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Karolinska Institute (Department of Biosciences and Nutrition)
Stockholm University (Mathematical institution)

Statistical spatiotemporal hierarchical modeling of cell adhesion and migration on systems and sub-systems level
2009-2013

Metastasis is the primary cause of mortality for most cancer types and represents a major therapeutic challenge in oncology. As a matter of fact, approximately 90% of all cancer deaths arise from the metastatic spread of primary tumors. Understanding this complex pathological process in its entirety is contingent upon elucidating the cellular systems underlying it. Notably, metastasis occurs as a direct result of changes in cell adhesion and cell migration, and as such, the mechanisms regulating these complex cellular behaviors are of great importance.
Cell adhesion and migration are emergent processes that ultimately derive from a diverse network of molecular interactions. These interaction networks increase in complexity in a hierarchical manner to produce recognizable systems of intermediate complexity (such as those controlling actin polymerization, cytoskeletal tension, and integrin-mediated adhesion complex turnover). Likewise, these intermediate systems interact to eventually produce adhesive and migratory cellular behaviors, which themselves are components of even more complex biological phenomena such as cancer metastasis, tissue remodeling, development, and inflammation (Lock et al 2008). The complex, hierarchical nature of cell adhesion and migration necessitates characterization at multiple mechanistic/resolution levels, from the molecular to the systems level, in order for a complete understanding to be achieved.
Given also that cell migration is a highly dynamic process, where precisely controlled events in space and time are critical, the understanding of cell migration demands comprehensive analysis of these spatiotemporal events. However, until now there have been no methodologies capable of such a comprehensive analyses. Excitingly, recent developments in quantitative live cell microscopy methodology in the host laboratory as well as in other laboratories can now facilitate quantitative, multi-parametric analyses of cellular events based on live cell imaging. This is an important part of the emerging research field of Systems Microscopy, where the use of multi-parametric quantitative live cell imaging will allow a better understanding of dynamic cellular processes (Muzzey & van Oudenaarden 2009; Verveer & Bastiaens 2008; Pepperkok & Ellenberg 2006).

The adhesive interactions between cells and their extra-cellular matrix (ECM) environment, mediated by proteins of the integrin family, undergo constant dynamic regulation and remodeling and are vital components of cellular processes such as cell adhesion, spreading, survival, invasion, differentiation, proliferation and migration (Danen 2005; Lock et al 2008). This dynamic regulation is heavily dependent on the cellular sub-systems most directly involved in cell adhesion and migration: cell-matrix adhesion complexes (CMACs) and filamentous actin. CMACs are composed of clustered, ECM-bound integrins associated with a diverse intra-cellular network of adaptor proteins. CMACs constitute the core regulatory machinery of cell adhesion and migration and also coordinate regulation of the actin microfilament system (Berrier & Yamada 2007; Lock et al 2008; Broussard et al 2008). Therefore, the parameters extracted by our methodology focuses on dynamic whole cell parameters, such as cell size, shape, migration speed, directional persistence and trajectories, as well as the dynamics of the core migratory machineries of CMACs and the actin filament system.


Karolinska Institute (Department of Neurosciences)
Stockholm University (Mathematical institution)

Spatial point pattern analysis of pyramidal neurons
2008-2010

Master's Thesis (2008)
Publication (2010)

The distribution of neurons can be described as a spatial point pattern, a multi- dimensional stochastic process which can be analyzed with mathematical methods. With the development of new techniques it is now possible to acquire large datasets of the 3-dimensional distribution of specifically labeled cells. Recent efforts in the development of high-resolution 3D digital atlases require the development of analytical tools for the analysis of the data. The digital atlases provide remarkable tools for visualization, but the fundamental advantage of the digital era in anatomy is the use of mathematical tools to analyze large datasets. One such tool is Ripley's K-function which has been extensively employed in 2D analysis. This project extends the usage of this method to 3D domains using a an adequate edge-correction term.

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Contact: mjm@math.su.se __ Tel: +46 8 16 4507