# Identifying feasible operating regimes for early T-cell recognition: The   speed, energy, accuracy trade-off in kinetic proofreading and adaptive   sorting

**Authors:** Wenping Cui, Pankaj Mehta

arXiv: 1703.03398 · 2018-08-20

## TL;DR

This paper investigates the fundamental trade-offs between speed, energy consumption, and accuracy in kinetic proofreading mechanisms used by T cells for ligand discrimination, identifying a feasible operating regime where these processes are optimized.

## Contribution

It introduces a numerical simulation framework to analyze the speed, accuracy, and energy trade-offs in KPR and adaptive sorting networks, revealing a generic feasible operating regime.

## Key findings

- Existence of a feasible speed-energy-accuracy regime in T-cell recognition
- KPR mechanisms can operate efficiently within this regime
- Implications for understanding T cell receptor circuit design

## Abstract

In the immune system, T cells can quickly discriminate between foreign and self ligands with high accuracy. There is evidence that T-cells achieve this remarkable performance utilizing a network architecture based on a generalization of kinetic proofreading (KPR). KPR-based mechanisms actively consume energy to increase the specificity beyond what is possible in equilibrium.An important theoretical question that arises is to understand the trade-offs and fundamental limits on accuracy, speed, and dissipation (energy consumption) in KPR and its generalization. Here, we revisit this question through numerical simulations where we simultaneously measure the speed, accuracy, and energy consumption of the KPR and adaptive sorting networks for different parameter choices. Our simulations highlight the existence of a 'feasible operating regime' in the speed-energy-accuracy plane where T-cells can quickly differentiate between foreign and self ligands at reasonable energy expenditure. We give general arguments for why we expect this feasible operating regime to be a generic property of all KPR-based biochemical networks and discuss implications for our understanding of the T cell receptor circuit.

## Figures

9 figures with captions in the complete paper: https://tomesphere.com/paper/1703.03398/full.md

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Source: https://tomesphere.com/paper/1703.03398