Data-driven learning of structure augments quantitative prediction of biological responses.

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement.

Building Synthetic Cells─From the Technology Infrastructure to Cellular Entities.

The construction of a living organism is a compelling vision. Despite the astonishing technologies developed to modify living cells, building a functioning cell "from scratch" has yet to be accomplished. The pursuit of this goal alone has─and will─yield scientific insights affecting fields as diverse as cell biology, biotechnology, medicine, and astrobiology.

Horizontal gene transfer enables programmable gene stability in synthetic microbiota.

The functions of many microbial communities exhibit remarkable stability despite fluctuations in the compositions of these communities. To date, a mechanistic understanding of this function-composition decoupling is lacking. Statistical mechanisms have been commonly hypothesized to explain such decoupling.

Modulation of microbial community dynamics by spatial partitioning.

Microbial communities inhabit spatial architectures that divide a global environment into isolated or semi-isolated local environments, which leads to the partitioning of a microbial community into a collection of local communities. Despite its ubiquity and great interest in related processes, how and to what extent spatial partitioning affects the structures and dynamics of microbial communities are poorly understood. Using modeling and quantitative experiments with simple and complex microbial communities, we demonstrate that spatial partitioning modulates the community dynamics by altering the local interaction types and global interaction strength.

Living fabrication of functional semi-interpenetrating polymeric materials.

Cell-mediated living fabrication has great promise for generating materials with versatile, programmable functions. Here, we demonstrate the engineering of living materials consisting of semi-interpenetrating polymer networks (sIPN). The fabrication process is driven by the engineered bacteria encapsulated in a polymeric microcapsule, which serves as the initial scaffold.

Massive computational acceleration by using neural networks to emulate mechanism-based biological models.

For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model.

A unifying framework for interpreting and predicting mutualistic systems.

Coarse-grained rules are widely used in chemistry, physics and engineering. In biology, however, such rules are less common and under-appreciated. This gap can be attributed to the difficulty in establishing general rules to encompass the immense diversity and complexity of biological systems.

Quantitative and synthetic biology approaches to combat bacterial pathogens.

Antibiotic resistance is one of the biggest threats to public health. The rapid emergence of resistant bacterial pathogens endangers the efficacy of current antibiotics and has led to increasing mortality and economic burden. This crisis calls for more rapid and accurate diagnosis to detect and identify pathogens, as well as to characterize their response to antibiotics.

Metabolic division of labor in microbial systems.

Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience.

Hacking DNA copy number for circuit engineering.

DNA copy number represents an essential parameter in the dynamics of synthetic gene circuits but typically is not explicitly considered. A new study demonstrates how dynamic control of DNA copy number can serve as an effective strategy to program robust oscillations in gene expression circuits.

Processing Oscillatory Signals by Incoherent Feedforward Loops.

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input.

Coupling spatial segregation with synthetic circuits to control bacterial survival.

Engineered bacteria have great potential for medical and environmental applications. Fulfilling this potential requires controllability over engineered behaviors and scalability of the engineered systems. Here, we present a platform technology, microbial swarmbot, which employs spatial arrangement to control the growth dynamics of engineered bacteria.