Hierarchical mixed effects modelling of dynamical systems

Main objectives Biological dynamic systems are heterogeneous and hierarchal. Individual cells may respond or behave differently as do individual human subjects. Studying population averages therefore does not provide information on individual behaviour, neither does the study of individuals provide sufficient information about specific group behaviour such as common disease mechanisms. Our aim is to develop models that not only capture heterogeneity but also harness the benefits of variation when identifying model parameters. Condensed work plan We will study three applications in medicine and biology; Proliferation and migration dynamics in brain tumour cells; Human lipid metabolism in metabolic diseases; Regulation of glucose metabolism in yeast. Three research aims which are applicable to each of the applications have been identified: mixed effects modelling of dynamical systems, extension of homogenisation techniques and inclusion of uncertainties using stochastic differential equations. The fourth aim is to implement the developed methods into a demonstrator software, used for solving typical problems that arise in the applications. Expected results The selected applications provide a range of data and models which guarantees that the developed methods are broadly applicable. However, the selection of real research applications, in which the consortium members and close collaborators are already world leading, guarantees a fast translation of results into practice.


Bernt Wennberg (contact)

Professor at Mathematical Sciences

Martin Adiels

Forskarassistent at Mathematical Sciences, Mathematics

Philip Gerlee

at Mathematical Sciences, Mathematics

Torbjörn Lundh

at Mathematical Sciences, Mathematics


Fraunhofer-Chalmers Centre


Sahlgrenska University Hospital

Gothenburg, Sweden


Swedish Foundation for Strategic Research (SSF)

Funding years 2014–2019

Related Areas of Advance and Infrastructure

Life Science Engineering

Area of Advance

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