ML-enhanced peroxisome capacity enables compartmentalization of multienzyme pathway.

Repurposing an organelle for specialized metabolism provides an avenue for fermentable, unicellular organisms such as Saccharomyces cerevisiae to mimic compartmentalization of metabolic pathways within different plant tissues. Peroxisomes are attractive organelles for repurposing as they are not required for yeast viability when grown on glucose and can efficiently compartmentalize heterologous enzymes to enable physical separation of cytosolic native metabolism and peroxisomal engineered metabolism. However, when not required, peroxisomes are repressed, leading to low functional capacities for heterologous proteins.

AI in cellular engineering and reprogramming.

During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function.

Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning.

Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations of 13 signaling motifs. These CARs promoted diverse human T cell fates, which were sensitive to motif combinations and configurations.

Distributed information encoding and decoding using self-organized spatial patterns.

Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns that generate high-dimensional outputs, combined with machine learning, to achieve distributed information encoding and decoding.

Intra- and interpopulation transposition of mobile genetic elements driven by antibiotic selection.

The spread of genes encoding antibiotic resistance is often mediated by horizontal gene transfer (HGT). Many of these genes are associated with transposons, a type of mobile genetic element that can translocate between the chromosome and plasmids. It is widely accepted that the translocation of antibiotic resistance genes onto plasmids potentiates their spread by HGT.

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.

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations.

We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity.

Collective colony growth is optimized by branching pattern formation in Pseudomonas aeruginosa.

Branching pattern formation is common in many microbes. Extensive studies have focused on addressing how such patterns emerge from local cell-cell and cell-environment interactions. However, little is known about whether and to what extent these patterns play a physiological role.

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.

Synthetic Pattern Formation.

A fundamental question in biology is how biological patterns emerge. Because of the presence of numerous confounding factors, it is tremendously challenging to elucidate the mechanisms underlying pattern formation solely on the basis of studies of natural biological systems. Synthetic biology provides a complementary approach to investigating pattern formation by creating systems that are simpler and more controllable than their natural counterparts.

Robust, linear correlations between growth rates and β-lactam-mediated lysis rates.

It is widely acknowledged that faster-growing bacteria are killed faster by β-lactam antibiotics. This notion serves as the foundation for the concept of bacterial persistence: dormant bacterial cells that do not grow are phenotypically tolerant against β-lactam treatment. Such correlation has often been invoked in the mathematical modeling of bacterial responses to antibiotics.

Quantifying negative feedback regulation by micro-RNAs.

Micro-RNAs (miRNAs) play a crucial role in post-transcriptional gene regulation by pairing with target mRNAs to repress protein production. It has been shown that over one-third of human genes are targeted by miRNA. Although hundreds of miRNAs have been identified in mammalian genomes, the function of miRNA-based repression in the context of gene regulation networks still remains unclear.