A. Nourmohammad

Variability in the local and global composition of human T-cell receptor repertoires during thymic development across cell types and individuals

The paper analyzes human TCR repertoires across thymic development stages and individuals, finding that inter-individual variability persists through maturation and that local sequence similarity correlates with cell type information.

Flexible and scalable control of T cell memory by a reversible epigenetic switch

The paper identifies a reversible epigenetic switch in T cells that controls memory potential, allowing flexible responses to pathogen virulence.

Learning the shape of protein micro-environments with a holographic convolutional neural network

This paper introduces H-CNN, a machine learning model that predicts mutation impacts on protein function using structural and evolutionary data.

Probabilities of HIV-1 bNAb development in healthy and chronically infected individuals

The paper uses sequence analysis to predict the likelihood of HIV-1 broadly neutralizing antibody (bNAb) development in healthy and infected individuals, finding that chronic infection is not required for bNAb generation.

Risk-utility tradeoff shapes memory strategies for evolving patterns

The paper introduces an energy-based framework to model memory strategies for evolving patterns, showing that moderate risk tolerance improves classification of dynamic stimuli.

Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing

This study uses deep mutational scanning to show that charge balancing constrains antigenic evolution in H3N2 neuraminidase.

Learning and organization of memory for evolving patterns

This paper develops a theoretical framework using artificial neural networks to analyze optimal memory strategies for static and evolving patterns, finding that compartmentalized networks are better for dynamic patterns.

Deep generative selection models of T and B cell receptor repertoires with soNNia

The paper uses machine learning to analyze T and B cell receptor sequences and identifies features distinguishing functional lymphocyte subsets and their selection processes.

Optimal evolutionary decision-making to store immune memory

This paper introduces a mathematical framework to model optimal strategies for immune memory storage, showing that memory specificity depends on organism lifespan and pathogen exposure.

On generative models of T-cell receptor sequences

This study compares two models for predicting TCR sequence probabilities, finding that a knowledge-guided approach outperforms a deep learning model in accuracy, interpretability, and efficiency.

Optimal evolutionary control for artificial selection on molecular phenotypes

This paper develops a feedback control framework for optimal artificial selection to guide molecular phenotype evolution by addressing evolutionary tradeoffs and improving predictive monitoring.

Fierce selection and interference in B-cell repertoire response to chronic HIV-1

The paper analyzes B-cell repertoire evolution in chronic HIV-1 infection, finding that beneficial mutations are frequently outcompeted by new mutants due to clonal interference.

The size of the immune repertoire of bacteria

The paper examines the trade-off between the size of the bacterial immune repertoire and the effectiveness of response to phage threats, showing how this limits the optimal size of CRISPR cassettes.