Cells  utilize chemical communication to exchange information and coordinate their behavior in noisy environments. Depending on the scenario, communication can reduce variability and shape a collective response, or amplify variability to generate distinct phenotypic subpopulations. Here we use a moment-based approach to study how cell-cell communication affects noise in biochemical networks that arises from both intrinsic and extrinsic sources. Based on a recently proposed model reduction technique, we derive a system of  differential equations that captures lower-order moments of a population of cells, which communicate by secreting and sensing a diffusing molecule.  Importantly, the number of equations that we obtain in this way is independent of the number of considered cells such that the method scales to arbitrary population sizes. Based on this approach, we analyze how cell-cell communication affects noise in several biochemical circuits .
Cells can utilize chemical communication to exchange information and coordinate their behavior in the presence of noise. Communication can reduce noise to shape a collective response, or amplify noise to generate distinct phenotypic subpopulations. Here we discuss a moment-based approach to study how cell-cell communication affects noise in biochemical networks that arises from both intrinsic and extrinsic sources. We derive a system of approximate differential equations that captures lower-order moments of a population of cells, which communicate by secreting and sensing a diffusing molecule. Since the number of obtained equations grows combinatorially with number of considered cells, we employ a previously proposed model reduction technique, which exploits symmetries in the underlying moment dynamics. Importantly, the number of equations obtained in this way is independent of the number of considered cells such that the method scales to arbitrary population sizes. Based on this approach, we study how cell-cell communication affects population variability in several biochemical networks. Moreover, we analyze the accuracy and computational efficiency of the moment-based approximation by comparing it with moments obtained from stochastic simulations .
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can
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noisy environments. Depending on the scenario, communication can reduce variability and
the presence of noise. Communication can reduce noise to
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variability
noise
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use
discuss
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Based on a recently proposed model reduction technique, we
We
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approximate
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Since the number of obtained equations grows combinatorially with number of considered cells, we employ a previously proposed model reduction technique, which exploits symmetries in the underlying moment dynamics.
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that we obtain
obtained
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analyze
study
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noise
population variability
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circuits
networks. Moreover, we analyze the accuracy and computational efficiency of the moment-based approximation by comparing it with moments obtained from stochastic simulations