Very soon, it will be economically feasible to sequence human genomes and collect massive amounts of differenttypes of health data as standard medical practice. Already, there areremarkable examples of how these new genetic data are changing our thinkingabout disease and diagnosis.

Consider the Beery twins, born in 1996 in San  Diego, California.They suffered from chronic vomiting, seizures,and muscle weakness, sending them and their parents on an odyssey of medical examinations and tests. Thefirst diagnosis was cerebral palsy. Thenthey received a diagnosis of dystonia, arare neurological disorder. But treatmentsbased on these diagnoses did not alleviate the children’s symptoms.

Frustrated, their parents had the twins’ genomes sequenced. The resultsrevealed that the twins had been incompletely diagnosed. Their previouslydiagnosed dystonia was being caused by a genetic mutation that was interferingwith the neurotransmitter serotonin. Thetwins’ doctors found that the dystonia could be fully treated with a readilyavailable serotonin replacement.

So why haven’t success stories like that of the Beery twins, together withthe Internet’s power and increasingly affordable collection of molecular data,led to the construction of a knowledge network ofdisease? Why aren’t scientists and doctors turning in droves to data-intensivescience in order to build better “disease maps”?

One possible answer is that there are stilltechnical barriers that block the construction and use of such networks.With our ability to generate ever-rising oceans of molecular data – nowapproaching the zetabyte scale (that’s a onefollowed by 21 zeros) – comes the challenge of storing and deciphering this information. But scientists andsoftware engineers regularly face such daunting challenges, and, with DNAserving as the reference language of modern biomedical research, the technicalbarriers to constructing disease networks will be short-lived.

Cultural barriers are the real stumbling block. Ashumans, we are highly evolved to adjust to our surroundings: we tend to adaptto a culture, well-conceived or not, and lose sightof its failings. But when we glimpse an alternative, our culture’sinadequacies (and even insanities) are immediately apparent, which may prompt acultural shift, collective action, and change. The fall of the Berlin Wall inNovember 1989 and, more recently, the Arab Spring are clear examples of thisdynamic.

Similarly, the example of the Beery twinsshows us that an alternative to symptom-based medicine can be realized:the advent of genomics technology can changenot just what is known, but, more importantly, how we think of ourselves.

But, in order to build the disease networks oftomorrow, we will need to move beyond the currentlinear approaches to science and to how scientists work. We all like agood story that unfolds in a straightforwardway, but the story of disease plays out across apoly-nodal information network, similar to what an air trafficcontroller might track in the skies above a major airport. Biomedicalresearchers’ “lock and key” and linear-pathway representations are incomplete,and should be supplemented with disease maps that can now be built usingmolecular data.

We must also build the infrastructure and cultivate therelationships needed to share disease maps with basic researchers, practicingdoctors, drug developers, and even the public at large. And that could prove to be even more difficult, because the current closednature of the medical-information system and its self-directed incentivestructure block such sharing. Patents, trademarks, and competition forresources (people, money, and accolades) seal off information and prevent molecular datafrom being analyzed and shared. Rewards in biomedical research go to “soloworkers,” and do nothing to acknowledge the work that can be done only bymulti-functional groups.

Despite these imposing obstacles to progress, there are reasons to believethat a cultural shift is afoot: researchers from geographically distant labs are formingnon-traditional “federations” to combine their data sets, work on themcollaboratively, and post the results for other scientists to analyze.Crowd-sourced competitions like DREAMChallenges and FoldIt show thatimportant scientific findings can emerge from outside of universities andpharmaceutical companies. And public-private partnerships between drugdevelopers, basic researchers, and patient groups that share informationpre-competitively (that is, with no or limited patent filings) are anincreasingly popular way to translate scientific findings into potentiallymeaningful clinical benefits.

But a few successful federations, competitions, and partnerships may notbe enough to transform biomedical research. Disruptivechange may be required, and here each of us can make a profounddifference. Patient groups are already organized; their members can reporttheir symptoms online and self-enroll in clinical trials. We can already obtainportions of our own genetic information and use it to make informed medicaldecisions, join existing patient groups, or create new ones. We can provide ourgenetic samples to data-driven trials to learn about our likelihood to respondto particular therapies. We can even organize to self-fund future studies orjoin only those studies that give us the legal right to say how and where ourdata are used.

In other words, patients can and should stopbeing the passive “sick” and actively engage to pressure clinicians,researchers, and drug developers to adapt or perish. Democratized medicine represents the fullestflowering of the biomedical information revolution. There are few worthiergoals than a future in which citizen-patients are active participants inmanaging their own health.