In silico: When biotech research goes digital

Since the dawn of life sciences, observations and experiments were conducted on live objects and also on dead ones. The crudity of existing analytic methods made most meaningful in vivo experiments on humans increasingly unacceptable except rare cases in extremis, yet working with dead matter was evidently inadequate. Science took the first step towards modeling by resorting to animal experiments. The concept was based on the assumption of similarity of all relevant animal structures and processes with human ones, ceteris paribus. That was an assumption increasingly recognized as flawed and problematic, not least because of increasing public awareness and disapproval of quantitative as well as qualitative suffering inflicted on laboratory animals in the process, and the emergence of the concept that at least certain animate beings had recognizable rights.

But ethical issues aside, contemporary research increasingly recognized that existing models had two severe limitations: first and foremost, they differ significantly and in critical aspects from human structures and processes they were intended to approximate. Second, replication and variation of experiments is frequently and quite substantially limited by two critical factors: time and cost. As a result, live (or formerly live) models could no longer be considered valid approximations in a growing number of areas, calling for alternatives capable of bypassing these restrictions that were also able to handle a dramatic increase in complexity which is the basis of any really useful approximation.

As a result, experiments in silico were conceived, interfacing computational and experimental work, especially in biotechnology and pharmacology. There, computer simulation replaces biological structures and wet experiments. It is done completely outside living or dead organisms and requires a quantifiable, digitized mathematical model of such organism with appropriate similarities, analogies, and Kolmogorov complexity (a central concept of algorithmic information theory)[1], relying in part on category theory[2] to formalize known concepts of high-level abstractions.[3] So, always presuming availability of a high quality computational mathematical model of the biological structure which it is required to adequately represent, “executable biology” has become a rapidly emerging and exciting field expected to permit complex computer simulation of entire organisms, not merely partial structures. A pretty good digital molecular model of a rather simple cell has already been created at Stanford. Much evidence suggests that this could be a significant part of the future of synthetic biology and of neuroscience – the cybernetics of all living organisms – where a new field, connectomics, has emerged to shed light on the connections between neurons. In silico research is expected to increase and accelerate the rate of discovery while simultaneously reducing the need for costly lab work and clinical trials.  But languages must be defined that are sufficiently powerful to express all relevant features of biochemical systems and pathways. Efficient algorithms need to be developed to analyze models and to interpret results. Finally, as a matter of pragmatic realism, modeling platforms need to become accessible to and manageable by non-programmers.