Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition

TL;DR

This paper introduces a data-driven method using deep dynamic mode decomposition to program the steady state of controlled nonlinear systems, with applications in synthetic biology for cellular computation.

Contribution

It presents a novel approach to control nonlinear systems' steady states by approximating the Koopman operator with deep learning, tailored for systems with hyperbolic fixed points.

Findings

Effective in simulation of biological networks

Handles systems with saddle points and limit cycles

Provides a structured Koopman learning process

Abstract

This paper describes the optimal selection of a control policy to program the steady state of controlled nonlinear systems with hyperbolic fixed points. This work is motivated by the field of synthetic biology, in which saddle points are common (along with limit cycles), and the aim is to program cells to perform both digital and analog computation, though developing genetic digital computation has been the main focus. We frame the analog computing challenge of generating a steady state input-output function inside living cells. To program the steady state, a data-driven approach is taken wherein an approximation of the Koopman operator, identified via deep dynamic mode decomposition, is used to describe the dynamics of the system linearly. The new representation of the dynamics are then used to solve an optimization problem for the input which maximizes a direction in state space. Some added structure on the Koopman operator learning process for controlled systems is given for dynamics that are separable in the state and input. Finally, the methods are demonstrated on simulation examples of an incoherent feedforward loop and a combinatorial promoter system, two common network architectures seen in the field of synthetic biology.