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Predictive approaches in biology and ecology

Updated on 01/23/2017
Published on 01/05/2017

The digital transition, combined with systems biology, must contribute to enriching the life sciences with more quantitative and predictive approaches. Our observational and experimental capabilities, from the molecular level to that of living organisms, populations and communities, are developing at different paces. Digital technologies are playing a crucial role in the collection, organisation, processing and exploitation of data and knowledge. This context offers a natural framework for the development of formal representations, the modelling of systems and their exploration through simulation; these in turn are posing new questions to biology, and triggering further experimentation. 

These research issues can be divided into three main areas:

Systems biology, or the integration of mechanisms from the molecular scale to that of an organism in its biotic and abiotic environment:

  • The formal representation of living systems at different scales, to enable the management, integration and exploitation of data, information and knowledge,
  • Multi-scale modelling for the analysis, simulation or prediction of living systems.

The dynamics of populations and communities at the scale of an agro-ecosystem and its impact in terms of performance, ecosystem services and sustainability:

  • Formal, dynamic and multi-scale representations of the entities and agents that structure agro-ecosystems in a landscape or exchange network, 
  • The modelling of biotic and abiotic interactions in space, coupling population dynamics and genetics, including evolutionary processes, and quantification of the uncertainties and variability of this modelling.

The exploitation of big data in certain fields where the immediate aim is not so much to understand underlying mechanisms – which may be the subject of other studies – but the exploitation of big data. For example, in genomic selection, correlations between genotypes and phenotypes in different environments mean it is possible to obtain predictions that are efficient and operational in the short term which can then be used to optimise more detailed knowledge of the mechanisms in play. 

As well as cognitive challenges, these issues also concern innovation, particularly in synthetic biology, genetic improvement and the design of food systems.
Systems biology, from the gene to the agro-ecosystem and in all living kingdoms, requires the crossover of both formal disciplines (notably mathematics and computer sciences) and observational disciplines (life and physical sciences).

The organisation of associated data and information will generate a corpus of knowledge and methods where the formal, observational and experimental sciences will be mutually enhanced. In the same way as the information systems designed in a technology sector, this will require the development of integrative platforms so as to best exploit these new capacities to investigate living organisms and, in return, underpin the hypotheses that guide these investigations.