A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Here, with the use of a combinatorial method, a library of networks with varying connectivity was generated in Escherichia coli. These networks were composed of genes encoding the transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters. They displayed phenotypic behaviors resembling binary logical circuits, with two chemical "inputs" and a fluorescent protein "output." Within this simple system, diverse computational functions arose through changes in network connectivity. Combinatorial synthesis provides an alternative approach for studying biological networks, as well as an efficient method for producing diverse phenotypes in vivo.

The molecular mechanisms underlying phenotypic variation in isogenic bacterial populations remain poorly understood. We report that AcrAB-TolC, the main multidrug efflux pump of exhibits a strong partitioning bias for old cell poles by a segregation mechanism that is mediated by ternary AcrAB-TolC complex formation. Mother cells inheriting old poles are phenotypically distinct and display increased drug efflux activity relative to daughters. Consequently, we find systematic and long-lived growth differences between mother and daughter cells in the presence of subinhibitory drug concentrations. A simple model for biased partitioning predicts a population structure of long-lived and highly heterogeneous phenotypes. This straightforward mechanism of generating sustained growth rate differences at subinhibitory antibiotic concentrations has implications for understanding the emergence of multidrug resistance in bacteria.

The chemotaxis signalling network in E. coli that controls the locomotion of bacteria is a classic model system for signal transduction1–2. This pathway modulates the behaviour of flagellar motors to propel bacteria towards sources of chemical attractants. Although this system relaxes to a steady-state in response to environmental changes, the signalling events within the chemotaxis network are noisy and cause large temporal variations of the motor behaviour even in the absence of stimulus3. The fact that the same signalling network governs both behavioural variability and cellular response raises the question of whether these two traits are independent. Here, we experimentally establish a fluctuation-response relationship in the chemotaxis system of living bacteria. Using this relationship, we demonstrate the possibility of inferring the cellular response from the behavioural variability measured before stimulus. In monitoring pre- and post-stimulus switching behaviour of individual bacterial motors, we found that variability scales linearly with the response time for different functioning states of the cell. This study highlights that the fundamental fluctuation-response relationship is not constrained to physical systems at thermodynamic equilibrium4 but is extensible to living cells5. Such a relationship not only implies that behavioural variability and cellular response are coupled traits, but also provides a general framework to examine how the selection of a network design shapes this interdependence.