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Sparse-grid-based adaptive model predictive control of HL60 cellular differentiation.

Sarah L Noble¹, Lindsay E Wendel, Maia M Donahue

¹Weapons and Systems EngineeringDepartment, United States Naval Academy, Annapolis, MD 21401, USA. noble@usna.edu

IEEE Transactions on Bio-Medical Engineering

|November 8, 2011

Summary

This summary is machine-generated.

This study introduces an adaptive model predictive control (MPC) strategy for directing HL60 cell differentiation. The method efficiently manages model uncertainties, successfully guiding cell differentiation in laboratory experiments.

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Area of Science:

Biotechnology
Systems Biology
Control Engineering

Background:

Model-based predictive control (MPC) offers powerful tools for manipulating biological systems.
HL60 cellular differentiation is a complex process that can be guided by external control strategies.

Purpose of the Study:

To develop and evaluate a sparse-grid-based adaptive MPC strategy for directing HL60 cellular differentiation.
To assess the controller's performance in silico and in vitro, particularly in the presence of model mismatch and environmental perturbations.

Main Methods:

A computationally efficient adaptive MPC scheme utilizing sparse-grid sampling and interpolation.
Identification of multiple data-consistent model parameter regions for control compromise calculation.
In silico evaluation with structural model mismatch and in vitro implementation for HL60 cell differentiation.

Main Results:

The multiscenario control strategy effectively managed model mismatch compared to a single-scenario approach in simulations.
The controller successfully achieved and sustained the target granulocyte level in laboratory experiments with HL60 cells.
The controller demonstrated robustness in both normal and perturbed cellular environments.

Conclusions:

The developed sparse-grid-based adaptive MPC is a viable technique for directing cellular differentiation.
Accurate mathematical models and extensions to multiobjective control are necessary for complex tissue engineering applications.