Abstract

BackgroundCells use signaling protein networks to sense their environment and mediate specific responses. Information about environmental stress is usually encoded in the dynamics of the signaling molecules, and qualitatively distinct dynamics of the same signaling molecule can lead to dramatically different cell fates. Exploring the design principles of networks with multiple signal-encoding functions is important for understanding how distinct dynamic patterns are shaped and integrated by real cellular networks, and for building cells with targeted sensing–response functions via synthetic biology.ResultsIn this paper, we investigate multi-node enzymatic regulatory networks with three signal-encoding functions, i.e., dynamic responses of oscillation, transient activation, and sustained activation upon step stimulation by three different inducers, respectively. Taking into account competition effects of the substrates for the same enzyme in the enzymatic reactions, we searched for robust subnetworks for each signal-encoding function by three-node-network enumeration and then integrated the three subnetworks together via node-merging. The obtained tri-functional networks consisted of four to six nodes, and the core structures of these networks were hybrids of the motifs for the subfunctions.ConclusionsThe simplest but relatively robust tri-functional networks demonstrated that the three functions were compatible within a simple negative feedback loop. Depending on the network structure, the competition effects of the substrates for the same enzyme within the networks could promote or hamper the target functions, and can create implicit functional motifs. Overall, the networks we obtained could in principle be synthesized to construct dynamic control circuits with multiple target functions.

Highlights

  • Cells use signaling protein networks to sense their environment and mediate specific responses

  • Module selection for each signal-processing function We use the method of three-node-network enumeration to explore the functional networks of oscillation (F1) and adaptation (F2), respectively

  • The robustness of sub-functions Fig. 1c as oscillations (F1) and F2 is measured by the number of parameter sets, i.e., Q1- and Q2-values, that generate the target dynamic functions

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Summary

Introduction

Cells use signaling protein networks to sense their environment and mediate specific responses. Information about environmental stress is usually encoded in the dynamics of the signaling molecules, and qualitatively distinct dynamics of the same signaling molecule can lead to dramatically different cell fates. Exploring the design principles of networks with multiple signal-encoding functions is important for understanding how distinct dynamic patterns are shaped and integrated by real cellular networks, and for building cells with targeted sensing–response functions via synthetic biology. Cells transmit information via dynamic control of signaling molecules [3]. Cells mediate gene expression programs and induce cellular responses according to the sequential dynamics of signaling molecules [3,4,5,6,7,8,9,10,11,12,13]. Signaling dynamics usually (2019) 13:6 such as ERK, NF-κB and Crz all drive distinct responses via dynamic control [3, 28,29,30]

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