Creating the Architecture for a New Medicine

The full article was originally published by Brian Lewis on Medium. Read the full article here.

Systems Biology, Big Data, and Healing Healthcare

Understanding Systems Biology

To cure the diseases of the modern world, we need to shift the way we think about ourselves.

Although we like to think of ourselves as individuals, we are more of an ecosystem. We live as the expression of a complex system that is seemingly held in the confines of our skin.

Yet, looking for a definite boundary of our organism, we find only a functional boundary, not an absolute one. On the level of biology, it is now difficult to define an individual. We are an ecosystem living inter-dependently within a complex of larger systems. What we think of as an individual, emerges from the interplay of these systems.

Systems Biology is a developing field to describe, predict, and promote health within this complex web of life. It provides a way to marry advanced science with ancient wisdom for reversing the illnesses that plague modern man.

For decades Western Medicine has used a model of linear causality to address trauma, infections, and other illnesses. For acute illnesses, it has been remarkably successful.

A linear chain of events would be: Someone runs a red light, hits another car, bones are broken, align the bones, let them heal, and then back to normal life.

Yet, even underlying this simple linear example there are complex system processes which we are beginning to understand. Or in this context, what are the circumstances that led to the man running the red light?

For real world examples:

  • Was he late to his second job because he was preparing food for his child?
  • Was he caring for his spouse who is sick at home lacking the medical care that she needs?

Low wages and/or medical expenses necessitated the second job, while a lack of health coverage in their country left them overwhelmed. Looking even deeper, we also know that marital stress, elevated blood sugars, air quality, nutrition, adequate sleep, and other factors affect both decision making and wound healing. The list of influences could go on…

When we pull on a thread of a seemingly linear event, we find find a complex web of influences that can be hard to understand, and even harder to model for study. In this case, the bone will likely heal, but have we dealt with the real problem?

When we look at chronic conditions such as diabetes, obesity, and hypertension, we find that Western Medicine and our linear models fall short of success. In the U.S. 70–80% of health care expense is for chronic disease, and these conditions are rising rapidly. As many as 48% of Americans now have insulin resistance or diabetes. That’s right- nearly half. The incidence continues to worsen while we miss the BIG PICTURE.

The broader perspective of interdependence is fundamental to the field of Systems Biology. In actuality, there is nothing new here. Most traditional cultures and healing systems have held this understanding of interdependence in health for millennia. The difference is now we have powerful new scientific tools to monitor, model, diagnosis, and address these patterns.

Using this model, we can create personalized, comprehensive interventions to prevent and reverse illness. There is over 35 years of evidence showing the reversal of diabetes, obesity, early forms of cancer, and more recently, Alzheimer’s dementia. Systems Biology approaches have solid evidence for preventing and reversing many of the chronic conditions that plague modern man. Given that US health care expenses are at least 18% of GDP ($3.5 Trillion), and that 70–80% of this is for chronic disease and end of life care, this would result in a cost savings of up to $2.8 Trillion in the US alone. It would also lead to a significant happier, healthier, creative, and productive society. This cost savings could be used to fund public works, education, or when distributed among the population would be $7.3K per person. This is real impact, and worth pursuing.

Yet, Systems Biology is challenging because there are so many influences. For instance, we know that nutrition interacts directly with our genome to influence expression (nutrigenomics,) persistent organic pesticides (POPs) increase risk for metabolic disorders, stress affects our microbiome which contributes to immune dis-regulation, and so on. Compiling the list of influences into an effective intervention can be overwhelming for even the savviest of systems thinkers.

  • We need a common language to discuss these patterns and communicate our thinking.
  • We need effective models to bring clarity to interactions
  • We need large amounts of data to draw forth subtle interactions with any clinical significance.

We aren’t talking about just the car wreck. We are talking about thousands of subtle influences working in coordination. It is through understanding the patterns of health and disease that emerge from the coordination of these influences that we are able to live well. Our current linear models of health care are not only committing us to an uncomfortable state of dis-ease, they are entirely unsustainable both financially and ecologically.

There are efforts such as Lifestyle Medicine, Functional Medicine, Precision Medicine and others which are translating Systems Biology into common understanding and clinical practice. Public Health Datascience is maturing in its capacity to model, understand, predict, and apply system behavior.

Yet, all of this is likely to fail without adequate data.

We need large, diverse datasets to effectively model Systems Biology and find correlations that are both statistically and clinically significant. We will need access to personal data to translate these correlations into personalized interventions.

The data is coming soon. How we structure access will powerfully influence society in ways that are not immediately obvious.

Privately Translating Data to Healthcare

We are generating tremendous amounts of data through the electronic activities of our daily lives, though most of this data remains ‘siloed’ in central databases. At worst, this centralized data stream contains personal information then used to market our behaviors and place profit over health. How can we liberate this data for application in Systems Biology while also restoring ownership and privacy? How can we create incentives to participate that respect privacy and promote mutual benefit?

The innovation of the Distributed Ledger Technology (DLT) provides one part of the solution. This is a decentralized, transparent record of information authenticated by consensus in a network. Although the ledger may be public with many copies distributed through the network, the data may be encrypted and accessible only to those with the private encryption keys.

‘Smart contracts’ running on top of a DLT allow for another aspect of the solution. These allow for the creation of programmable transactions to form Data Marketplaces that may reveal anonymized data in exchange for reimbursement. This creates incentives to contribute to the public good while retaining both data sovereignty and privacy.

Data Marketplaces are currently in development though I would argue that only an open-source DLT Data Marketplace engenders the public trust to enable adoption. Open-source code is clearly auditable and individuals can be assured that their sensitive data is not sold through the hidden clauses of an opaque user agreement. Corporate marketplaces might have the capital to produce a compelling interface, but these will likely place profit over health. And, we can see where that has brought over 48% of Americans. For these reasons, I believe it is worth the public effort to support open-source projects to realize such a distributed data marketplace. It seems like more work, and why is it important?

It is not hyperbole to say that in coming years data sovereignty is vital to personal sovereignty.

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The full article was originally published by Brian Lewis on Medium, where people are continuing the conversation by highlighting and responding to this story.

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