AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.

Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit.

Large-scale design and refinement of stable proteins using sequence-only models.

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources.

Prediction of whole-cell transcriptional response with machine learning.

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.

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion.

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization.

The Role of Configurational Entropy in Miniprotein Stability.

Predicting protein stability is a challenge due to the many competing thermodynamic effects. Through protein design, one begins with a target structure and searches for a sequence that will fold into it. Previous work by Rocklin et al.

On the anisotropy of van der Waals atomic radii of O, S, Se, F, Cl, Br, and I.

The Cambridge Structural Database (CSD) was used to obtain flattening factors to describe the overall anisotropy of nonbonding van der Waals (vdW) contacts between several main group elements. The method for obtaining the flattening factors is based on a novel minimization process. Results show that the vdW contact distances are significantly dependent on the environment and the orientations of the surrounding covalently bonded atoms: head-on vdW contacts are generally shorter than sideways contacts in overall agreement with earlier results by Nyburg and Faerman (Acta Crystallogr.