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Welcome to the Laboratory of Systems Biology

The Laboratory of Systems Biology is a part of the Center for Nonlinear Studies in Institute Of Cybernetics, Tallinn University of Technology.


The main aim of the laboratory is to study regulation of intracellular processes and understand functional influences of intracellular interactions.
For this, a mixture of experimental and theoretical approaches are used.

Specific scientific interests, projects

  • Intracellular compartmentation and diffusion restrictions, influence on bioenergetics.
  • How the fine structure of mitochondrion is influencing the dynamics of oxidative phosphorylation?
  • Regulation of energy transfer in different types of cells.
  • Metabolic stability of heart muscle cells.
  • Interaction between mechanics and energetics of the heart.


We use interdisciplinary approaches to tackle questions in cardiac physiology. For that, we have formed a team of researchers with backgrounds in biophysics, biology, and applied mathematics/physics. As a result, we are able to approach scientific questions on different scales, from organ to molecular level, using combinations of different experimental and theoretical techniques. When needed, we find new ways to characterize the data, develop new mathematical models, build new hardware and program it to carry out novel experimental protocols. For example, we have developed mitochondrial positioning analysis in 2D and 3D, and built a confocal microscope to study diffusion in the cell. Among the experimental techniques routinely used in the laboratory are

  • kinetic measurements,
  • fluorescence correlation spectroscopy based techniques,
  • confocal and wide-field fluorescence imaging,
  • controlled single cell force measurements,
  • patch clamping.

We have developed models

  • to study energetics and mechanics of the left ventricle as well as intracellular diffusion using finite element method,
  • solved partial differential equations to analyze cross-bridge mechanoenergetics,
  • used molecular dynamics to analyze interaction between proteins and mitochondrial membrane,
  • designed multiple other models and computational techniques to analyze the experimental data.

The results of our work are distributed through publications and the open-source software that we develop.


The research in laboratory is/was financed by


Head: Marko Vendelin

Laboratory of Systems Biology
Institute of Cybernetics
Tallinn University of Technology
Akadeemia 21
12618 Tallinn

Fax: +372 620 4151
Phone: +372 620 4169
CK system is inactivated in GAMT-/- mouse cardiomyocytes due to lack of creatine. On permeabilized cardiomyocytes, a full set of kinetic data were recorded to analyze the intracellular compartmentation of ADP/ATP associated with GAMT deficiency using mathematical models. On the basis of our data and analysis, we conclude in Branovets et al, Am J Physiol Heart Circ Physiol 2013, inactivation of the CK system by GAMT deficiency does not alter mitochondrial organization and intracellular compartmentation in relaxed cardiomyocytes.
Approach to analyze intracellular compartmentation. Experimental data from measurements designed to show intracellular ATP production and utilization through different angles is used as an input to mathematical models. The models can be used to test various scenarios of intracellular compartmentation. Read more about the application of this approach on different preparations in Mervi's PhD thesis, 2013.
Determination of sarcomere lengths in single rat cardiomyocyte experiments with different preload levels. Upper plot shows the time series of sarcomere lengths over experiment period of 40 seconds. During the experiment period the preload is varied. Middle plot shows the sarcomere lengths over 1 second period as recorded during different loading conditions. Lower plot shows the transmission images of cardiomyocytes at time moments indicated with connecting lines between the middle and lower plots. The region between lines of white pixels indicates the ROI that is used for determining the mean sarcomere lengths for each captured image of cardiomyocytes. See Peterson et al, AJP Cell Phys 2013.
Red and green vehicles represent molecules with different diffusion coefficients: small molecules are represented by a fast red car and big molecules by a large green truck. In solution surrounding the cell (on the highway), diffusion is unhindered and the diffusion coefficient of smaller molecules (fluorescent ATP in this case) is almost 4 times higher than of a larger molecule (fluorescent Dextran 10K). In the cardiomyocyte, diffusion restrictions imposed by intracellular structures lead to considerably smaller difference between estimated diffusion coefficients (1.7 times). See Illaste et al, Biophys J 2012 for measurement results and computational model explaining this counter-intuitive result. (Author of image: Erik Illaste)
Patch of mitochondrial inner membrane with attached octameric creatine kinase and transmembrane adenine nucleotide translocase proteins. Using molecular dynamics simulations, the details of mitochondrial creatine kinase binding to the membrane were analyzed (inset). See Karo et al, J Biol Chem 2012.
Example of the response of permeabilized cardiomyocytes to changes in solution. From analysis of autofluorescence changes (NADH and flavoproteins, Fp), we demonstrated that there are significant diffusion restrictions within heart muscle cells. See Jepihhina et al, Biophys J 2011.
The image shows mitochondria in rat cardiomyocyte before (left) and after deconvolution (right). Both, xy and yz cross-sections of the image stack are shown. At upper left corner, a zoomed region from the middle of xy cross-section is shown. Note that the deconvolution has improved contrast (space between mitochondria is much clearer) and smoothed noise out. Deconvolution software is published as an open-source project (see IOCBio at and Laasmaa et al, J Microsc 2011).
With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. We have developed a symbolic routine to find the steady state regimes for genome-scale metabolic networks. As an example, flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast is shown in the figure. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. See Schryer et al, BMC Syst Biol 2011; software is available as a part of SympyCore.
The image shows time course of 31P-NMR inversion and saturation transfer experiments performed on perfused rat hearts. During the experiments, NMR spectra (from top to bottom) change in time (from left to right) due to kinetics of intracellular reactions and NMR relaxation. In the image, the peaks corresponding to PCr (the first from the bottom) and γATP (the second from the bottom) are clearly visible. Note how inversion of PCr leads to the changes in γATP (the left part of the image) and how saturation of ATP reduces PCr peak (the right part of the image). Through analysis of several inversion and saturation transfer protocols, it is possible to determine energy transfer pathways in the heart. See Vendelin et al, J Biol Chem 2010.

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