%0 Journal Article
%T A visual analytics approach for models of heterogeneous cell populations
%A Jan Hasenauer
%A Julian Heinrich
%A Malgorzata Doszczak
%A Peter Scheurich
%A Daniel Weiskopf
%A Frank Allg£¿wer
%J EURASIP Journal on Bioinformatics and Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1687-4153-2012-4
%X Cell populations are heterogeneous in terms of, e.g, cell age, cell cycle state, and protein abundance [1,2]. This heterogeneity is ubiquitous, even in clonal population, and influences cell fate decisions [2,3], such as cell death/proliferation [4-7]. Thus, to ultimately understand and control the behavior of populations, the key sources of cell-to-cell variability have to be unraveled. Unfortunately, this is challenging due to experimental constraints. Most experimental systems and measurement devices only allow for the simultaneous assessment of a few cellular properties on a single cell basis. This prohibits the purely experimental analysis of processes which depend on many different cellular properties. Spencer et al. [5] have shown that the experimental limitations can be overcome partially using mathematical models.To mathematically describe heterogeneous populations, agent-based models are used most frequently. Each agent provides a mechanistic description of the signal transduction within individual cells and thus of its behavior. In such a framework, variability can be modeled by either stochastic [8-10] or deterministic [4,5,11] differences among individual cells. The source of the former is the stochasticity of biochemical reactions, while the latter may arise from genetic and epigenetic differences, environmental heterogeneity, or slow dynamic processes (such as the cell cycle).We focus on the deterministic differences among cells ¡ª also called extrinsic factors [12] ¡ª in populations of non-interacting cells. Those differences are commonly modeled by differential parameter values and initial conditions [5,13]. Several methods exist to infer the distribution of parameters and initial conditions from experimental data [13-15] and to obtain quantitative, mechanistic models for cell populations. Unfortunately, the resulting agent-based models are in general highly complex. This complexity prevents the analysis of these models using common tools for dynamica
%U http://bsb.eurasipjournals.com/content/2012/1/4