Linear array of multi-substrate tracts for simultaneous assessment of cell adhesion, migration, and differentiation.

Cell migration, which is central to a wide variety of life processes, involves integration of the extracellular matrix (ECM) with the internal cytoskeleton and motor proteins via receptors spanning the plasma membrane. Cell migration can be induced by a variety of signals, including gradients of external soluble molecules, differences in ECM composition, or electrical gradients. Current in vitro methods to study cell migration only test one substrate at a time. Here, we present a method for assessing cell adhesion, migration, and differentiation in up to 20 different test conditions simultaneously, using only minute amounts of target substrate. Our system, which we call the linear array of multi-substrate cell migration assay (LAMA), has two configurations for direct comparison of one or two cell types in response to an array of ECM constituents under the same culture conditions. This culture model utilizes only nanogram amounts of test substrates and a minimal number of cells, which maximizes the use of limited and expensive test reagents. Moreover, LAMA can also be used for high-throughput screening of potential pharmaceuticals that target ECM-dependent cell behavior and differentiation.

Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population.

Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special attention. The present article provides an introduction to one particular class of algorithms which employ marginalization in order to take heterogeneity into account. An overview of alternative approaches is provided for comparison. We treat two frequently encountered scenarios in single-cell experiments, namely, single-cell trajectory data and single-cell distribution data.

Optimal Prediction by Cellular Signaling Networks.

Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.

Modeling evolutionary dynamics of epigenetic mutations in hierarchically organized tumors.

The cancer stem cell (CSC) concept is a highly debated topic in cancer research. While experimental evidence in favor of the cancer stem cell theory is apparently abundant, the results are often criticized as being difficult to interpret. An important reason for this is that most experimental data that support this model rely on transplantation studies. In this study we use a novel cellular Potts model to elucidate the dynamics of established malignancies that are driven by a small subset of CSCs. Our results demonstrate that epigenetic mutations that occur during mitosis display highly altered dynamics in CSC-driven malignancies compared to a classical, non-hierarchical model of growth. In particular, the heterogeneity observed in CSC-driven tumors is considerably higher. We speculate that this feature could be used in combination with epigenetic (methylation) sequencing studies of human malignancies to prove or refute the CSC hypothesis in established tumors without the need for transplantation. Moreover our tumor growth simulations indicate that CSC-driven tumors display evolutionary features that can be considered beneficial during tumor progression. Besides an increased heterogeneity they also exhibit properties that allow the escape of clones from local fitness peaks. This leads to more aggressive phenotypes in the long run and makes the neoplasm more adaptable to stringent selective forces such as cancer treatment. Indeed when therapy is applied the clone landscape of the regrown tumor is more aggressive with respect to the primary tumor, whereas the classical model demonstrated similar patterns before and after therapy. Understanding these often counter-intuitive fundamental properties of (non-)hierarchically organized malignancies is a crucial step in validating the CSC concept as well as providing insight into the therapeutical consequences of this model.

Spatial simulations of myxobacterial development.

Many bacteria exhibit multicellular behaviour, with individuals within a colony coordinating their actions for communal benefit. One example of complex multicellular phenotypes is myxobacterial fruiting body formation, where thousands of cells aggregate into large three-dimensional structures, within which sporulation occurs. Here we describe a novel theoretical model, which uses Monte Carlo dynamics to simulate and explain multicellular development. The model captures multiple behaviours observed during fruiting, including the spontaneous formation of aggregation centres and the formation and dissolution of fruiting bodies. We show that a small number of physical properties in the model is sufficient to explain the most frequently documented population-level behaviours observed during development in Myxococcus xanthus.