Scientists generated a mathematical model that describes glucose turnover in the liver

Scientists of the Virtual Liver Network generated a mathematical model of glucose turnover in the liver. Main steps to generate such a model are:

  • i) the collection of available information about the biochemical processes of the glucose metabolism (with substrates and enzymes) from databases (SABIO-RK) and the published literature;
  • ii) manual curation of the information;
  • iii) preparation of a complex network of reactions;
  • iv) description of the behavior of the individual processes with mathematical equations (ordinary differential equation, ODE) which form the basis of the mathematical model.
  • The resulting model describes the transformations that happen to glucose in the liver. It includes the hormonal regulation by the key factors insulin and glucagon, which speed up, switch on, slow down or switch off central reactions of glucose metabolism depending on the physiological state. Thereby, glucose homeostasis, the process of keeping the glucose levels within a narrow range, can be achieved. The resulting model can describe the dual role of the liver as glucose producer and glucose consumer depending on the blood glucose concentrations.
  • Creative Commons License
    VLN glucose metabolism by Matthias Koenig is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
    Based on a work at

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    Detailed Simulation of Blood Flow and Drug Metabolization

  • At the organ scale, blood is distributed along blood vessels of decreasing size to the so-called sinusoids where an exchange of substances (e.g., drugs) between blood and cells takes place.

  • Scientists from Virtual Liver Network are interested in physiology-based models which mechanistically link liver perfusion and cellular processes, in particular in cases of diseases of spatially varying extent, such as lipid accumulations in steatotic livers. For this purpose, an accurate geometric representation of the liver and its blood vessels (obtained from an in vivo CT scan) is combined with so-called pharmacokinetics models. These describe in effective form how fast a given substance is transferred from the blood into the liver cells and how it is metabolized there.
  • The video above shows simulation results for the substance CFDA SE (carboxyfluorescein diacetate succinimidyl ester) injected into the blood flow through an isolated mouse liver. At first, no CFDA SE is present in the blood vessels and the liver tissue (shown in blue). Following the infusion (high concentration visualized in red), the substance enters through the supplying blood vessels and gets distributed throughout the organ and into the draining blood vessels. After the infusion stops, CFDA SE is washed out of the organ within about half a minute.
  • Authors: Lars Küpfer and Ole Schwen

  • Scientific publication: Spatio-Temporal Simulation of First Pass Drug Perfusion in the Liver
    Average: 4.8 (5 votes)
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    Large-scale measurement of cellular properties

    To ensure the validity of the models developed in the Virtual Liver project, they are validated by means of experimental data. Here, the validation of scale-independent models is a particular challenge. The data must be both fine-grained enough to describe the properties of individual cells, and comprehensive enough to represent their distribution across the organ.

  • Computer-based image analysis methods can measure cellular properties in microscopic tissue images automatically and accurately. The imaging of entire organs, however, produces gigabyte-sized tissue images, which are too big to be analyzed with existing methods.
  • Scientists in the Virtual Liver project therefore develop novel analysis methods that can analyze even large tissue images comprehensively and quickly. These methods can recognize different tissue and cell types, for example in order to measure the regeneration activity or the fraction of necrotic tissue in the liver. Another application is the automatic measurement of fatty changes in the liver, which become visible under the microscope as thousands of roundish fat vacuoles (see figure). Fatty changes are an important model parameter in the Virtual Liver Project because they reduce the liver function.
  • In clinical practice, cellular properties are regularly measured in tissue samples in order to assess the aggressiveness of tumors. Since a complete analysis is too time-consuming, it has to be limited to only a few tissue regions. The image analysis methods developed in the Virtual Liver project, for the first time, enable the complete analysis of large tissue regions. In this manner, they do not only provide the data for the validation of scale-independent models, but also have the potential to improve the accuracy of medical diagnostics.
  • Author: André Homeyer

  • Image: Uta Dahmen, Olaf Dirsch and André Homeyer

  • Publications:
    1. Practical quantification of necrosis in histological whole-slide images
    2. A comparison of sampling strategies for histological image analysis
    Average: 5 (3 votes)
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    3D reconstruction of serial whole slide sections

    In order to understand liver functions, the physiology and morphology of microscopic tissue sections are invaluable. However, one section is extremely thin (4 µm) and thus captures only a small fraction of the whole organ. This means disregarding a lot of tissue information as well as their 3D correlation.

  • Scientists in the Virtual Liver develop methods to digitally reconstruct the organ in 3D based on serial tissue sections. The main challenges for this are deformations of the tissue which are introduced during the cutting process and the placement of the section on the slide. Thus consecutive slides cannot simply be stacked on top of each other to regain the 3D object.
  • Therefore a so-called registration algorithm was developed. This algorithm analyzes two consecutive sections for corresponding structures - e.g. vessels that are expected to continue smoothly through the organ. Based on this analysis the digital image is deformed, so that those corresponding structures match again.
  • The video shows three different stages of this process: the left side shows a run through a stack of consecutive serial tissue sections in their initial stage – directly from the slide scanner. As we can see the position from one slide to the next in general does not match at all. This middle shows the result of the first registration step which compensates coarse deformations. The right side shows the result after a refinement step, which is aimed at compensating misalignments in more detail.
  • Further reading: Registration of histological whole slide images guided by vessel structures
  • Author: Michael Schwier

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