Realizing ‘integral control’ in living cells: how to overcome leaky integration due to dilution?

Abstract

A major problem in the design of synthetic genetic circuits is robustness to perturbations and uncertainty. Because of this, there have been significant efforts in recent years in finding approaches to implement integral control in genetic circuits. Integral controllers have the unique ability to make the output of a process adapt perfectly to disturbances. However, implementing an integral controller is challenging in living cells. This is because a key aspect of any integral controller is a ‘memory’ element that stores the accumulation (integral) of the error between the output and its desired set-point. The ability to realize such a memory element in living cells is fundamentally challenged by the fact that all biomolecules dilute as cells grow, resulting in a ‘leaky’ memory that gradually fades away. As a consequence, the adaptation property is lost. Here, we propose a general principle for designing integral controllers such that the performance is practically unaffected by dilution. In particular, we mathematically prove that if the reactions implementing the integral controller are all much faster than dilution, then the adaptation error due to integration leakiness becomes negligible. We exemplify this design principle with two synthetic genetic circuits aimed at reaching adaptation of gene expression to fluctuations in cellular resources. Our results provide concrete guidance on the biomolecular processes that are most appropriate for implementing integral controllers in living cells.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 01, 2018
Source ID
10.1098/rsif.2017.0902

Entities

People

  • Domitilla Del Vecchio
  • Yili Qian

Organizations

  • Air Force Office of Scientific Research
  • Massachusetts Institute of Technology

Tags

Fields of Study

  • Biology

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Educational Psychology

Technology Areas

  • Biotechnology