Chris P. Barnes

This paper explores how activator-inhibitor cellular automata can function as reservoirs for computation by tuning nonlinear dynamics to enable memory and separability.

This paper evaluates RL and SFT for plasmid generation, finding RL achieves higher quality-control pass rates and converges to real plasmid distributions in 3-mer composition and minimum free energy density.

PlasmidLM is a DNA language model that generates plasmid constructs from component specifications, achieving high fidelity and viability in generated sequences.

This paper develops a pipeline to generate functional E. coli plasmids using a DNA language model, resulting in three validated synthetic plasmids.

This paper develops a mathematical framework to infer cancer drug resistance dynamics from genetic lineage tracing and population size data, validated using experimental evolution and single-cell sequencing.

The paper develops a computational cell-cycle model to quantify SV generation and unify genomic patterns from chromosomal instability.

This study demonstrates ethanolamine utilization and eut BMC formation in E. coli Nissle 1917 through growth assays, microscopy, and flux balance analysis, showing comparable metabolism to Salmonella enterica with distinct ammonium release.

The paper surveys cancer patients and their relatives about their attitudes toward microbiome engineering technologies for cancer treatment.

The paper introduces DNOS and DPC measures to quantify pattern diversity and complexity in nonlinear systems, demonstrating their application across multiple biological models.

This study uses variational autoencoders to generate minimal E. coli genomes, finding 6 out of 100 sampled genomes were viable in a computational model.

This study shows that high ploidy and whole genome duplication in ovarian high-grade serous carcinoma originate in STIC lesions, indicating early determination of the tumor's genomic trajectory.

This paper designs bacterial computers using spatial diffusion and engineered Escherichia coli to perform digital logic operations and complex computations.

DeepCNA identifies cancer-specific copy number aberrations across 13 cancer types using explainable deep learning for diagnosis and biomarker discovery.

The paper develops a modular platform for expressing and secreting bacteriocins from non-pathogenic E. coli to target pathogenic bacteria, demonstrating antimicrobial activity and modeling strain interactions.

CNETML infers phylogenies from spatio-temporal copy number profiles of cancer samples, handling total copy numbers and temporal data to estimate mutation rates and tree topology.

This paper presents an optimization-based method to design synthetic gene circuits with specified bifurcation diagrams, focusing on mushroom bifurcation for applications in biosensors and synthetic biology.

The paper investigates how conflicting positive and negative experiences are represented in the neural circuit of C. elegans during learning, showing that valence is flexibly encoded across neurons rather than in a single class.

This paper applies reinforcement learning to optimal experimental design in biology, showing it outperforms existing methods in bacterial growth parameter inference under parametric uncertainty.

The paper introduces rapidly oscillating molecular clocks to quantify human stem cell dynamics and finds differences in stem cell replacement rates between small intestine and colon, as well as effects of germline mutations.

The paper constructs acetoacetate-sensitive whole-cell biosensors using the AtoSC two-component system and demonstrates their functionality in E. coli.

This paper engineers a single Escherichia coli strain to stabilize microbial consortia through competitive exclusion and toxin secretion, showing tunable population composition.

This paper designs synthetic microbial communities using quorum sensing and Bayesian methods to identify stable interactions in chemostat environments.

This paper introduces a model-based subclonal reconstruction method that integrates evolutionary theory with machine learning to improve accuracy in tumor subclonal analysis.

FlopR calibrates and normalizes flow cytometry and plate reader data using a unified R package, validated on E. coli GFP expression data to identify subpopulations from bulk measurements.

This paper demonstrates the use of reinforcement learning to control microbial co-cultures in bioreactors, showing improved performance over traditional methods.

This study links dN/dS ratios to selective coefficients in somatic evolution, revealing the distribution of fitness effects for mutations in human tissues.

The paper develops a bacterial biosensor system in C. elegans to detect environmental changes in the intestinal tract using an inducer.

The paper introduces a Bayesian framework for analyzing systems biology models of the brain, showing it provides a more complete understanding of parameter spaces compared to maximum likelihood estimates.

The paper identifies two new plasmid post-segregational killing mechanisms in E. coli that enhance plasmid stability in both in vitro and in vivo settings.

This study measures single-cell division rates and survival in human cancers using multi-region sequencing data, revealing per-cell mutation and survival rates across different cancer types and healthy tissues.