Diffusion-weighted magnetic resonance imaging (DWI) is a key non-invasive imaging technique for cancer diagnosis and tumor treatment assessment, reflecting Brownian movement of water molecules in tissues. Since densely packed cells restrict molecule mobility, tumor tissues produce usually higher signal (a.k.a. less attenuated signal) on isotropic maps compared with normal tissues. However, no general quantitative relation between DWI data and the cell density has been established. In order to link low-resolution clinical cross-sectional data with high-resolution histological information, we developed an image processing and analysis chain, which was used to study the correlation between the diffusion coefficient (D value) estimated from DWI and tumor cellularity from serial histological slides of a resected non-small cell lung cancer tumor. Color deconvolution followed by cell nuclei segmentation was performed on digitized histological images to determine local and cell-type specific 2d (two-dimensional) densities. From these, the 3d cell density was inferred by a model-based sampling technique, which is necessary for the calculation of local and global 3d tumor cell count. Next, DWI sequence information was overlaid with high-resolution CT data and the resected histology using prominent anatomical hallmarks for co-registration of histology tissue blocks and non-invasive imaging modalities' data. The integration of cell numbers information and DWI data derived from different tumor areas revealed a clear negative correlation between cell density and D value. Importantly, spatial tumor cell density can be calculated based on DWI data. In summary, our results demonstrate that tumor cell count and heterogeneity can be predicted from DWI data, which may open new opportunities for personalized diagnosis and therapy optimization.

MOTIVATION: Biological reality can in silico only be comprehensively represented in multi-scaled models. To this end, cell behavioural models addressing the multi-cellular level have to be semantically linked with mechanistic molecular models. These requirements have to be met by flexible software workflows solving the issues of different time scales, inter-model variable referencing and flexible sub-model embedding. RESULTS: We developed a novel software workflow (EPISIM) for the semantic integration of Systems Biology Markup Language (SBML)-based quantitative models in multi-scaled tissue models and simulations. This workflow allows to import and access SBML-based models. SBML model species, reactions and parameters are semantically integrated in cell behavioural models (CBM) represented by graphical process diagrams. By this, cellular states like proliferation and differentiation can be flexibly linked to gene-regulatory or biochemical reaction networks. For a multi-scale agent-based tissue simulation executable code is automatically generated where different time scales of imported SBML models and CBM have been mapped. We demonstrate the capabilities of the novel software workflow by integrating Tyson's cell cycle model in our model of human epidermal tissue homeostasis. Finally, we show the semantic interplay of the different biological scales during tissue simulation. AVAILABILITY: The EPISIM platform is available as binary executables for Windows, Linux and Mac OS X at http://www.tiga.uni-hd.de. Supplementary data are available at http://www.tiga.uni-hd.de/supplements/SemSBMLIntegration.html. CONTACT: [email protected].

MOTIVATION: For a mechanistic understanding of skin and its response to an induced perturbation, systems biology is gaining increasing attention. Unfortunately, quantitative and spatial expression data for skin, like for most other tissues, are almost not available. RESULTS: Integrating organotypic skin cultures, whole-slide scanning and subsequent image processing provides bioinformatics with a novel source of spatial expression data. We here used this approach to quantitatively describe the effect of treating organotypic skin cultures with sodium dodecyl sulphate in a non-corrosive concentration. We first measured the differentiation-related spatial expression gradient of Heat-Shock-Protein 27 in a time series of up to 24 h. Secondly, a multi-dimensional tissue classifier for predicting skin irritation was developed based on abstract features of these profiles. We obtained a high specificity of 0.94 and a sensitivity of 0.92 compared with manual classification. Our results demonstrate that the integration of tissue cultures, whole-slide scanning and image processing is well suited for both the standardized data acquisition for systems biological tissue models and a highly robust classification of tissue responses.

MOTIVATION: For the efficient application of multi-agent systems to spatial and functional modeling of tissues flexible and intuitive modeling tools are needed, which allow the graphical specification of cellular behavior in a tissue context without presuming specialized programming skills. RESULTS: We developed a graphical modeling system for multi-agent based simulation of tissue homeostasis. An editor allows the intuitive and hierarchically structured specification of cellular behavior. The models are then automatically compiled into highly efficient source code and dynamically linked to an interactive graphical simulation environment. The system allows the quantitative analysis of the morphological and functional tissue properties emerging from the cell behavioral model. We demonstrate the relevance of the approach using a recently published model of epidermal homeostasis as well as a series of cell-cycle models. AVAILABILITY: The complete software is available in binary executables for MS-Windows and Linux at tiga.uni-hd.de.

BACKGROUND: The epidermal physiology results from a complex regulated homeostasis of keratinocyte proliferation, differentiation and death and is tightly regulated by a specific protein expression during cellular maturation. Cellular in silico models are considered a promising and inevitable tool for the understanding of this complex system. Hence, we need to incorporate the information of the differentiation dependent protein expression in cell based systems biological models of tissue homeostasis. Such methods require measuring tissue differentiation quantitatively while correlating it with biomarker expression intensities. RESULTS: Differentiation of a keratinocyte is characterized by its continuously changing morphology concomitant with its movement from the basal layer to the surface, leading to a decreased average nuclei density throughout the tissue. Based thereon, we designed and evaluated three different mathematical measures (nuclei based, distance based, and joint approach) for quantifying differentiation in epidermal keratinocytes. We integrated them with an immunofluorescent staining and image analysis method for tissue sections, automatically quantifying epidermal differentiation and measuring the corresponding expression of biomarkers. When studying five well-known differentiation related biomarkers in an epidermal neck sample only the resulting biomarker profiles incorporating the relative distance information of cells to the tissue borders (distance based and joint approach) provided a high-resolution view on the whole process of keratinocyte differentiation. By contrast, the inverse nuclei density approach led to an increased resolution at early but heavily decreased resolution at late differentiation. This effect results from the heavy non-linear decay of DAPI intensity per area, probably caused by cytoplasmic growth and chromatin decondensation. In the joint approach this effect could be compensated again by incorporating distance information. CONCLUSION: We suppose that key mechanisms regulating tissue homeostasis probably depend more on distance information rather than on nuclei reorganization. Concluding, the distance approach appears well suited for comprehensively observing keratinocyte differentiation.

Computational models of tissue homeostasis will facilitate a deeper understanding of many diseases. They link molecular networks, cellular differentiation and the spatial and temporal organization of tissues. Here we show an approach which is able to computationally turn a healthy in silico epidermis into one with four central properties of psoriatic epidermis. We achieve this by altering a single simulation parameter in the cellular differentiation program of the simulated epidermal keratinocytes: the fractional time period during which transit amplifying cells proliferate (tau). Prolonging tau results in the four main pathological characteristics of psoriatic skin: (1) an absolute increase of the germinative compartment, (2) an absolute increase of the differentiated compartment, (3) a higher proportion of germinative cells and (4) a marked reduction in turnover time. The prolongation of tau is able to increase the proliferation capacity of the epidermal tissue without altering the cell cycle frequency.

MOTIVATION: For systems biology of complex stratified epithelia like human epidermis, it will be of particular importance to reconstruct the spatiotemporal gene and protein networks regulating keratinocyte differentiation and homeostasis. RESULTS: Inside the epidermis, the differentiation state of individual keratinocytes is correlated with their respective distance from the connective tissue. We here present a novel method to profile this correlation for multiple epithelial protein biomarkers in the form of quantitative spatial profiles. Profiles were computed by applying image processing algorithms to histological sections stained with tri-color indirect immunofluorescence. From the quantitative spatial profiles, reflecting the spatiotemporal changes of protein expression during cellular differentiation, graphs of protein networks were reconstructed. CONCLUSION: Spatiotemporal networks can be used as a means for comparing and interpreting quantitative spatial protein expression profiles obtained from different tissue samples. In combination with automated microscopes, our new method supports the large-scale systems biological analysis of stratified epithelial tissues.

MOTIVATION: Systems biology is currently focused on integrating intracellular networks, although clinically, diseases are largely defined by their histological features. For example, no computational model can simulate today the formation of a horizontally layered epidermis. Since the epidermis is the most complex structured epithelial tissue, systems biology models could yield important insights in epithelial tissue, in which most of all human cancers arise. RESULTS: We describe the algorithms of a system, capable of simulating the tissue homeostasis in human epidermis leading to a horizontally layered tissue with cells of different differentiation stages. The system predicts epidermal morphology, tissue kinetics and 2D flow of Ca2+ ions. Predicted properties of an epidermis with a healthy and a disturbed barrier are compared with the literature. The system closely mimics the respecting physiological situations. AVAILABILITY: Additional information and films of the simulation are available at the website. Source code is available on request. http://www.zbh.uni-hamburg.de/research/ESB/index.php CONTACT: [email protected]

SUMMARY: The prediction of significant short functional protein sequences has inherent problems. In predicting phosphorylation sites, problems came from the shortness of phosphorylation sites, the difficulties in maintaining many different predefined models of binding sites, and the difficulties of obtaining highly sensitive predictions and of obtaining predictions with a constant sensitivity and specificity. The algorithm presented in this paper overcomes these problems. The proposed algorithm PHOSITE is based on the case-based sequence analysis. This enables the prediction of phosphorylation sites with constant specificity and sensitivity. Furthermore, this method leads not only to the prediction of phosphorylation sites in general but also predicts the most probable type of kinase involved. AVAILABILITY: The tool PHOSITE implementing the presented method can be evaluated under the website http://www.phosite.com.