Learning the Lingo: New Terminology for Modern Biology

Like everything else in our modern world, biological research has changed dramatically over the last couple of decades. One of the largest shifts has been the proliferation of advanced lab equipment, new digital tools and huge data sets. Not surprisingly, the practitioners of biological R&D— biological researchers, bioinformaticians, and entrepreneurs— have started to adapt to this digital movement, transforming their industries and reimagining the role of science and technology in this modern world.


This digital revolution in biology, has ushered in a new way of thinking and talking about scientific research, although not everybody might be speaking the same common language. The colloquialisms and buzzwords that are commonplace in one group—"Industry 4.0,” for example, or “augmented intelligence”—can be completely unheard of in another group.


We’ve created a helpful list of some of the most commonly used phrases associated with this digital revolution to help you navigate the confusing jungle of science and technology terms. These definitions can be used to better understand burgeoning scientific trends or simply to impress your colleagues at the next networking event.


Industry 4.0 describes the revolution of industry through connected automation and digital technologies. It succeeds water and steam power (1.0), assembly-line production (2.0), and the first digital revolution brought about by the personal computer and Internet (3.0). Industry 4.0 is powered by artificial intelligence and networked automation (IoT; internet of things) devices, allowing autonomous manufacturing decision making from the vast amounts of process, product and environmental data streams. In biotechnology, one area we are seeing this applied is with the move to the adaptive manufacture of advanced biologics.


The Lab of the Future (also called a Smart Lab) envisions the modernization of the scientific laboratory through networked digital tools. With the increasing emphasis on data analysis comes the increased adoption of electronic lab notebooks (ELNs), lab information management systems (LIMS), computational methods, lab IoT and automated “wet lab” experiments. In this regard, the Smart Lab is the Industry 4.0 realisation of the biological research lab. In other words, less pipetting, more processing.


Engineering biology, related to synthetic biology, is an approach that leverages concepts from traditional engineering such as modularity, standardisation and characterisation  to systematically engineer biological systems. This includes harnessing existing organisms or engineering brand-new organisms to make materials and resources to transform everything from healthcare and agriculture to materials, energy, and the environment. Engineering Biology is closely related to both Computer Aided Biology (CAB) and also the Design-Make-Test-Analyse (DMTA) cycle described below.


Computer Aided Biology, our own contribution to this modern lexicon is to Engineering Biology, what CAD is to automotive engineering or electronic design automation to electrical engineering.  CAB is the modern toolkit (which we separate into two classes: the digital (advanced software powered by artificial intelligence) and the physical (automation) that realises the potential of an Engineering Biology approach.


The DMTA cycle (or the Closed Loop), which stands for Design-Make-Test-Analyse, describes the engineering approach being increasingly used to identify new and effective drugs, materials and products. This way of conducting research allows researcher to rapidly iterate on a hypothesis faster than linear research, with the rapid speed of iteration driven by automation, integrated experimental/biological design tools, and machine learning.


Augmented intelligence describes how normal human intelligence is being increasingly supplemented with technology. This is already happening in our daily lives, with everything from virtual assistants like Siri or Alexa to the smartphones in our pockets. Augmented intelligence tools put the researcher at the centre of the digital revolution in science, seeking to empower their natural capabilities and allowing them to focus on the more thoughtful, strategic parts of science instead of mindless and time-consuming busywork (here’s looking at you, Western blotting).


Active Learning. Active learning is a type of machine learning in which the algorithms are trained directly by interaction with the human user. Active learning is particularly relevant to biological research, as often there is considerable undocumented organisational or procedural know-how within a lab environment (think of those with the Midas touch in the lab). Active learning is a way to capture that expertise digitally.

These terms are only a selection of some of the ones we’ve also seen in the general community. Despite the differing nomenclature in our experience each of these phrases and the technology they represent, is focused on enabling a core theme:


“Enabling biological research, development and manufacturing, with less error and at faster speeds than can be done at present”