Autor(es):
Berg, Naomi Iris van den ; Machado, Daniel ; Santos, Sophia Torres ; Rocha, I. ; Chacón, Jeremy ; Harcombe, William ; Mitri, Sara ; Patil, Kiran R.
Data: 2022
Identificador Persistente: https://hdl.handle.net/1822/84436
Origem: RepositóriUM - Universidade do Minho
Projeto/bolsa:
info:eu-repo/grantAgreement/EC/H2020/866028/EU;
info:eu-repo/grantAgreement/EC/H2020/715097/EU;
info:eu-repo/grantAgreement/FCT/POR_NORTE/SFRH%2FBD%2F121695%2F2016/PT;
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04469%2F2020/PT;
Assunto(s): Computational biology and bioinformatics; Ecology; Microbiology; Science & Technology
Descrição
Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties---patterns or functions that cannot be deduced linearly from the properties of the constituent parts---underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka--Volterra, consumer--resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement no. 866028), and from the UK Medical Research Council (project number MC_UU_00025/11). SM would like to thank the Swiss National Science Foundation for funding the NCCR Microbiomes and an Eccellenza project, and the ERC for Starting grant no. 715097. WH received funding from NIH through R01-GM121498, SS received funding from the Portuguese Foundation for Science and Technology (FCT) under the scope of a Ph.D grant (SFRH/BD/121695/2016) and the strategic funding of UIDB/04469/2020 unit.
info:eu-repo/semantics/publishedVersion
