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Why we need better scientific models

Over the last ~4 billion years on earth, evolutionary processes acting on the transmission of DNA have given rise to a wondrous variety of life and a form of complexity that is unrivaled outside of biological systems. It is testament to human ingenuity and the scientific method that we are beginning to appreciate this complexity.

The human genome (the DNA code that is unique to each of us) consists of 6.4 billion base pairs of chemical information. Each person has tens of millions of unique copies of this code contained throughout the cells of their body. Almost all of this DNA is transcribed in to RNA – creating millions of different functional molecules in each cell that regulate every facet of the life of that cell. A smaller proportion of our DNA is then copied in to protein molecules - the molecular workhorse of biology – with vast roles ranging from structural molecules and mechanical machinery to hormones and digestive enzymes. Nature has devised numerous mechanisms to get an extraordinary variety of proteins out of the ~20,000 protein coding genes in the human genome, with an upper estimate of several billion unique protein species per genome.

These functional units (created by gene expression and the resulting reactions within the human body) self-organise in to wildly interconnected systems with hierarchical complexity - forming molecular complexes then biochemical pathways, functional networks, cells, tissues, organs, organisms and communities. Environmental signals operate at every level of this hierarchy, making things even more complex.

Our current scientific methods have only given us the low hanging fruit of biological knowledge. We have drastically increased quality of life over the last century or so, in part through medical care. However, our understanding of the biology underlying health and disease is extremely limited. Very few diseases have cures - many medical treatments just treat symptoms of a disease, and many diseases are without available treatment options or fall outside of the realm of current medical knowledge altogether.

The primary methods of bioscience to this day are still 2D cell culture and animal models. In 2D cell culture - cells are grown as a monolayer on a flat surface. This environment does not reflect how cells grow in nature. In vivo (within the living) - the 3D cellular architecture allows for an extensive network of biochemical and mechanical signalling - caused by cell to cell and cell to extracellular matrix interactions. These signalling networks instruct cell development and are critical for normal function of the tissue. Moreover, the 2D environment exposes the cells to non-physiological cues from things such as the unnatural material they are growing on, the lack of flow and uncontrolled chemical gradients.

Animal studies are often poorly predictive for humans, primarily due to the genetic differences between humans and animals. A variety of other problems exist such as the lack of human relevance of the models for inducing illness or injury as well as the measures for determining outcomes. These shortcomings are painfully clear when we consider that the vast majority (~92%) of drugs that pass in vitro and animal preclinical tests fail to pass human clinical trials and make it to market. Further, the genetic and environmental variation between humans means that adverse drug reactions still occur even after drugs have passed clinical trials – killing at least hundreds of thousands of people every year.

It is clear that we need better, human-relevant models if we are to unravel biological complexity and begin to tackle complex disease. Conventional molecular biology approaches simply do not have enough fidelity to model and decipher the dynamic, interconnected biological mechansisms at play. We need to collect and analyse more and better data - for this we need new technology based on human biology.