Chris P. Barnes

Nonlinear regime transitions enable reservoir computation in activator-inhibitor cellular automata

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

Effects of Structural Reward Shaping on Biophysical Properties in RL-Trained Plasmid Generators

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: A Promptable DNA Language Model via Verifiable-Reward Post-Training

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

Generative design and construction of functional plasmids with a DNA language model

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

Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding

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.

Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability

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

Bacterial microcompartment utilisation in the human commensal Escherichia coli Nissle 1917

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.

Engaging cancer patients on their attitudes towards microbiome engineering technologies

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

Quantifying the intricacies of biological pattern formation: A new perspective through complexity measures

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

Designing minimal E. coli genomes using variational autoencoders

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

Emergent digital bio-computation through spatial diffusion and engineered bacteria

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

DeepCNA: an explainable deep learning method for cancer diagnosis and cancer-specific patterns of copy number aberrations

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

An antimicrobial peptide expression platform for targeting pathogenic bacterial species

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: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

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.

Automated design of gene circuits with optimal mushroom-bifurcation behaviour

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.

Conflict during learning reconfigures the neural representation of positive valence and approach behaviour

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.

Deep Reinforcement Learning for Optimal Experimental Design in Biology

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

Reconstruction of Contemporary Human Stem Cell Dynamics with Oscillatory Molecular Clocks

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.

Engineered acetoacetate-inducible whole-cell biosensors based on the AtoSC two-component system

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

Single strain control of microbial consortia

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

Automated design of synthetic microbial communities

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

Model-based tumor subclonal reconstruction

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

FlopR: An open source software package for calibration and normalization of plate reader and flow cytometry data

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.

Deep reinforcement learning for the control of microbial co-cultures in bioreactors

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

Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

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

Detecting changes in the Caenorhabditis elegans intestinal environment using an engineered bacterial biosensor

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

A Bayesian framework for the analysis of systems biology models of the brain

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.

Two new plasmid post-segregational killing mechanisms for the implementation of synthetic gene networks in E. coli

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.

Reply: Neutral tumor evolution?

The paper responds to criticism of a method used to infer neutral cancer evolution from variant allele frequency distributions, arguing that neutral evolution explains intra-tumour heterogeneity in some cancers.

A computational method for the investigation of multistable systems and its application to genetic switches

This paper introduces StabilityFinder, a computational tool for analyzing multistable genetic switches, and demonstrates its application to identify design principles for bistable, tristable, and quadristable systems.