We studied the behaviour of the repressilator at 28 °C, 30 °C, 33 °C, and 37 °C. From the fluorescence in each cell over time, we determined the period of oscillations, the functionality (fraction of cells exhibiting oscillations) and the robustness (fraction of expected oscillations that occur) of this circuit. We show that the oscillatory dynamics differs with temperature. Functionality is maximized at 30 °C. Robustness decreases beyond 30 °C, as most cells exhibit 'failed' oscillations. These failures cause the distribution of periods to become bimodal, with an 'apparent period' that is minimal at 30 °C, while the true period decreases with increasing temperature. Based on previous studies, we hypothesized that the failures are due to a loss of functionality of one protein of the repressilator, CI. To test this, we studied the kinetics of a genetic switch, formed by the proteins CI and Cro, whose expression is controlled by PRM and PR, respectively. By probing the activity of PRM by in vivo detection of MS2-GFP tagged RNA, we find that, beyond 30 °C, the production of the CI-coding RNA changes from sub-Poissonian to super-Poissonian. Given this, we suggest that the decrease in efficiency of CI as a repressor with temperature hinders the robustness of the repressilator beyond 30 °C. We conclude that the repressilator is sensitive but not robust to temperature. Replacing CI for a less temperature-dependent protein should enhance robustness.
Oscillatory regulatory networks have been discovered in many cellular pathways. An especially challenging area is studying dynamics of cellular oscillators interacting with one another in a population. Synchronization is only one of and the simplest outcome of such interaction. It is suggested that the outcome depends on the structure of the network. Phase-attractive (synchronizing) and phase-repulsive coupling structures were distinguished for regulatory oscillators. In this paper, we question this separation. We study an example of two interacting repressilators (artificial regulatory oscillators based on cyclic repression). We show that changing the cooperativity of transcription repression (Hill coefficient) and reaction timescales dramatically alter synchronization properties. The network becomes birhythmic-it chooses between the in-phase and antiphase synchronization. Thus, the type of synchronization is not characteristic for the network structure. However, we conclude that the specific scenario of emergence and stabilization of synchronous solutions is much more characteristic.
Data-based stochastic modeling of tree growth and structure formation Potapov I., Järvenpää M., Åkerblom M., Raumonen P., Kaasalainen M. (2016). Data-based stochastic modeling of tree growth and structure formation. Silva Fennica vol. 50 no. 1 article id 1413. 11 p. Highlights• We propose a stochastic version of the tree growth model LIGNUM for producing tree structures consistent with detailed terrestrial laser scanning data, and we provide the proofof-concept by using model-based simulations and real laser scanning data.• Trees produced with the data-based model resemble the trees of the dataset, and are statistically similar but not copies of each other; the number of such synthetic trees is not limited. AbstractWe introduce a general procedure to match a stochastic functional-structural tree model (here LIGNUM augmented with stochastic rules) with real tree structures depicted by quantitative structure models (QSMs) based on terrestrial laser scanning. The matching is done by iteratively finding the maximum correspondence between the measured tree structure and the stochastic choices of the algorithm. First, we analyze the match to synthetic data (generated by the model itself), where the target values of the parameters to be estimated are known in advance, and show that the algorithm converges properly. We then carry out the procedure on real data obtaining a realistic model. We thus conclude that the proposed stochastic structure model (SSM) approach is a viable solution for formulating realistic plant models based on data and accounting for the stochastic influences.
The relation between the electrical properties of the heart and the beating rate is essential for the heart functioning. This relation is central when calculating the “corrected QT interval” — an important measure of the risk of potentially lethal arrhythmias. We use the transfer entropy method from information theory to quantitatively study the mutual dynamics of the ventricular action potential duration (the QT interval) and the length of the beat-to-beat (RR) interval. We show that for healthy individuals there is a strong asymmetry in the information transfer: the information flow from RR to QT dominates over the opposite flow (from QT to RR), i.e. QT depends on RR to a larger extent than RR on QT. Moreover, the history of the intervals has a strong effect on the information transfer: at sufficiently long QT history length the information flow asymmetry inverts and the RR influence on QT dynamics weakens. Finally, we demonstrate that the widely used QT correction methods cannot properly capture the changes in the information flows between QT and RR. We conclude that our results obtained through a model-free informational perspective can be utilised to improve and test the QT correction schemes in clinics.
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