Dynamic cell stimulation is a powerful technique for probing gene networks and for applications in stem cell differentiation, immunomodulation and signaling. We developed a robust and flexible method and associated microfluidic devices to generate a wide-range of precisely formulated dynamic chemical signals to stimulate live cells and measure their dynamic response. This signal generator is capable of digital to analog conversion (DAC) through combinatoric selection of discrete input concentrations, and outperforms existing methods by both achievable resolution, dynamic range and simplicity in design. It requires no calibration, has minimal space requirements and can be easily integrated into microfluidic cell culture devices. The signal generator hardware and software we developed allows to choose the waveform, period and amplitude of chemical input signals and features addition of well-defined chemical noise to study the role of stochasticity in cellular information processing.
The master equation is rarely exactly solvable and hence various means of approximation have been devised. A popular systematic approximation method is the system-size expansion which approximates the master equation by a generalised Fokker-Planck equation. Here we first review the use of the expansion by applying it to a simple chemical system. The example shows that the solution of the generalised Fokker-Planck equation obtained from the expansion is generally not positive definite and hence cannot be interpreted as a probability density function. Based on this observation, one may also a priori conclude that moments calculated from the solution of the generalised Fokker-Planck equation are not accurate; however calculation shows these moments to be in good agreement with those obtained from the exact solution of the master equation. We present an alternative simpler derivation which directly leads to the same moments as the system-size expansion but which bypasses the use of generalised Fokker-Planck equations, thus circumventing the problem with the probabilistic interpretation of the solution of these equations.
Recently, measurement technologies allowing to determine the abundance of tens signaling proteins in thousands of single cells became available. The interpretation of this high dimensional end-point time course data is often difficult, because sources of cell-to-cell abundance variation in measured species are hard to determine. Here I present an analytic tool to tackle this problem. By using a recently developed chemical signal generator to manipulate input noise of biochemical networks, measurement of state variables and modeling of input noise propagation, pathway-specific variability can be distinguished from environmental variability caused by network embedding. By employing different sources of natural input noise, changes in the output variability of the apoptosis pathway were measured, indicating that also synthetic noisy perturbations are biologically feasible. The presented analytic tool shows how signal generators can improve our understanding of the origin of cellular variability and help to interpret multiplexed single cell information.
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