Computer-Aided Biology is the way forward as collaboration between biology and data science is set to increase
Given the drastic changes that AI and automation have brought to almost every aspect of our existence, it should come as no surprise that the way we look at (and study) life has been completely transformed as well. The thought behind Computer-Aided Design (CAD) has slowly given rise to the field of Computer-Aided Biology (abbreviated CAB), notably in Synthetic Biology, with ensuing comparisons between the two being almost completely justified. Computer-Aided Biology is essentially an emerging ecosystem of tools that augment human capabilities in biological thought and research, almost completely redefining the way that biology is thought of and taught.
Arresting the slide
The previous challenges faced by the biopharmaceutical research industry were global and un-arrested even until a few years ago. Pharmaceutical productivity in R&D had witnessed several decades of downward slide, with some research even suggesting that by 2020, the industry-wide internal rate of return on new R&D would be 0% – making any new research unsustainable.
In order to arrest the slide in productivity, the bio-research industry is exploring new methods and technologies to help accelerate its R&D. Recent innovations in automation have facilitated a new era of open-source science, driving down sequencing costs and pushing processes like screening towards much higher data output, thereby pushing biological experimentation directly into the realm of big data. We have now reached a point where the inherent complexity of biology is finally beginning to be codified in the form of large datasets from increasingly optimised experimentation.
However, it is still worth noting that the worlds of synthetic biology and engineering hadn’t quite completely merged into a sustained positive-feedback loop. Biology is a subject with a high degree of multivariate complexity, and one can argue that the integration of technology and biology wasn’t quite hitting “the foundational level of expanding and enhancing experiments to enable effective data integration and iterative design”
Until fairly recently, most of the automation technology being used – such as liquid-handling robots or electronic ‘lab notebook’ technologies – were mostly for single-factor experiments. Programming such robots was a task that required several weeks of dedicated work. Readjusting the combination of factors for a different experiment was also highly time-consuming. The advent of an integrated CAB ecosystem is set to change this drastically.
The Digital and the Physical
Although biological and medical research over the past few years has seen rapid advances, the way research is conducted has remained static. Newer methods have simply fit into existing outlines or promoted reductionist ways of working in certain aspects. Computer-Aided Biology, however, is set to change this landscape by integrating its digital and physical domains and striving towards reimagining research methods at the source.
The Digital domain is primarily an AI-powered environment meant for the designing and simulation of modelled biological systems. Its primary functions involve collation, connection, structuring and analysis of experimental data from wet-lab experiments (e.g. CAD, CAE and PLM). The Physical aspect, on the other hand, is centred around automation, facilitating the seamless transmission of digitally simulated environments into the ‘real’ world through protocol design, logistics simulation, and execution.
Integrating the physical execution of these two aspects will allow creation of tagged, connected and structured datasets – ideal for most advanced machine learning algorithms today. It will allow for experimentation at much higher levels of complexity using digital tools to rigorously explore the dynamism in biological spaces, leading to a higher volume of data that can be analysed to produce insightful insights as well as new lines of research.
Stress on the ‘why’, not the ‘how’
The primary reason why CAB is set to be ground-breaking is the fact that it is finally replacing the ‘brute force’ approach to drug discovery and replacing it with a much more nuanced and holistic means of carrying out research. Of course, it goes without saying that a radical transformation of this magnitude will not occur overnight and will have its fair share of challenges.
The adoption of CAB and new technologies associated with it will lead to a skills-shortage within workers in the days to come. Skills such as proficiency in biological research methods will be replaced with new skills in coding and data science. Of course, this may also lead to a new levelling of skilled workers within any R&D team: (1) the ‘creative thinkers’, or proven scientific experts driving research and drug discovery, (2) the ‘technicians’, responsible for the support required for the swathes of new automation equipment and AI-technologies, and (3) ‘data scientists’, who will bridge the gap between the two. Effectively, there will be a shift in mentality among research scientists – with a renewed focus on the ‘why’ instead of the ‘how’.
The development of an aggregate integrated system is a time-intensive process which, albeit gradually, will deliver returns over much longer periods. For it to function smoothly, however, there needs to be a smoother internal transmission of design files, analytical data, environment data and other information that will require higher degrees of collaboration among individuals who will now have to speak two languages: biology and data science.