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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. ...

Microalgae (including cyanobacteria) are the basis for an emerging worldwide industry but still face significant bottlenecks in contributing to the global economy. It is an enormous challenge to translate experiences from established industries such as aquaculture and agriculture to the microalgae sector. In particular, this includes the challenge of adapting regulations that apply to such macroscopic production and mindsets, to the microscopic world of microalgae and to the scale-up to a million times smaller. Current European and country-based regulations do not always, indeed rarely, consider relevant specific issues that limit the path for innovation and growth applicable to the microalgae sector. In this work, the boundaries for the main issues impacting this sector are presented and discussed. Examples and possible analytical frameworks are presented in a question and answer format. Relevant key topics and related boundaries are discussed: What are algae and how do microalgae differ from macroalgae? Why are algae and specifically microalgae relevant? Is algae cultivation an aquaculture process? Can algae and specifically microalgae be classified as vegetables and their production be classified as agriculture or are they an industrial process? How is algaculture compared with other agricultural sectors? What are organic algae? Can microalgae be grown in wastewater and how can they be used? What are toxic algae? What are the bottlenecks for microalgae culture scale-up? How does the microalgae biodiversity contribute to their development? We conclude that microalgae are developing as a novel agricultural enterprise that can provide major benefits to a sustainable circular economy and environment but require appropriate regulations and support from governments and businesses, recognising its unique attributes and potential.

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. ...