagentur Tirol, Government of Lower Austria and ZIT - Technology Agency of the City of Vienna through the COMET-Funding Program managed by the Austrian Research Promotion Agency FFG. A.H. has been supported by the Portuguese NORTE-08-5369-FSE-000053 operation. Additional funding came from the PhD program BioToP (Biomolecular Technology of Proteins) of the Austrian Science Fund (FWF Project W1224) and MIT-Portugal PhD program (Bioengineering Systems). The funding agencies had no influence on the conduct of this research. Open Access Funding by the University of Vienna., info:eu-repo/semantics/publishedVersion

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2019.01283/full#supplementary-material, A high-quality GSM model for Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed. The construction of this model involved performing a genome-wide reannotation to identify the metabolic capacity of the bacterium. A reaction representing the abstraction of the biomass composition was reconciled from several studies reported in the literature and previous models, and included in the model. The final model comprises two compartments and manifold automatically generated gene rules. The validation was performed with experimental data from recent studies, regarding the usability of carbon sources, the effect of the presence of oxygen and the requirement of amino acids for growth. This model can be used to better understand the metabolism of this major pathogen, provide clues regarding new drug targets and eventually design strategies for fighting infections by these bacteria., This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 – Programa Operacional Regional doNorte. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreementno.686070., info:eu-repo/semantics/publishedVersion

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., info:eu-repo/semantics/publishedVersion

The uncertain relationship between genotype and phenotype can make strain engineering an arduous trial and error process. To identify promising gene targets faster, constraint-based modelling methodologies are often used, although they remain limited in their predictive power. Even though the search for gene knock-outs is fairly established in constraint-based modelling, most strain design methods still model gene up/down-regulations by forcing the corresponding flux values to fixed levels without taking in consideration the availability of resources. Here, we present a constraint-based algorithm, the Turnover Dependent Phenotypic Simulation (TDPS) that quantitatively simulates phenotypes in a resource conscious manner. Unlike other available algorithms, TDPS does not force flux values and considers resource availability, using metabolite production turnovers as an indicator of metabolite abundance. TDPS can simulate up-regulation of metabolic reactions as well as the introduction of heterologous genes, alongside gene deletion and down-regulation scenarios. TDPS simulations were validated using engineered Saccharomyces cerevisiae strains available in the literature by comparing the simulated and experimental production yields of the target metabolite. For many of the strains evaluated, the experimental production yields were within the simulated intervals and the relative strain performance could be predicted with TDPS. However, the algorithm failed to predict some of the production changes observed experimentally, suggesting that further improvements are necessary. The results also showed that TDPS may be helpful in finding metabolic bottlenecks, but further experiments would be required to confirm these findings., ortuguese Foundation forScience and Technology (FCT) under the scope of thestrategic funding of UID/BIO/04469/2019 unit and Bio-TecNorte operation (NORTE-01-0145-FEDER-000004)funded by the European Regional Development Fund under the scope of Norte2020−Programa Operacional Regional doNorte. This project has received funding from the EuropeanUnion’s Horizon 2020 research and innovation programmeunder Grant Agreement No. 686070 and from the NovoNordisk Foundation. The work of Rui Pereira was supported by a PhD grant from FCT (ref SFRH/BD/51111/2010), info:eu-repo/semantics/publishedVersion

Plants synthesize a variety of different secondary metabolites, such as polyphenols, terpenoids, alkaloids, etc., with pharmaceutical and nutraceutical importance. Polyphenols have shown numerous health benefits with rare side effects. However, the extraction of these compounds from natural sources cannot meet the increasing consumer demand for natural products, and its purification is often difficult, making the overall process too expensive. In contrast, microbial production of polyphenols is a powerful alternative to produce natural products in large amounts, in an environmentally sustainable way. Nevertheless, plant-derived polyphenols are not naturally produced by microorganisms and therefore require the integration of the heterologous pathway from plants through genetic engineering techniques. In the present chapter, the recent advances in microbial production of plant-derived polyphenols, emphasizing on flavonoids, anthocyanins, curcuminoids, and stilbenes, have been summarized. In addition, different strategies used to increase the product yield, and the production processes are also highlighted., We would like to thank the European Union Framework Program 7 “BacHBerry” (www.bachberry.eu), Project No. FP7- 613793 for financial support, the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/ BIO/04469 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), 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., info:eu-repo/semantics/publishedVersion

Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modelling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and limitations are evaluated when designing novel heterologous pathways, by applying these methods on three case studies of synthetic ME related to the production of non-native compounds in E. coli and S. cerevisiae: 1-butanol, curcumin and vanillin. Some targeted improvements are implemented, extending both methods to address limitations identified that impair their scalability, improving their ability to extract potential pathways over large-scale databases. In all case-studies, the algorithms were able to find already described pathways for the production of the target compounds, but also alternative pathways that can represent novel ME solutions after further evaluation., 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 Strategic Project PEst-OE/EQB/LA0023/2013, and also by Project 23060, PEM - Technological Support Platform for Metabolic Engineering, co-funded by FEDER through Portuguese QREN under the scope of the Technological Research and Development Incentive system, North Operational.

Given the great potential impact of the growing number of complete genome-scale metabolic network reconstructions of microorganisms, bioinformatics tools are needed to simplify and accelerate the course of knowledge in this field. One essential component of a genome-scale metabolic model is its biomass equation, whose maximization is one of the most common objective functions used in Flux Balance Analysis formulations. Some components of biomass, such as amino acids and nucleotides, can be estimated from genome information, providing reliable data without the need of performing lab experiments. In this work a java tool is proposed that estimates microbial biomass composition in amino acids and nucleotides, from genome and transcriptomic information, using as input files sequences in FASTA format and files with transcriptomic data in the csv format. This application allows to obtain the results rapidly and is also a user-friendly tool for users with any or little background in informatics (http://darwin.di.uminho.pt/biomass/). The results obtained using this tool are fairly close to experimental data, showing that the estimation of amino acid and nucleotide compositions from genome information and from transcriptomic data is a good alternative when no experimental data is available., The authors thank the project DeYeastLibrary - Designer yeast strain library optimized for metabolic engineering applications, Ref. ERA-IB-2/0003/2013, funded by national funds through FCT/MCTES. This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTec Norte operation (NORTE-01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte.

Given the huge potential impact of the growing number of complete genome-scale metabolic network reconstructions of organisms, it is necessary to use bioinformatics tools to simplify and accelerate the course of knowledge in this field. One essential component of genome-scale metabolic model is biomass equation, whose maximization is one of the most common objective functions used in Flux Balance Analysis formulations. Some components of biomass, such as amino acids and nucleotides, can be estimated from genome information. In this work it is proposed a Java tool that estimates biomass composition in amino acids and nucleotides, from genome and transcriptomic information, using as input files sequences in FASTA format and files with transcriptomic data in csv format. This application allows to obtain the results rapidly and is also a user-friendly tool since facilitates its use by users with any or little background in informatics., Project DeYeastLibrary - Designer yeast strain library optimized for metabolic engineering applications, Ref. ERA-IB-2/0003/2013, funded by national funds through FCT/MCTES

Metabolite profiling of E. coli W3110 and the isogenic ∆relA mutant cells was used to characterize the RelA-dependent stringent control of metabolism under different growth conditions. Metabolic profiles were obtained by gas chromatography–mass spectrometry (GC-MS) analysis and revealed significant differences between E. coli strains grown at different conditions. Major differences between the two strains were assessed in the levels of amino acids and fatty acids and their precursor metabolites, especially when growing at the lower dilution rates, demonstrating differences in their metabolic behavior. Despite the fatty acid biosynthesis being the most affected due to the lack of the RelA activity, other metabolic pathways involving succinate, lactate and threonine were also affected. Overall, metabolite profiles indicate that under nutrient-limiting conditions the RelA-dependent stringent response may be elicited and promotes key changes in the E. coli metabolism., The authors thank to Raphael Aggio for assisting in the automatic refinement and correction of the GC-MS data, Katie Smart for performing acetate analyses and Clark Ehlers for his support with the bioreactor set up. This work was supported by the Portuguese FCT (Fundacao para a Ciencia e Tecnologia) funded MIT-Portugal Program in Bioengineering (MIT-Pt/BS-BB/0082/2008) and 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 the project FCOMP-01-0124-FEDER-009707 (HeliSysBio. molecular Systems Biology in Helicobacter pylori). The work was also supported by a PhD grant from FCT (ref. SFRH/BD/22863/2005).

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Availability: Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. Contact: dmachado@deb.uminho.pt Supplementary information: Supplementary data are available at Bioinformatics online.

a b s t r a c t In metabolic systems, the cellular network of metabolic reactions together with constraints of (ir)reversibility of enzymes determines the space of all possible steady-state phenotypes. Analysis of large metabolic models, however, is not feasible in real-time and identification of a smaller model without loss of accuracy is desirable for model-based bioprocess optimization and control. To this end, we propose two search algorithms for systematic identification of a subset of pathways that match the observed cellular phenotype relevant for a particular process condition. Central carbon metabolism of Escherichia coli was used as a case-study together with three phenotypic datasets obtained from the literature. The first search method is based on ranking pathways and the second is a controlled random search (CRS) algorithm. Since we wish to obtain a biologically realistic subset of pathways, the objective function to be minimized is a trade-off between the error and investment costs. We found that the CRS outperforms the ranking algorithm, as it is less likely to fall into local minima. In addition, we compared two pathway analysis methods (elementary modes versus generating vectors) in terms of modelling accuracy and computational intensity. We conclude that generating vectors have preference over elementary modes to describe a particular phenotype. Overall, the original model containing 433 generating vectors or 2706 elementary modes could be reduced to a system of one to three pathways giving a good correlation with the measured datasets. We consider this work as a first step towards the use of detailed metabolic models to improve real-time optimization, monitoring, and control of biological processes.

In Biomedical research, retrieving documents that match an interesting query is a task performed quite frequently. Typically, the set of obtained results is extensive containing many non-interesting documents and consists in a flat list, i.e., not organized or indexed in any way. This work proposes BioDR, a novel approach that allows the semantic indexing of the results of a query, by identifying relevant terms in the documents. These terms emerge from a process of Named Entity Recognition that annotates occurrences of biological terms (e.g. genes or proteins) in abstracts or full-texts. The system is based on a learning process that builds an Enhanced Instance Retrieval Network (EIRN) from a set of manually classified documents, regarding their relevance to a given problem. The resulting EIRN implements the semantic indexing of documents and terms, allowing for enhanced navigation and visualization tools, as well as the assessment of relevance for new documents., Fundação para a Ciência e a Tecnologia (FCT), Maria Barbeito” contract Xunta, HUELLA financed by the Consellería de Sanidade (Xunta de Galicia de Galicia)

The lack of model-based information in bioreactor monitoring and control can have a profound impact on biological systems. We therefore aim to develop a model using elementary modes (EMs) that represents the observed phenotype in given environmental conditions suited for bioprocess control. Challenges in the model development were the high number of possible phenotypes of stoichiometric models and the high computational intensity. Two methods were compared to reduce the number of EMs to match the observed cellular phenotype. The first method is based on ranking modes and the second is a controlled random search (CRS) algorithm. Since we wish to obtain a biologically realistic subset of EMs, the objective function to be minimized is a trade-off between the error, efficiency of the modes, and model size. The case study considered the central carbon metabolism of Escherichia coli. The original model containing 2706 modes for case 1 and 11718 for case 2 was reduced to a system of one for case 1 and three modes for case 2 giving a good correlation with the measured data. Furthermore, considering also intracellular besides extracellular metabolites, results in a better fit of the measured rates. Finally, the CRS outperformed the ranking algorithm., (undefined)

So far, few large scale kinetic models of metabolic networks have been successfully constructed. The main reasons for this are not only the associated mathematical complexity, but also the large number of unknown kinetic parameters required in the rate equations to define the system. In contrast to kinetic models, the constraint-based modelling approach bypasses these difficulties by using basically only stoichiometric information with certain physicochemical constraints to delimit the solution space without large fitted parameter sets. Although these constraintbased models are highly relevant to predict feasible steady-state fluxes under a diverse range of genetic and environmental conditions, the steady-state assumption may oversimplify cellular behaviour and cannot predict time-course profiles. To overcome these problems, combining these two approaches appears as a reasonable alternative to modelling large-scale metabolic networks. On the other hand, several of the experimental data required for model construction are often rare and in this way it is usually assumed that the enzyme concentrations are constant. In this work, we used a central carbon metabolic network of E. coli to investigate whether including high throughput enzyme concentration data into a kinetic model allows improved predictions of metabolic flux distributions in response to single knockouts perturbations. For this purpose, an E. coli model, based on results obtained from flux balance analysis (FBA) and approximate lin-log kinetics was constructed. The intracellular fluxes distributions, obtained using this model, were compared with published in vivo measurements., (undefined)

The development of monitoring methods for assessing the physiological state of microorganisms during recombinant fermentation processes has been encouraged by the need to evaluate the influence of processing conditions in recombinant protein production. In this work, a technique based on microscopy and image analysis was developed that allows the simultaneous quantification of parameters associated with viability and fluorescent protein production in recombinant Escherichia coli fermentations. Images obtained from light microscopy with phase contrast are used to assess the total number of cells in a given sample and, from epifluorescence microscopy, both protein producing and injured cells are evaluated using two different fluorochromes: propidium iodide and enhanced yellow fluorescent protein. This technique revealed the existence of different cell populations in the recombinant E. coli fermentation broth that were evaluated along four batch fermentations, complementing information obtained with standard techniques to study the effects of the temperature and induction time in recombinant protein production processes., Fundação para a Ciência e a Tecnologia (FCT) - POCI/BIO/60139/2004

The specific growth rate is one of the most important process variables characterizing the state of microorganisms during fermentations mainly because the biosynthesis of many products of interest is often related with the values assumed by this parameter. In the particular case of the fed-batch operation of Escherichia coli for the production of recombinant proteins, it is important to maintain the specific growth rate below a certain threshold in order to avoid the accumulation of acetic acid throughout the fermentation and, additionally, it is often argued that both pre- and the post-induction specific growth rates should be closely controlled in order to achieve maximum productivities of the desired recombinant protein. In a previous work the authors have developed and validated by simulations a strategy for the automatic control of the specific growth rate in E. coli fed-batch fermentations based on an asymptotic observer for biomass and on developed estimators for the specific growth rates. The main purpose of the present work was to implement experimentally the developed observer, estimator and controller in a real fed-batch fermentation process. For that purpose a data acquisition and control program was developed in LabVIEW that allows the acquisition of the necessary on line data (off gas and dissolved oxygen concentration and culture weight) and the calculation of the feeding rates using the developed equations. The feedforward-feedback controller developed was able to keep the culture growing in an exponential phase throughout the fermentation without accumulation of glucose and acetate., Fundação para a Ciência e a Tecnologia (FCT) - RecSysBio projecto POCI/BIO/60139/2004

Systems biology has fostered interest in the use of kinetic models to better understand the dynamic behavior of metabolic networks in a wide variety of conditions. Unfortunately, in most cases, data available in different databases are not sufficient for the development of such models, since a significant part of the relevant information is still scattered in the literature. Thus, it becomes essential to develop specific and powerful text mining tools towards this aim. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The pipeline proposed integrates the development of a novel plug-in over the text mining tool @Note2. Overall, the results validate the developed approach., (undefined)

Live attenuated strains of Salmonella typhimurium have been extensively investigated as vaccines for a number of infectious diseases. However, there is still little information available concerning aspects of their metabolism. S. typhimurium and Escherichia coli show a high degree of similarity in terms of their genome contents and metabolic networks. However, this work presents experimental evidence showing that significant differences exist in their abilities to direct carbon fluxes to biomass and energy production. It is important to study the metabolism of Salmonella in order to elucidate the formation of acetate and other metabolites involved in optimizing the production of biomass, essential for the development of recombinant vaccines. The metabolism of Salmonella under aerobic conditions was assessed using continuous cultures performed at dilution rates ranging from 0.1 to 0.67 h1, with glucose as main substrate. Acetate assimilation and glucose metabolism under anaerobic conditions were also investigated using batch cultures. Chemostat cultivations showed deviation of carbon towards acetate formation, starting at dilution rates above 0.1 h1. This differed from previous findings for E. coli, where acetate accumulation was only detected at dilution rates exceeding 0.4 h1, and was due to the lower rate of acetate assimilation by S. typhimurium under aerobic conditions. Under anaerobic conditions, both microorganisms mainly produced ethanol, acetate, and formate. A genome-scale metabolic model, reconstructed for Salmonella based on an E. coli model, provided a poor description of the mixed fermentation pattern observed during Salmonella cultures, reinforcing the different patterns of carbon utilization exhibited by these closely related bacteria. This article is protected by copyright. All rights reserved., Special thanks to Amadeus Azevedo for the HPLC analyses and technical assistance. The authors acknowledge the national funding received from CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, Brazil), the international cooperation project CAPES-FCT (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior/Brazil-Fundacao para a Ciencia e a Tecnologia/Portugal-Process 315/11), CAPES (Atracao de Jovens Talentos-Process 064922/2014-01) and to Fundacao para a Ciencia e Tecnologia the strategic funding of UID/BIO/04469/2013 unit.

Modeling cellular metabolism is fundamental for many biotechnological applications, including drug discovery and rational cell factory design. Central carbon metabolism (CCM) is particularly important as it provides the energy and precursors for other biological processes. However, the complex regulation of CCM pathways has still not been fully unraveled and recent studies have shown that CCM is mostly regulated at post-transcriptional levels. In order to better understand the role of allosteric regulation in controlling the metabolic phenotype, we expand the reconstruction of CCM in Escherichia coli with allosteric interactions obtained from relevant databases. This model is used to integrate multi-omics datasets and analyze the coordinated changes in enzyme, metabolite, and flux levels between multiple experimental conditions. We observe cases where allosteric interactions have a major contribution to the metabolic flux changes. Inspired by these results, we develop a constraint-based method (arFBA) for simulation of metabolic flux distributions that accounts for allosteric interactions. This method can be used for systematic prediction of potential allosteric regulation under the given experimental conditions based on experimental data. We show that arFBA allows predicting coordinated flux changes that would not be predicted without considering allosteric regulation. The results reveal the importance of key regulatory metabolites, such as fructose-1,6-bisphosphate, in controlling the metabolic flux. Accounting for allosteric interactions in metabolic reconstructions reveals a hidden topology in metabolic networks, improving our understanding of cellular metabolism and fostering the development of novel simulation methods that account for this type of regulation., (undefined)

BACKGROUND:Ashbya gossypii is an industrially relevant microorganism traditionally used for riboflavin production. Despite the high gene homology and gene order conservation comparatively with Saccharomyces cerevisiae, it presents a lower level of genomic complexity. Its type of growth, placing it among filamentous fungi, questions how close it really is from the budding yeast, namely in terms of metabolism, therefore raising the need for an extensive and thorough study of its entire metabolism. This work reports the first manual enzymatic genome-wide re-annotation of A. gossypii as well as the first annotation of membrane transport proteins.RESULTS:After applying a developed enzymatic re-annotation pipeline, 847 genes were assigned with metabolic functions. Comparatively to KEGG's annotation, these data corrected the function for 14% of the common genes and increased the information for 52 genes, either completing existing partial EC numbers or adding new ones. Furthermore, 22 unreported enzymatic functions were found, corresponding to a significant increase in the knowledge of the metabolism of this organism. The information retrieved from the metabolic re-annotation and transport annotation was used for a comprehensive analysis of A. gossypii's metabolism in comparison to the one of S. cerevisiae (post-WGD - whole genome duplication) and Kluyveromyces lactis (pre-WGD), suggesting some relevant differences in several parts of their metabolism, with the majority being found for the metabolism of purines, pyrimidines, nitrogen and lipids. A considerable number of enzymes were found exclusively in A. gossypii comparatively with K. lactis (90) and S. cerevisiae (13). In a similar way, 176 and 123 enzymatic functions were absent on A. gossypii comparatively to K. lactis and S. cerevisiae, respectively, confirming some of the well-known phenotypes of this organism.CONCLUSIONS:This high quality metabolic re-annotation, together with the first membrane transporters annotation and the metabolic comparative analysis, represents a new important tool for the study and better understanding of A. gossypii's metabolism., Research described in this article was financially supported by FEDER and "Fundacao para a Ciencia e a Tecnologia" (FCT): Project AshByofactory (PTDC/EBB-EBI/101985/2008 - FCOMP-01-0124-FEDER-009701), Strategic Project PEst-OE/EQB/LA0023/2013, Project "BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF.NORTE-07-0124-FEDER-000028" Co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER and the PhD grant to DG (SFRH/BD/88623/2012).

Curcumin is a natural compound obtained from turmeric, and is well known for its pharmacological effects. In this work, we design a heterologous pathway for industrial production of curcumin in Escherichia coli. A kinetic model of the pathway is then developed and connected to a kinetic model of the central carbon metabolism of E. coli. This model is used for optimization of the mutant strain through a rational design approach, and two manipulation targets are identified for overexpression. Dynamic simulations are then performed to compare the curcumin production profiles of the different mutant strains. Our results show that it is possible to obtain a significant improvement in the curcumin production rates with the proposed mutants. The kinetic model here developed can be an important framework to optimize curcumin production at an industrial scale and add value to its biomedical potential., Research supported by the Portuguese Foundation for Science and Technology (FCT), through the project "SYNBIOBACTHER - Synthetic biology approaches to engineer therapeutic bacteria" (PTDC/EBB-BIO/102863/2008).

The design of cell factories for the production of compounds involves the search for suitable heterologous pathways. Different strategies have been proposed to infer such pathways, but most are optimization approaches with specific objective functions, not suited to enumerate multiple pathways. In this work, we analyze two pathway enumeration algorithms based on graph representations: the Solution Structure Generation and the Find Path algorithms. Both are capable of enumerating exhaustively multiple pathways using network topology. We study their capabilities and limitations when designing novel heterologous pathways, by applying these methods on two case studies of synthetic metabolic engineering related to the production of butanol and vanillin.

Background Flux analysis methods lie at the core of Metabolic Engineering (ME), providing methods for phenotype simulation that allow the determination of flux distributions under different conditions. Although many constraint-based modeling software tools have been developed and published, none provides a free user-friendly application that makes available the full portfolio of flux analysis methods. Results This work presents Constraint-based Flux Analysis (CBFA), an open-source software application for flux analysis in metabolic models that implements several methods for phenotype prediction, allowing users to define constraints associated with measured fluxes and/or flux ratios, together with environmental conditions (e.g. media) and reaction/gene knockouts. CBFA identifies the set of applicable methods based on the constraints defined from user inputs, encompassing algebraic and constraint-based simulation methods. The integration of CBFA within the OptFlux framework for ME enables the utilization of different model formats and standards and the integration with complementary methods for phenotype simulation and visualization of results. Conclusions A general-purpose and flexible application is proposed that is independent of the origin of the constraints defined for a given simulation. The aim is to provide a simple to use software tool focused on the application of several flux prediction methods., 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 project ref. COMPETE FCOMP-01-0124-FEDER-015079. RC's work is funded by a Ph.D. grant from the Portuguese FCT (ref. SFRH/BD/66201/2009).The authors would also like to thank the FCT Strategic Project PEst-OE/EQB/LA0023/2013 and the Projects "BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes", REF. NORTE-07-0124-FEDER-000028 and "PEM - Metabolic Engineering Platform", project number 23060, both co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER.

In silico cell simulation approaches based in the use of genome-scale metabolic models (GSMMs) and constraint-based methods such as Flux Balance Analysis are gaining importance, but methods to integrate these approaches with omics data are still greatly needed. In this work, the focus relies on fluxomics data that provide valuable information on the intracellular fluxes, although in many cases in an indirect, incomplete and noisy way. The proposed framework enables the integration of fluxomics data, in the form of 13C labeling distribution for metabolite fragments, with GSMMs enriched with carbon atom transition maps. The algorithms implemented allow to infer labeling distributions for fragments/metabolites not measured and to build expressions for the relevant flux ratios that can be then used to enrich constraint-based methods for flux determination. This approach does not require any assumptions on the metabolic network and reaction reversibility, allowing to compute ratios originating from coupled joint points of the network. Also, when enough data do not exist, the system tries to infer ratio bounds from the measurements.

The identification of genetic modifications leading to mutant strains able to overproduce compounds of industrial interest is a challenging task in Metabolic Engineering (ME). Several methods have been proposed but, to some extent, none of them is suitable for all the specificities of each particular strain optimization problem. This work proposes an integrated framework that allows its users to configure and fine tune all the various steps involved in a strain optimization strategy, including the loading of models in distinct formats, the definition of a suitable phenotype simulation method and the choice and configuration of the strain optimization engine. Moreover, it is designed to suit the needs of users skilled at programming, as well as less advanced users. The framework includes a GUI implemented as the strain optimization plug-in for the OptFlux workbench (version 3), a reference platform for ME (http://www.optflux.org). All the code is distributed under the GPLv3 licence and it is fully available (http://sourceforge.net/projects/optflux/)., This 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 PTDC/EBB-EBI/104235/2008. This work is also funded by National Funds through the FCT within project PEst-OE/EEI/UI0752/2011. The work of PM was supported by the FCT through the Ph.D. grant SFRH/BD/61465/2009.

Helicobacter pylori is a pathogenic bacterium that colonizes the human epithelia, causing duodenal and gastric ulcers as well as gastric cancer. The genome of H. pylori 26695 has been sequenced and annotated. In addition, two genome-scale metabolic models have been developed. In order to maintain accurate and relevant information on coding sequences (CDS) and to retrieve new information, the assignment of new functions to Helicobacter pylori 26695’s genes was performed. The use of software tools, on-line databases and an annotation pipeline for inspecting each gene allowed the attribution of validated E.C. numbers to metabolic genes, and the assignment of 177 new functions to the CDS of this bacterium. This information provides relevant biological information for the scientific community dealing with this organism and can be used as the basis for a new metabolic model reconstruction., (undefined)

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency., This work was supported by the Portuguese Science Foundation [grant numbers MIT-Pt/BS-BB/0082/2008, SFRH/BPD/44180/2008 to ZS] (http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Escherichia coli has been widely used for the production of recombinant proteins. However, the unbalances between host metabolism and recombinant biosynthesis continue to hamper the efficiency of these recombinant bioprocesses. The additional drainage of biosynthetic precursors toward recombinant processes burdens severely the metabolism of cells that, ultimately, elicits a series of stress responses, reducing biomass growth and recombinant protein production. Several strategies to overcome these metabolic limitations have been implemented; however, in most cases, improvements in recombinant protein expression were achieved at the expense of biomass growth arrest, which significantly hampers the efficiency of recombinant bioprocesses. With the advent of high throughput techniques and modelling approaches that provide a system-level understanding of the cellular systems, it is now expected that new advances in recombinant bioprocesses are achieved. By providing means to deal with these systems, our understanding on the metabolic behaviour of recombinant cells will advance and can be further explored to the design of suitable hosts and more efficient and cost-effective bioprocesses. Here, we review the major metabolic responses associated with recombinant processes and the engineering strategies relevant to overcome these stresses. Moreover, the advantages of applying systems levels engineering strategies to enhance recombinant protein production in E. coli cells are discussed and future perspectives on the advances of mathematical modelling approaches to study these systems are exposed., This work was partially supported by the MIT-Portugal Program in Bioengineering (MIT-Pt/BS-BB/0082/2008), the research project HeliSysBio-Molecular Systems Biology Helicobacter pylori (FCT PTDC/EBB-EBI/104235/2008) and a PhD grant from Portuguese FCT (Fundacao para a Ciencia e Tecnologia) (SFRH/BD/22863/2005).

Helicobacter pylori is a pathogenic bacterium that colonizes the human epithelia, causing duodenal and gastriculcers, and gastric cancer. The genome of H. pylori 26695 has been previously sequenced and annotated. In addition, two genome-scale metabolic models have been developed. In order to maintain accurate and relevant information on coding sequences (CDS) and to retrieve new information, the assignment of new functions to Helicobacter pylori 26695s genes was performed in this work. The use of software tools, on-line databases and an annotation pipeline for inspecting each gene allowed the attribution of validated EC numbers and TC numbers to metabolic genes encoding enzymes and transport proteins, respectively. 1212 genes encoding proteins were identified in this annotation, being 712 metabolic genes and 500 non-metabolic, while 191 new functions were assignment to the CDS of this bacterium. This information provides relevant biological information for the scientific community dealing with this organism and can be used as the basis for a new metabolic model reconstruction., This work was supported by the project FCOMP-01-0124-FEDER-009707, entitled HeliSysBio molecular Systems Biology in Helicobacter pylori (Ref.: FCT PTDC/EBB-EBI/104235/2008). Daniela Correia is grateful for financial support from the FCT (PhD grant: SFRH/BD/47596/2008).

The productivity of an industrial fermentation process involving a filamentous microbe is heavily dependent on the morphological form adopted by the organism. The development of systems capable of rapidly and accurately characterizing morphology within a given process represents a significant challenge, as the complex phenotypes that are manifested are not easily quantified. Conventional parameters employed in these analyses are often of limited value, as they reveal little about the branching behavior of the organism; an important consideration given the demonstrated link between branching frequency and metabolite production. In this study, the influence of branching behavior on the spatial distribution of mycelia grown in silico is examined through fractal analysis. It is demonstrated that fractal dimension, quantified based on the frequency distribution of parameterized boundary curves, and lacunarity act as robust estimators of branching behavior. The analysis can, in theory, be applied to any morphological form, providing universally applicable process parameters for more complete data acquisition., This work was supported by a grant from the Spanish programme of Chemical Processes Technologies (CTQ2007-60347; CTQ2010-15131), HP2007-0045 from Science and Innovation Ministry, 2009-SGR-00281 and the Reference Network in Biotechnology (XRB) from the DURSI (Generalitat de Catalunya). Support from "Luso-Spanish Integrated Actions" (Action 100/08) are also acknowledged. JMB would like to thank the Spanish Education Ministry for his predoctoral fellowship.

Metabolic engineering (ME) efforts have been recently boosted by the increase in the number of annotated genomes and by the development of several genome-scale metabolic models for microbes of interest in industrial biotechnology. Based on these efforts, strain optimization methods have been proposed to reach the best set of genetic changes to apply to selected host microbes, in order to create strains that are able to overproduce metabolites of industrial interest. Previous work in strain optimization has been mostly based in finding sets of gene (or reaction) deletions that lead to desired phenotypes in computational simulations. In this work, we focus on enlarging the set of possible genetic changes, considering gene over and underexpression. A gene is considered under (over) expressed if its expression value is constrained to be significantly lower (higher) than the one in the wild-type strain, used as a reference. A method is proposed to propagate relative gene expression values to flux constraints over related reactions, making use of the available transcriptional/translational information. The algorithms chosen for the optimization tasks are metaheuristics such as Evolutionary Algorithms (EA) and Simulated Annealing (SA), based on previous successful work on gene knockout optimization. These methods were modified appropriately to accommodate the novel optimization tasks and were applied to study the optimization of succinic and lactic acid production using Escherichia coli as the host. The results are compared with previous ones obtained in gene knockout optimization, thus showing the usefulness of the approach. The methods proposed in this work were implemented in a novel plug-in for OptFlux, an open-source software framework for ME. Supplementary Material is available at www.liebertonline.com/cmb.

Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of Escherichia coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods., Fundacao para a Ciencia e a Tecnologia (PhD Grant SFRH/BD/35215/2007), Fundacao para a Ciencia e a Tecnologia (PhD Grant SFRH/BD/25506/2005), MIT-Portugal Program (MIT-Pt/BS-BB/0082/2008)

Elementary Flux Modes (EFMs) have been claimed as one of the most promising approaches for pathway analysis. These are a set of vectors that emerge from the stoichiometric matrix of a biochemical network through the use of convex analysis. The computation of all EFMs of a given network is an NP–hard problem and existing algorithms do not scale well. Moreover, the analysis of results is difficult given the thousands or millions of possible modes generated. In this work, we propose a new plug–in, running on top of the OptFlux Metabolic Engineering workbench (Rocha et al., 2010), whose aims are to ease the analysis of these results and explore synergies among EFM analysis, phenotype simulation and strain optimisation. Two case studies are shown to illustrate the capabilities of the proposed tool., The authors wish to thank the financial support of the Portuguese FCT for the PhD grant SFRH/BD/61465/2009 and the project MIT-PT/BS-BB/0082/2008. Also, thanks to the company Dupont under the scope of the Dupont European University Support Program Award.

Even before having its genome sequence published in 2004, Kluyveromyces lactis had long been considered a model organism for studies in genetics and physiology. Research on Kluyveromyces lactis is quite advanced and this yeast species is one of the few with which it is possible to perform formal genetic analysis. Nevertheless, until now, no complete metabolic functional annotation has been performed to the proteins encoded in the Kluyveromyces lactis genome. Results In this work, a new metabolic genome-wide functional re-annotation of the proteins encoded in the Kluyveromyces lactis genome was performed, resulting in the annotation of 1759 genes with metabolic functions, and the development of a methodology supported by merlin (software developed in-house). The new annotation includes novelties, such as the assignment of transporter superfamily numbers to genes identified as transporter proteins. Thus, the genes annotated with metabolic functions could be exclusively enzymatic (1410 genes), transporter proteins encoding genes (301 genes) or have both metabolic activities (48 genes). The new annotation produced by this work largely surpassed the Kluyveromyces lactis currently available annotations. A comparison with KEGG's annotation revealed a match with 844 (~90%) of the genes annotated by KEGG, while adding 850 new gene annotations. Moreover, there are 32 genes with annotations different from KEGG. Conclusions The methodology developed throughout this work can be used to re-annotate any yeast or, with a little tweak of the reference organism, the proteins encoded in any sequenced genome. The new annotation provided by this study offers basic knowledge which might be useful for the scientific community working on this model yeast, because new functions have been identified for the so-called metabolic genes. Furthermore, it served as the basis for the reconstruction of a compartmentalized, genome-scale metabolic model of Kluyveromyces lactis, which is currently being finished., This work was partially supported by the MIT-Portugal Program in Bioengineering (MIT-Pt/BS-BB/0082/2008) and a PhD grant (SFRH / BD / 47307 / 2008) from Portuguese FCT (Fundacao para a Ciencia e Tecnologia).

In this work, a plug-in for the OptFlux Metabolic Engineering platform is presented, implementing methods that allow the identification of sets of genes to over/under express, relatively to their wild type levels. The optimization methods used are Simulated Annealing and Evolutionary Algorithms, working with a novel representation and operators. This overcomes the limitations of previous approaches based solely on gene knockouts, bringing new avenues for Biotechnology, fostering the discovery of genetic manipulations able to increase the production of certain compounds using a host microbe. The plug-in is made freely available together with appropriate documentation., Support of FCT and Programa COMPETE (ref. PTDC/EIA-EIA/ 115176/2009).

Fructooligosaccharides are prebiotics with numerous health benefits within which the improvement of gut microbiota balance can be highlighted, playing a key role in individual health. In this study, an integrated one-stage method for FOS production via sucrose fermentation by Aureobasidium pullulans was developed and optimized using experimental design tools. Optimization of temperature and agitation speed for maximizing the FOS production was performed using response surface methodology. Temperature was found to be the most significant parameter. The optimum fermentation conditions were found to be 32 ◦C and 385 rpm. Under these conditions, the model predicted a total FOS production yield of 64.7 gFOS/gsucrose. The model was validated at optimal conditions in order to check its adequacy and accuracy and an experimental yield of 64.1 (±0.0) gFOS/gsucrose was obtained. A significant improvement of the total FOS production yields by A. pullulans using a one-stage process was obtained, (undefined)

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future., Fundação para a Ciência e a Tecnologia (SFRH/BD/35215/2007), Fundação para a Ciência e a Tecnologia (SFRH/BD/25506/2005), MIT-Portugal Program (project "Bridging Systems and Synthetic Biology for the development of improved microbial cell factories" (MIT-Pt/BS-BB/0082/2008))

Detailed kinetic models at the network reaction level are usually constructed using enzymatic mechanistic rate equations and the associated kinetic parameters. However, during the cellular life cycle thousands of different reactions occur, which makes it very difficult to build a detailed large-scale ldnetic model. In this work, we provide a critical overview of specific limitations found during the reconstruction of the central carbon metabolism dynamic model from E. coli (based on kinetic data available). In addition, we provide clues that will hopefully allow the systems biology community to more accurately construct metabolic dynamic models in the future. The difficulties faced during the construction of dynamic models are due not only to the lack of kinetic information but also to the fact that some data are still not curated. We hope that in the future, with the standardization of the in vitro enzyme protocols the approximation of in vitro conditions to the in vivo ones, it will be possible to integrate the available kinetic data into a complete large scale model. We also expect that collaborative projects between modellers and biologists will provide valuable kinetic data and permit the exchange of important information to solve most of these issues., Rafael S. Costa would like to thank Fundacao para a Ciencia e Tecnologia for providing the grant SFRH/BD/25506/2005. The authors also acknowledge the MIT-Portugal project 'Bridging Systems and Synthetic Biology for the development of improved microbial cell factories' MIT-Pt/BS-BB/0082/2008.

Background and scope Recently, a number of methods and tools have been proposed to allow the use of genome-scale metabolic models for the phenotype simulation and optimization of microbial strains, within the field of Metabolic Engineering (ME). One of the limitations of most of these algorithms and tools is the fact that only metabolic information is taken into account, disregarding knowledge on regulatory events. Implementation and performances This work proposes a novel software tool that implements methods for the simulation and optimization of microbial strains using integrated models, encompassing both metabolic and regulatory information. This tool is developed as a plug-in that runs over OptFlux, a computational platform that aims to be a reference tool for the ME community. Availability The plug-in is made available in the OptFlux web site (www.optflux.org) together with examples and documentation., Portuguese FCT (MIT-Portugal Program) - project MIT-PT/BS-BB/0082/2008.

The field of Metabolic Engineering has been growing, sup- ported by the increase in the number of annotated genomes and genome- scale metabolic models. In silico strain optimization methods allow to create mutant strains able to overproduce certain metabolites of interest in Biotechnology. Thus, it is possible to reach (near-) optimal solutions, i.e. strains that provide the desired phenotype in computational pheno- type simulations. However, the validation of the results involves under- standing the strategies followed by these mutant strains to achieve the desired phenotype, studying the different use of reactions/ pathways by the mutants. This is quite complex given the size of the networks and the interactions between (sometimes distant) components. The manual verification and comparison of phenotypes is typically impossible. Here, automatic methods are proposed to analyse large sets of mutant strains, by taking the phenotypes of a large number of possible solutions and identifying shared patterns, using methods from network topology analysis. The topological comparison between the networks provided by the wild type and mutant strains highlights the major changes that lead to successful mutants. The methods are applied to a case study consider- ing E. coli and aiming at the production of succinate, optimizing the set of gene knockouts to apply to the wild type. Solutions provided by the use of Simulated Annealing and Evolutionary Algorithms are analyzed. The results show that these methods can help in the identification of the strategies leading to the overproduction of succinate., This work is supported by project PTDC/EIA-EIA/115176/2009, funded by Portuguese FCT and Programa COMPETE., José Pedro Pinto work is funded by a PhD grant from the Portuguese FCT (ref. SFRH/BD/41763/2007).

Many regulatory processes in the cell are based on the control of gene expression through the interaction of transcription factors. However, enzymatic regulation often overlays transcriptional regulation and even, in some metabolic pathways, enzymatic regulation prevails. The present study addresses the regulatory network of Escherichia coli and offers a global view of the regulation of its metabolic pathways. It identifies the regulatory mechanisms responsible for key metabolic activities and details the structures behind such mechanisms. This knowledge is considered of relevance to further studies on the bacteria’s system and its industrial application, namely for understanding the signal cascades comprised in the responses to various environmental stresses., FCT funded MIT-Portugal Program in Bioengineering (MIT-Pt/BSBB/ 0082/2008); PhD grants from FCT (ref. SFRH/BD/22863/2005 and SFRH/BD/41763/2007).