Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring mechanistic knowledge, are an ideal tool for this task.

The computational capabilities of many-to-many protein interaction networks.

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors.

The context-dependent, combinatorial logic of BMP signaling.

Cell-cell communication systems typically comprise families of ligand and receptor variants that function together in combinations. Pathway activation depends on the complex way in which ligands are presented extracellularly and receptors are expressed by the signal-receiving cell. To understand the combinatorial logic of such a system, we systematically measured pairwise bone morphogenetic protein (BMP) ligand interactions in cells with varying receptor expression.

Programmable removal of bacterial strains by use of genome-targeting CRISPR-Cas systems.

Unlabelled: CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) systems in bacteria and archaea employ CRISPR RNAs to specifically recognize the complementary DNA of foreign invaders, leading to sequence-specific cleavage or degradation of the target DNA. Recent work has shown that the accidental or intentional targeting of the bacterial genome is cytotoxic and can lead to cell death. Here, we have demonstrated that genome targeting with CRISPR-Cas systems can be employed for the sequence-specific and titratable removal of individual bacterial strains and species.