Ca2+ Requirements for Long-Term Depression Are Frequency Sensitive in Purkinje Cells

Cerebellar long-term depression (LTD) is a form of synaptic plasticity dependent on postsynaptic Ca(2+) changes. One fundamental question is how LTD is selectively induced by specific numbers of Ca(2+) pulses and which are the frequency and duration of this train of pulses required for LTD induction. The molecular mechanism which leads the integration of postsynaptic Ca(2+) pulses in the LTD signaling network has not been elucidated either. Recent publications have shown that Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) is required for LTD induction. Additionally, protein kinase C (PKC), CaMKII, and MAPK play an important role to transduce the frequency of Ca(2+) pulses into their enzymatic activity levels; however, it is still unknown which enzymes are involved in decoding Ca(2+) pulses in LTD. We have extended a stochastic model of LTD by adding the molecular network regulating CaMKII activity and its activation. We solved this model with stochastic engine for pathway simulation to include the effect of biochemical noise in LTD. We systematically investigated the dependence of LTD induction on stimulus frequencies, and we found that LTD is selectively induced by a specific number of Ca(2+) spikes at different frequencies. We observed that CaMKII is essential to induce LTD, and LTD is only weakly induced when its Thr286 phosphorylation site has been deleted. We found that CaMKII decodes the frequency of Ca(2+) spikes into different amounts of kinase activity during LTD induction. In addition, PKC and ERK enzyme activity is highly sensitive to the frequency and the number of Ca(2+) pulses and this sensitivity has an important effect on LTD activation. This research predicts the postsynaptic Ca(2+) requirements to induce LTD using a typical synaptic activation sequence and explains how LTD is selectively induced by specific number of Ca(2+) pulses at different frequencies

In Vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli Using Microfluidics

We study both in silico and in vivo the real-time feedback control of a molecular titration motif that has been earmarked as a fundamental component of antithetic and multicellular feedback control schemes in E. coli. We show that an external feedback control strategy can successfully regulate the average fluorescence output of a bacterial cell population to a desired constant level in real-time. We also provide in silico evidence that the same strategy can be used to track a time-varying reference signal where the set-point is switched to a different value halfway through the experiment. We use the experimental data to refine and parametrize an in silico model of the motif that can be used as an error computation module in future embedded or multicellular control experiments

Cheetah: A Computational Toolkit for Cybergenetic Control

Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells