Summary The Bone Morphogenetic Protein (BMP) signaling pathway comprises multiple ligands and receptors that interact promiscuously with one another, and typically appear in combinations. This feature is often explained in terms of redundancy and regulatory flexibility, but it has remained unclear what signal processing capabilities it provides. Here, we show that the BMP pathway processes multi-ligand inputs using a specific repertoire of computations, including ratiometric sensing, balance detection and imbalance detection. These computations operate on the relative levels of different ligands, and can arise directly from competitive receptor-ligand interactions. Furthermore, cells can select different computations to perform on the same ligand combination through expression of alternative sets of receptor variants. These results provide a direct signal processing role for promiscuous receptor-ligand interactions, and establish operational principles for quantitatively controlling cells with BMP ligands. Similar principles could apply to other promiscuous signaling pathways.

Genetic engineering technology has become sophisticated enough to allow precise manipulation of bacterial genetic material. Engineering eorts with these technologies have created modied bacteria for various medical, industrial, and environmental purposes, but organisms designed for specic functions require improvements in stability, longevity, or eciency of function. Most bacteria live in multispecies communities, whose composition may be closely linked to the eect the community has on the environment. Bacterial engineering eorts will benet from building communities with regulated compositions, which will enable more stable and powerful community functions.We present a design of a synthetic two member bacterial community capable of maintaining its composition at a dened ratio of [cell type 1] : [cell type 2]. We have constructed the genetic motif that will act in each cell in the two member community, containing an AHL-based negative feedback loop that activates ccdB toxin, which caps population density with increasing feedback strength. It also contains one of two ccdB sequestration modules, either the ccdA protein antitoxin, or an RNA device which prevents transcription and translation of ccdB mRNA, that rescues capped population density with induction. We compare absorbance and colony counting methods of estimating bacterial population density, nding that absorbance-based methods overestimate viable population density when ccdB toxin is used to control population density.Prior modeling results show that two cell types containing this genetic circuit motif that reciprocally activate the other's ccdB sequestration device will establish a steady state ratio of cell types. Experimental testing and tuning the full two member community will help us improve our modeling of multi-member bacterial communities, learn more about the strengths and weaknesses of our design for community composition control, and identify general principles of design of compositionally-regulated microbial communities.

As studies continue to demonstrate how our health is related to the status of our various commensal microbiomes, synthetic biologists are developing tools and approaches to control these microbiomes and stabilize healthy states or remediate unhealthy ones. Building on previous work to control bacterial communities, we have constructed a synthetic twomember bacterial consortium engineered to reach population density and composition steady states set by inducer inputs. We detail a screening strategy to search functional parameter space in this high-complexity genetic circuit as well as initial testing of a functional two-member circuit.We demonstrate non-independent changes in total population density and composition steady states with a limited set of varying inducer concentrations. After a dilution to perturb the system from its steady state, density and composition steady states are not regained. Modeling and simulation suggest a need for increased degradation of intercellular signals to improve circuit performance. Future experiments will implement increased signal degradation and investigate the robustness of control of each characteristic to perturbations from steady states.

It has been an ongoing scientific debate whether biological parameters are conserved across experimental setups with different media, pH values, and other experimental conditions. Our work explores this question using Bayesian probability as a rigorous framework to assess the biological context of parameters in a model of the cell growth controller in You et al. When this growth controller is uninduced, the E. coli cell population grows to carrying capacity; however, when the circuit is induced, the cell population growth is regulated to remain well below carrying capacity. This growth control controller regulates the E. coli cell population by cell-cell communication using the signaling molecule AHL and by cell death using the bacterial toxin CcdB.To evaluate the context dependence of parameters such as the cell growth rate, the carrying capacity, the AHL degradation rate, the leakiness of AHL, the leakiness of toxin CcdB, and the IPTG induction factor, we collect experimental data from the growth control circuit in two different media, at two different pH values, and with several induction levels. We define a set of possible context-dependencies that describe how these parameters may differ with the experimental conditions and we develop mathematical models of the growth controller across the different experimental contexts. We then determine whether these parameters are shared across experimental contexts or whether they are context-dependent. For each of these possible context-dependencies, we use Bayesian inference to assess its plausibility and to estimate the growth controller's parameters assuming this context-dependency. Ultimately, we find that there is significant experimental context-dependence in this circuit. Moreover, we also find that the estimated parameter values are sensitive to our assumption of a context relationship.