Engineering control into medicine

Abstract

The human body is a tightly controlled engineering miracle. However, medical training generally does not cover "control" (in the engineering sense) in physiology, pathophysiology, and therapeutics. A better understanding of how evolved controls maintain normal homeostasis is critical for understanding the failure mode of controlled systems, that is, disease. We believe that teaching and research must incorporate an understanding of the control systems in physiology and take advantage of the quantitative tools used by engineering to understand complex systems. Control systems are ubiquitous in physiology, although often unrecognized. Here we provide selected examples of the role of control in physiology (heart rate variability, immunity), pathophysiology (inflammation in sepsis), and therapeutic devices (diabetes and the artificial pancreas). We also present a high-level background to the concept of robustly controlled systems and examples of clinical insights using the controls framework.

Keywords: Artificial pancreas; Autoimmune disease; Control systems; Heart rate variability; Sepsis.

Figure 1

Control loop elements in automotive cruise control (1A) and temperature homeostasis (1B). Inner labels describe specific elements for each domain e.g. the throttle setting is the actuator in automotive cruise control. The examples highlight the similarities between the systems that maintain automotive cruise control and temperature homeostasis in humans. Figure courtesy of Yuan Lai.

Figure 2

Schematic for cardiovascular control of aerobic metabolism. Blue arrows represent venous beds, red arrows are arterial beds, and dashed lines represent controls. Four types of signals, distinct in both functional role and time series behavior, together define the required elements for robust efficiency. The main control requirement is to maintain (i) small errors in internal variables for brain homeostasis (e.g., arterial O2 saturation SaO2, mean arterial blood pressure Pas, and cerebral blood flow CBF), and muscle efficiency (oxygen extraction ΔO2 across working muscle) despite (ii) external disturbances (muscle work rate W), and (iii) internal sensor noise and perturbations (e.g., pressure changes from different respiratory patterns due to pulsatile ventilation V) using (iv) actuators (heart rate H, minute ventilation VE, vasodilatation and peripheral resistance R, and local cerebral autoregulation). Reproduced with permission from reference .

Figure 3

Heart rate HR responses (red) to simple changes in muscle workload (blue) collected from subjects on a stationary bicycle. Each subject performed separate exercises of 10 min for each workload profile, with different means but nearly identical square wave fluctuations around the mean. A typical result is shown from a subject for three workload profiles with time on the horizontal axis (zoomed in to focus on a 6-min window). Reproduced with permission from reference .

Figure 4

Low granularity view of the glucose control system (top) and a room temperature controller (bottom), emphasizing similar system structures. Controllers used in industry applications have been applied in development of the wearable artificial pancreas. Reproduced with permission from reference .

Figure 5

A simplified control device in sepsis with time moving from top to bottom. The top image displays a representation of a limited number of the many inter-related chemical and cellular factors involved in the inflammatory response. The balloons represent the individual factors with their values represented by their position relative to the orbital network. The middle image displays an example of what might be detected in an index septic patient. The control device would sense the anomalies in these values and act to restore the levels of the factors to target values, as represented in the bottom image. The target values would depend on a number of factors such as their timing in the inflammatory sequence and the nature of the inciting trigger. Figure courtesy of Yuan Lai.