A cure. Ostensibly the goal that scientists and biotech companies are working towards, but in our minds cures are often something mythical. Like fairytales, they happened once upon a time, and can’t be produced on demand like a car or satellite. The next cure can’t be predicted; it will come like a revelation, and we can only go through the rituals we believe the cure demands. Even investors in biotech companies often have an ingrained and indefinite pessimism, a sense that gambling is an inescapable part of the endeavor.

And yet we have cures. That’s why we live for many decades. We may not remember where these blessings came from, that it took sustained scientific effort rather than prayers or good fortune. But in aggregate, our progress is impressive:

Childhood leukemia went from <10% five-year survival in the 20th century to ~90% of children being fully cured.

Each of the millions of annual childbirths in the 19th century had a ~1% risk of death, a risk now reduced by ~99%.

Before the 1920s, Type 1 Diabetes meant a single-digit lifespan. Every single patient died rapidly. Now, a near-normal life can be had for roughly the price of a new phone per year.

Medications for heart disease and stroke have reduced mortality by two-thirds since 1950, with the frontline drug combinations costing about a single restaurant meal per year.

None of these cures came about as a single stroke of genius. Each time we’ve reclaimed healthy years from disease, there was a societal decision that effort and investment were justified.

Cures are not fairytales or alien visitors. They are real, but not yet so abundant that we’re surprised when one is missing. When we go to the hospital, we still have to hope that a cure exists for whatever we’re suffering from.

Like waiting in traffic, our experience with disease is dominated by the absence of cures: when a cure exists we either avoid the disease or get treated quickly, and when it doesn’t we often live the rest of our lives with the disease. When it takes us decades to engineer effective treatments, very few people experience the needed cure arriving in their lifetime. This reinforces a sense that cures, like seasons, come in their own time.

 It’s worth asking whether a felt lack of agency is holding back our rate of progress. Knowing that we are the source of every cure, could we ask for more? In competitions, from the Olympics to Kaggle to protein folding models, it’s common that when a record is first broken, many others soon achieve the same result (sometimes in very different ways, and/or without knowing how the record was first broken). When we believe something to be possible, we find a way to do it.

 What would happen if society believed it possible to find a cure for any disease before it claimed a human life? How could we create a system that produces cures on demand, and why haven’t we done so already?

 The dominant challenge is that biology is not logically structured, genes/proteins and functions do not map one-to-one, and there is no instruction manual. A body is a complex set of interacting feedback loops, and a ‘disease’ is not a cleanly defined process but emergent behavior from misaligned complexity. We can’t predict the outcome from a given prod well until we do it. And usually we can only measure the end state, which doesn’t tell us what happened to get there. Thus a cure means shifting a system we don’t fully understand to a specific desired state.

Biology’s most successful approach so far has been reductionism – taking the system apart to understand the pieces, then deciding which piece to prod. This works when we can find a single cause of disease, like a virus or cholesterol, but is extremely slow for complex, multifactorial diseases like dementia. But we can invert the process, by bringing our experiments into the complex system and using perturbations to understand it.

Intelligence will soon become incredibly cheap, making experimental data to interpret the scarcest resource. Biotechnology has enabled us to turn each individual gene up or down and measure the cellular response, even inside a diseased organ. Scaling this to perturb every gene, and combinations of genes, in each diseased organ is a question of engineering and optimization. With access to real outcomes, we can go beyond computational models making predictions based on imperfect understanding of disease biology. And sampling the responses to enough perturbations lets us map the physiological dynamics we don’t otherwise see, like mapping the ocean currents with thousands of floating GPS trackers. This gives us enough understanding to run the right test of possible cures.

Cellular responses can tell us which perturbations shift organs in the right direction, but to know how effective they are we need to measure how the human body responds. To do this at scale we can’t rely on the slow progression of symptoms – we need to be able to do a blood draw or body scan and know whether disease is retreating. Such biomarkers don’t yet exist, but we have the technology to make them: we can perform broad measurements of factors in our blood, both before and after disease. Proper mapping of such molecular changes to specific organs, and whether they drive or respond to disease states, would make a blood draw a window into the body’s response to a new perturbation. The same measurements would also tell us when people begin to develop disease, and let cures be administered in time.

The capacity to measure thousands of factors in a single blood sample means that the data from each sample can be used for multiple purposes. Cross-applicability lets our knowledge grow synergistically rather than linearly: each sample from a human test tells us how disease responds, and new understanding and predictive models can draw on samples from the past for confirmation. Each time we test to diagnose, we also create data for predictions. And vice versa.

Computational intelligence will transform the process of finding clues and connections in biology. Millions of research papers are published, far too many for a human to read. Absorbing this entirely, and connecting it to data from biobanks, clinical trials, and perturbational experiments will enable rapid hypothesis generation. Pooled perturbations in diseased tissues enables rapid testing of these hypotheses. And mapping this to high-dimensional measurements in humans, enables rapidly finding people who need the treatment and confirming their response to it.

Like the shift from scouring the Earth for organisms with medicinal properties to engineering proteins to purpose using biotechnology, the shift described above would be a new paradigm for finding cures. One that relies less on disciplines and specialized knowledge to understand each disease, and instead on an efficient and exhaustive search process that spans diseases. Achieving this is not guaranteed, not a matter of wishing or finding a magic trick – the components above are only briefly described but each involves immense challenges. Getting there requires us to truly believe that cures can be abundant, and dedicating ourselves to that effort.