Reducing phenotypic instabilities of a microbial population during continuous cultivation based on cell switching dynamics.

Predicting the fate of individual cells among a microbial population (i.e., growth and gene expression) remains a challenge, especially when this population is exposed to very dynamic environmental conditions, such as those encountered during continuous cultivation. Indeed, the dynamic nature of a continuous cultivation process implies the potential diversification of the microbial population resulting in genotypic and phenotypic heterogeneity. The present work focused on the induction of the arabinose operon in Escherichia coli as a model system to study this diversification process in continuous cultivations. As a preliminary step, the green fluorescent protein (GFP) level triggered by an arabinose-inducible ParaBAD promoter was tracked by flow cytometry in chemostat cultivations with glucose-arabinose co-feeding. For a wide range of glucose-arabinose co-feeding concentrations in the chemostats, the simultaneous occurrence of GFP positive and negative subpopulation was observed. In the second set of experiments, continuous cultivation was performed by adding glucose continuously and arabinose based on the capability of individual cells to switch from low GFP to high GFP expression states, performed with a technology setup called segregostat. In the segregostat cultivation mode, on-line flow cytometry analysis was used for adjusting the arabinose/glucose transitions based on the phenotypic switching profiles of the microbial population. This strategy allowed finding an appropriate arabinose pulsing frequency, leading to prolonged maintenance of the induction level with a limited increase in the phenotypic diversity for more than 60 generations. The results suggest that the steady forcing of individual cells into a given phenotypic trajectory may not be the best strategy for controlling cell populations. Instead, allowing individual cells to switch periodically around a predefined threshold seems to be a more robust strategy leading to oscillations, but within a predictable cell population behavior range.

Coupled oscillator cooperativity as a control mechanism in chronobiology.

Control of dynamical processes is vital for maintaining correct cell regulation and cell-fate decisions. Numerous regulatory networks show oscillatory behavior; however, our knowledge of how one oscillator behaves when stimulated by two or more external oscillatory signals is still missing. We explore this problem by constructing a synthetic oscillatory system in yeast and stimulate it with two external oscillatory signals. Letting model verification and prediction operate in a tight interplay with experimental observations, we find that stimulation with two external signals expands the plateau of entrainment and reduces the fluctuations of oscillations. Furthermore, by adjusting the phase differences of external signals, one can control the amplitude of oscillations, which is understood through the signal delay of the unperturbed oscillatory network. With this we reveal a direct amplitude dependency of downstream gene transcription. Taken together, these results suggest a new path to control oscillatory systems by coupled oscillator cooperativity.

Efficient hardware implementation of the subthalamic nucleus-external globus pallidus oscillation system and its dynamics investigation.

Modeling and implementation of the nonlinear neural system with physiologically plausible dynamic behaviors are considerably meaningful in the field of computational neuroscience. This study introduces a novel hardware platform to investigate the dynamical behaviors within the nonlinear subthalamic nucleus-external globus pallidus system. In order to reduce the implementation complexities, a hardware-oriented conductance-based subthalamic nucleus (STN) model is presented, which can reproduce accurately the dynamical characteristics of biological conductance-based STN cells. The accuracy of the presented design is ensured by the investigation of the dynamical properties including bifurcation analysis and phase portraits. Hardware implementation on a field-programmable gate array (FPGA) demonstrates that the proposed digital system can mimic the relevant biological characteristics with higher performance, which means the resource cost is cut down and the computational efficiency is improved by introducing the multiplier-less techniques including novel "shift MUL" approach and piecewise linear approximation. The central pattern generator (CPG) coupled by the presented system is also investigated, which can be applied as an embedded intelligent system in the field of neuro-robotic engineering.

Enhanced stability and polyadenylation of select mRNAs support rapid thermogenesis in the brown fat of a hibernator.

During hibernation, animals cycle between torpor and arousal. These cycles involve dramatic but poorly understood mechanisms of dynamic physiological regulation at the level of gene expression. Each cycle, Brown Adipose Tissue (BAT) drives periodic arousal from torpor by generating essential heat. We applied digital transcriptome analysis to precisely timed samples to identify molecular pathways that underlie the intense activity cycles of hibernator BAT. A cohort of transcripts increased during torpor, paradoxical because transcription effectively ceases at these low temperatures. We show that this increase occurs not by elevated transcription but rather by enhanced stabilization associated with maintenance and/or extension of long poly(A) tails. Mathematical modeling further supports a temperature-sensitive mechanism to protect a subset of transcripts from ongoing bulk degradation instead of increased transcription. This subset was enriched in a C-rich motif and genes required for BAT activation, suggesting a model and mechanism to prioritize translation of key proteins for thermogenesis.