Design of artificial myocardial microtissues

Cultivation technologies promoting organization of mammalian cells in three dimensions are essential for gene-function analyses as well as drug testing and represent the first step toward the design of tissue replacements and bioartificial organs. Embedded in a three-dimensional environment, cells are expected to develop tissue-like higher order intercellular structures (cell-cell contacts, extracellular matrix) that orchestrate cellular functions including proliferation, differentiation, apoptosis, and angiogenesis with unmatched quality. We have refined the hanging drop cultivation technology to pioneer beating heart microtissues derived from pure primary rat and mouse cardiomyocyte cultures as well as mixed populations reflecting the cell type composition of rodent hearts. Phenotypic characterization combined with detailed analysis of muscle-specific cell traits, extracellular matrix components, as well as endogenous vascular endothelial growth factor (VEGF) expression profiles of heart microtissues revealed (1) a linear cell number-microtissue size correlation, (2) intermicrotissue superstructures, (3) retention of key cardiomyocyte-specific cell qualities, (4) a sophisticated extracellular matrix, and (5) a high degree of self-organization exemplified by the tendency of muscle structures to assemble at the periphery of these myocardial spheroids. Furthermore (6), myocardial spheroids support endogenous VEGF expression in a size-dependent manner that will likely promote vascularization of heart microtissues produced from defined cell mixtures as well as support connection to the host vascular system after implantation. As cardiomyocytes are known to be refractory to current transfection technologies we have designed lentivirus-based transduction strategies to lead the way for genetic engineering of myocardial microtissues in a clinical setting.

Accelerating, hyperaccelerating, and decelerating networks

Many growing networks possess accelerating statistics where the number of links added with each new node is an increasing function of network size so the total number of links increases faster than linearly with network size. In particular, biological networks can display a quadratic growth in regulator number with genome size even while remaining sparsely connected. These features are mutually incompatible in standard treatments of network theory which typically require that every new network node possesses at least one connection. To model sparsely connected networks, we generalize existing approaches and add each new node with a probabilistic number of links to generate either accelerating, hyperaccelerating, or even decelerating network statistics in different regimes. Under preferential attachment for example, slowly accelerating networks display stationary scale-free statistics relatively independent of network size while more rapidly accelerating networks display a transition from scale-free to exponential statistics with network growth. Such transitions explain, for instance, the evolutionary record of single-celled organisms which display strict size and complexity limits.