Theoretical Immunology Group

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  Modeling cell-state continua using Probabilistic fate mapping & Gen AI

  We are building an integrative approach that synthesizes language models (LSTM, Transformers, RNN, etc.) and variational autoencoders (VAE) to build a statistical framework to infer parameters from mechanistic mathematical models. We will use this to gain insights into B and T cell expression dynamics and differentiation trajectories using data from single-cell RNA and genomic sequences collected during immune responses.

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  Space-time logistics of immune repertoire diversity

  The numbers of T and B lymphocytes are dynamically regulated by the processes of death, division, and differentiation across our lifespan. As a result, the breadth and the size of lymphocyte populations continuously evolve as we age. Our aim is to quantify the processes that govern the ecological distributions of lymphocytes and, in doing so, identify the mechanisms that establish and maintain our immune repertoires.

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  Lineage tracing to map B cell differentiation pathways

  Immune activation induces seismic changes in the lymphatic environment and exposes lymphocytes to a wide variety of interactions and stimuli, which dramatically alter their dynamics. We aim to understand how and to what degree the crosstalk between antigenic signals and environmental cues affects the fate decisions of responding B cells and their clonal trajectories.

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  Conventional and regulatory T cells in antitumor immunity

  The dynamics of diverse T cell subsets within non-lymphoid tissues are unclear, and therefore the intensity and longevity of the tumor-specific T cell response in several cancers remain largely unknown. Our goal is to combine experimental data with mathematical modeling to track the subset composition of intratumoral T cells in order to pinpoint the mechanisms that govern tumor tolerance or rejection.

• A linear ontogeny accounts for the development of naive, memory and
  tumour-infiltrating regulatory T cells in mice

  S Pathak, T Hogan, S Rane, Y Huang, C Sinclair, S Barry, L Carnevalli, A Yates, B Seddon bioRχiv

  Under review in Science Immunology

• Notch2 controls developmental fate choices between germinal center and
  marginal zone B cells upon immunization

  T Babushku, M Lechner, S Ehrenberg, U Rambold, M Schmidt-Supprian, A Yates, S Rane, U Zimber-Strobl, L Strobl Nature Communications 2024

• Towards a unified model of naive T cell dynamics across the lifespan

  S Rane, T Hogan, E Lee, B Seddon, AJ Yates eLife 2022

• Cell generation dynamics underlying naive T cell homeostasis in adult humans

  Jeff E Mold, Pedro Réu, Axel Olin, Samuel Bernard, Jakob Michaëlsson, Sanket Rane, Andrew Yates, Azadeh Khosravi, Mehran Salehpour, Göran Possnert, Petter Brodin, Jonas Frisén PLoS biology 2021

• Fate mapping quantifies the dynamics of B cell development and activation throughout life

  M Verheijen, S Rane, C Pearson, AJ Yates, B Seddon Cell reports 2020

• Spatiotemporal modeling of the key migratory events during the initiation of adaptive immunity

  Alan J Hayes, Sanket Rane, Hannah E Scales, Gavin R Meehan, Robert A Benson, Asher Maroof, Juliane Schroeder, Michio Tomura, Neil Gozzard, Andrew J Yates, Paul Garside, James M Brewer Frontiers in Immunology 2019

We collaborate both locally and globally with experimental immunology labs. The B cell study is primarily a partnership with the Schmidt-Supprian and Zimber-Strobl groups at Technical University Munich. The Seddon lab at UCL London collaborates on T cell research. We are closely connected with Yates and Tavare labs at Columbia University and the Kleinstein group at Yale University.

Dr. Sanket Rane (PI) is a theoretical immunologist trained in deterministic and probabilistic mathematical modeling, inference-driven Bayesian statistical methods, and cell biology techniques. Sanket earned his PhD in immunology at the National Institute of Immunology, India, for investigating molecular and cellular factors that govern T cell numbers and function during healthy aging. He transitioned to using computational approaches to study lymphocyte ecology during his postdoctoral years at the University of Glasgow and Columbia University.

Dr. Apoorva Singh is a postdoctoral research scientist. She completed her PhD in mathematics studying operator theory and functional analysis. She is interested in developing mathematical models to understand the dynamics of immune cells in health and disease. Her current research interests lie at the interface of applied mathematics, statistics, and immunology, with a focus on developing and refining computational tools to investigate immune system dynamics and better understand human immune responses.

Jun Won. Jun is working on building an innovative deep learning framework to address ill-posed Bayesian inverse problems He mainly writes statistical and programming routines to effciently connect probabilistic models to high-dimensional sequential data. In Rane lab so far, Jun has investigated early B cell development in the bone marrow, employing Bayesian statistical modeling to gain insights into their cellular dynamics.

Puqi Song, Columbia University (Statistics). Puqi uses Python and R to visually analyze the biological data of T cells and studies the characteristics of TCRS at different ages over different time. She tries to establish mathematical models and visual descriptions that could describe T cell behaviors (such as proliferation, death, and differentiation).

Allison Pascual, Stony Brook University (Biology), IICD Summer Research Program 2022. Allison developed a mathematical modeling framework to identify cellular processes that regulate the age-associated accumulation of Fas (an apoptotic marker) expressing T cells. Fas expression in T cells increases during healthy aging, but this increase is even more extreme in patients suffering from head and neck cancer. Allison built mechanistic models to delineate the effects of age on the rate of conversion of Fas negative to positive T cells and/or on their net loss rates, in an effort to understand their preferential enrichment with age.

Julia Rehring, University of Michigan, Ann Arbor (Mathematical Biology, minor in Computer Science), IICD Summer Research Program 2023. Julia is investigating plasticity in B cell differentiation trajectories during immune responses by combining mathematical modeling with data derived from a novel antigen-inducible reporter mouse model. She employs Bayesian statistical approach to validate models exploring time and/or density dependent variation in B cell generation or their loss.

Jiayi Dong, Columbia University (Data Science). Jiayi worked on incorporating transformer architecture with variational autoencoder (VAE) in reconstructing the parameters for complex biological models. He explored a variety of machine learning models to extract spatial information from biological datasets.