Premium
Predictive modelling of complex agronomic and biological systems
Author(s) -
KEURENTJES JOOST J. B.,
MOLENAAR JAAP,
ZWAAN BAS J.
Publication year - 2013
Publication title -
plant, cell and environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.646
H-Index - 200
eISSN - 1365-3040
pISSN - 0140-7791
DOI - 10.1111/pce.12156
Subject(s) - computer science , data science , management science , simple (philosophy) , systems biology , reduction (mathematics) , mathematical model , complex system , biochemical engineering , biological network , risk analysis (engineering) , artificial intelligence , biology , mathematics , engineering , computational biology , medicine , philosophy , statistics , geometry , epistemology
Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real‐world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead.