Multiparameter measurement and feedback systems

Soyuz and Space X capsules allow mission specialists to reach orbit. Along similar lines, well-conceived open-source control systems and high-quality state-of-the-art components give specialists the ability to construct sophisticated experimental configurations with little to no engineering expertise, bringing domain-specific knowledge closer to the laboratory equipment design process. The demand for such capabilities has resulted in the development of core control systems into which a range of high-quality state of the art sensors, actuators, and control algorithms can be inserted with minimal effort. Instead of many separate machines optimised for single measurements—such as ultraviolet to visible (UV-Vis) light spectrometers, or microscopes—these generalised frameworks allow for simultaneous measurement and control of multiple experimental parameters, under the complete direction of the end-user. Such systems are made affordable by the diverse range of extremely cheap—yet high-quality—components available on the consumer market, from supply companies such as Mouser, RS, or Digikey.

Multiparameter experimental arrangements introduce the possibility of sophisticated feedback loops to investigate coupling between several measurable quantities. By modifying an input perturbation in response to observed system behaviour, it is possible to drive the system out of equilibrium to specific steady states and actively keep it there—like balancing a pencil on its tip. The amount of energy or matter needed to maintain such a state can be tracked, allowing characterisation of the interaction between perturbed quantities and other co-monitored quantities. Although each measurement may not be as accurate as a measurement on a dedicated system, such loss of fidelity is more than compensated for by enhanced understanding of the interactions between parameters. For example, with the Chi-Bio bioreactor, Steel and colleagues [1] made use of the sensor histidine kinase—CcaS—and its cognate response receptor—CcaR—to form an optogenetic system coupled to green fluorescent protein (GFP) expression. They were able to drive the quantity of GFP in a culture of cells so it tracked an externally defined profile. The impact of the expression of GFP on other gene circuits—coupled via cellular burden—could then be monitored at distinct wavelengths with more than sufficient accuracy and precision to understand the behaviour.

Experimental feedback loops are becoming increasingly popular in other fields such as physics and chemistry.

A beautiful example of a feedback loop experiment [2]—with relevance to biologists—contributed evidence to support the topical and beguiling idea of dissipative adaptation [3,4] from the field of non-equilibrium physics, in which time-varying energy input into a system can be used to select assembly processes. Control over the assembly of a wide range of objects—including living cells—was established far from equilibrium by regulating the flow of energy supplied by a laser. Such observations are clearly of direct relevance in the behaviour of biological cells which exist in external flows of matter or energy.

In an intriguing chemical example, a feedback loop enabled external selection of the morphology of gold nanoparticles, directing their evolution [5]. A pedagogical example is shown in Fig 1, using the author’s own setup in which a dye (fluorescein) is combined with a continuous flow of water and the resulting emission is tracked to ensure the concentration stays the same.

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Fig 1. A pedagogic feedback loop for controlling dye concentration.

(A) Engineered feedback loops are extremely common and well understood for driving and monitoring physical processes. A sensor and actuator work in tandem to drive an arbitrary physical process—typically using an error-driven algorithm. (B) An example feedback loop uses spectral intensity as an input into a PID algorithm to regulate the concentration of a dye (fluorescein) in a continuous flow of water. (C) Only a narrow band of the spectrum is monitored in this setup. In principle many such bands can be monitored, allowing tracking of multiple species. (D) The emission intensity oscillates about the set-point, allowing the system to respond to impulses such as the rapid manual addition of water directly into the solution. Data acquisition was intermittent to simulate lost data. The time taken to return to the steady state behaviour after a transient impulse is known as the response time. Em, Emission; Ex, Excitation; I²C, inter-integrated circuit; LED, light emitting diode; PID, Proportional-Integral-Derivative.

https://doi.org/10.1371/journal.pbio.3000858.g001

The concept of a feedback loop is universal (Fig 1A) and could be applied at any time and length scale. The properties of the physical system dictate the specification of the necessary hardware that drives the cost of the system. Biological and chemical processes are generally, but not always, quite slow and respond on the order of seconds to hours. For a modern processor, such timescales are easy to cope with, so simple computers such as Arduinos (Arduino, Somerville, MA), Raspberry Pis (Raspberry Pi Foundation, Cambridge, UK), or Beaglebones (Beagleboard.org Foundation, Oakland, MI) have the capacity to drive biological and chemical systems with reasonably sophisticated algorithms.

Exploring picosecond chemical process—such as photoexcitation—needs femtosecond laser pulses and substantially more expensive electronics. Consequently, re-fitting engineered bioreactors, such as Chi-Bio, for use as chemical reactors is probably more likely to work for larger macromolecular polymerisations and self-assembly reactions, which can take hours or days. Such an observation is unsurprising when you consider a cell to be, in part, a macromolecular polymerization reaction network.

To go about designing a specific reactor such as Chi-Bio, or the author’s system (Fig 1B), a range of concepts need to be understood, which are explained in Box 1. Perhaps the most enticing aspect of these kinds of system is the requirement to include software as a key part of the experiment, which often incorporates a model—such as the digital twinning process described in Box 1. Indeed, such is the scientific goal of building feedback systems: If you can understand how a physical system responds to arbitrary input, and drive it to any particular state from any other state, and you know what all its states are—both numerically and in real life—is there anything left that is learnable about that system, besides pushing it into new conditions?

Comparison of systems

The Chi-Bio [1] is a great example of an open-source multiparameter control system, in which a team of engineers and specialists has taken care of a host of power, control, and mechanical engineering problems, enabling biologists (and chemists) to put together a wide range of experiments involving combinations of a broad variety of components. A comparison of Chi-Bio with the author’s own home-built feedback control system for polymer chemistry, dubbed Automated Parametric Explorer (APEX), reveals some key similarities and differences that will help provide a baseline of examples at different price points for anyone thinking about entering the space of multiparameter control systems.

Chi-Bio consists of a series of predesigned circuit boards that assemble to form a mechanical infrastructure that creates a cavity to host a monitored reaction chamber in standard reaction vessels. The control and power system service a range of sensors (spectrometer, thermometer) and actuators (light emitting diodes [LEDs], laser, fluidic pumps, stirrer) enabling interactions to occur within the reaction chamber volume. Up to 8 bioreactors can be operated in parallel from the same controlling computer (a Beaglebone).

APEX is part off-the-shelf and part homemade. It is mainly a software framework for linking together a wide variety of devices that are controlled via universal serial bus (USB) and a network of Arduinos. Each Arduino controls a specific laboratory actuator (such as an LED, a motor, pneumatic solenoids) which can be distributed around an opto-fluidic setup and hooked into a single control and power framework. The assembly in Fig 1 took less than a day to set up and begin acquiring data. It is trivial to upload complex functions to each Arduino allowing low-level commands to trigger complex actuation behaviour. Sensing is performed via standard devices—such as cameras and spectrometers—which employ conventional optics. A simple potentiostat has also been integrated. Such a setup transfers laboratory control into user-written software allowing all these devices to be incorporated into a single data-driven application.

Conclusion

As open-source systems employ ever more sophisticated architectures, the gaps between the knowledge of biologists, engineers, physicists, and chemists dwindle to occupy a similar realm, in which inert matter is pushed out of equilibrium to bring it one step closer to living material.