Synthetic consortia represent an emerging research area in synthetic biology, promising to solve various industrial challenges through the metabolic diversity, division of labor, and spatial organization inherent in microbial consortia. As synthetic biology advances into multi-cellular systems, new design strategies are essential for engineering distributed functions across networks of cells. However, existing strategies either lack scalability or require extensive reformulation, limiting their usage to various applications. In this work, we propose an application-agnostic approach to partitioning networks of interacting biological components using graph-based algorithms. We develop a three-stage algorithm, named "Oriole", that verifies and optimizes the subgroup distribution of all entities within a network, considering biological constraints and objectives for engineering these systems. We validate our algorithm on three types of networks, including 30 small-graph benchmarks, 537 regular electronic circuit designs, and 56 large circuit benchmarks. One large circuit benchmark was recently implemented experimentally. Compared with the other sub-graph partitioning solutions, the results generated by our new algorithm reduced design time from days to hours and decreased the total number of cells required for the multi-cellular system by 3%. This case study demonstrates that our algorithm provides a more efficient approach to designing multi-cellular systems.

MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS: The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION: The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Digital electronic circuits have inspired synthetic biologists to program living cells with synthetic decision-making circuits by creating multilevel genetic logic gates. In both genetic and electronic circuits, logic synthesis translates an abstract functional description into a representation that can be physically implemented. However, inherent differences in genetic logic devices present new synthesis challenges. Here we provide a starting point for a growing set of biodesign automation tools to tackle this challenge in a unified way. This framework will enable direct comparison of new approaches, results that are transferable and standardized, and research into independent areas of the problem formulation with a clear path toward future integration.

With the growing complexity of synthetic biological circuits, robust and systematic methods are needed for design and test. Leveraging lessons learned from the semiconductor and design automation industries, synthetic biologists are starting to adopt computer-aided design and verification software with some success. However, due to the great challenges associated with designing synthetic biological circuits, this nascent approach has to address many problems not present in electronic circuits. In this session, three leading synthetic biologists will share how they have developed software tools to help design and verify their synthetic circuits, the unique challenges they face, and their insights into the next generation of tools for synthetic biology.