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Predictive biology: modelling, understanding and harnessing microbial complexity

Partners' Institution
University of Perugia
Reference
LOPATKIN A. J. & COLLINS, J. J. 2020. Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol, 18, 507-520.
Thematic Area
Chemistry/Biology
DOI
10.1038s41579-020-0372-5
Summary
A comprehensive review of predictive and synthetic biology as for 2020. The presentation of the importance of quantitative biology and metabolic circuit logic shows the possibility, yet the difficulties, especially considering that more than 1/3 of the gene functions are still unknown, decades after the full genome sequencing. The aim of the paper is to show the mounting complexity of the systems with a bottom up approach, starting with gene regulation and heading to interspecific relationships in complex microbial communities and their evolution.
Relevance for Complex Systems Knowledge
The paper copes with the problem of non linear systems. It faces the difference between mathematical models and dynamic models, focus on the latter ones. The problem of complex behavior of populations is dissected in its various levels, showing the mounting complexity produced by each successive level.
Point of Strength
It is a good starting point to know the state of the art as for 2020. It is comprehensive. It gives good insights also for general biologists not deeply involved in system biology
Creative Commons License
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