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.

The Power of Data

This understanding requires going down the rabbit hole a little bit.

First, we need to clarify our goal. We hope to create a platform that allows modeling complex system behavior for the sake of:

Revealing unknown causes of illness

Updating diagnoses to address the underlying causes, rather than clusters of symptoms

Creating personalized, comprehensive interventions for individuals

Guiding public policy

Evaluating the efficacy of interventions and policies

This complex modeling require enormous amounts of data and computing power to significantly correlate influences that would be actionable for intervention or policies. While access to these resources could be applied to great good, they could also be used in unhealthy, negative ways.

To begin, we need to understand the concept of your Digital Twin. It is a useful term to understand individual privacy and sovereignty. To many companies, you and your data have become their product that they sell to marketers. You exchange your private data for convenience or the sense of a personalized service. They collect this data through various streams and create a composite virtual image of you. The amalgamation of your data becomes your digital twin and learning algorithms applied to your twin can both predict and influence your behavior.

Currently most of your internet behavior, purchase history, voting history as well as other actions and preferences are stored in central databases. This information is sold by social media and other platforms to their clients or third party affiliates for ‘marketing purposes.’

With this information, analysis of your digital twin predicts how you will respond to different situations and stimuli. By exposing you to these stimuli through articles, ads, and personalized feeds, it is then possible to drive not only your behavior, but also your very perceptions and values. You are the expression of the interplay of complex systems, and through deep learning algorithms we are able to pull the strings of influence below the level of your conscious perception. What world does this create?

The more information that is available, the more valuable the twin. Through machine learning, this portrait of meaning, preferences, and values becomes a very powerful tool of targeted influence. It is so accurate that programs such as IBM Watson or Facebook can likely predict you better than close family members. This is not just to provide products that ‘better fit your needs’ but can also influence your values and preferences to align with the goals of those in control of your data. I recommend that you check out the goals of the Personality Insights arm of IBM Watson. Then think about what firms such as Cambridge Analytica might be doing. Since it is possible to access this volume of personal information, it is likely that most corporations and political groups will uses these tools to stay competitive.

It will be up to us to remove this tool of manipulation by regaining our data and identity sovereignty.

So who owns your Digital Twin? Where does it live, and who has access to it? As the internet of things (IOT) matures, there is even more detailed information streaming from our devices. This will be amplified significantly if we upload our personal health data. If your sibling or parent knows just the right thing to say at dinner to ‘light your fire,’ what does Watson or others with access to this technology know about influencing you?

We now hear corporates champion the phrase ‘Data is the New Oil.’ When we think about the influence of oil on our current world, we then realize that in the coming world ‘Data is Power.’ We need to be careful that this power remains distributed and aligned for public good.

I think I have made the point that privacy in the age of Big Data is a Big Deal. Most people don’t really mind if Netflix has scoured our data to create a formula for an addictive series. We might hesitate if the Facebook ads somehow reflect the conversation that you were having in your living room. It may seem intrusive, but many of us are willing to accept this for the convenience of the product and the personalized experience.

However, more people begin to wake up when they realize that this same technology can be used to affect our perceptions, beliefs, and agency. And yet, others might only be interested in data sovereignty if there is a financial incentive. The effects of this manipulation are already evident in modern politics, and there is an urgent need for both public education and discourse. I recommend that collectively we look into ways of reclaiming our data streams and Digital Twins for even larger reasons than realizing the potential of Systems Biology.

A Solution

I propose creating a platform enabling a Digital Encrypted Anonymous Twin (DEAT) to steward our personal details. Yes, a DEAT for your ‘DEETS.’ The pun is intentional. The architecture of this platform is fairly simple and for this example I will use the IOTA protocol .

For a basic overview of the IOTA ecosystem:

The IOTA Protocol is an open-source, partition-tolerant, distributed ledger that enables fee-less microtransactions. IOTA serves as the transaction protocol for a second layer known as Qubic that allows for ‘smart contracts,’ distributed computations, and quorum computations as explained below. Custom modules known as ‘IXI modules’ may interface with Qubic to provide services. Finally, it has an open-source Data Marketplace in development that would enable financial incentives for participation. (www.iota.org)

Any open-source protocol with these features would serve our purposes of:

Providing encrypted, resilient storage of personal data

Allowing free transaction of data while preserving privacy

Distributing computations to outsource difficult processes

Enabling quorum computations to ensure data integrity

Creating incentives for sharing data

In the IOTA protocol, information is stored in an encrypted format on a distributed ledger called ‘The Tangle.’ The primary Tangle which we might call T(0) may have sub-Tangle partitions or clusters T(1-x) which exist independently and may interact with the primary Tangle T(0) based on defined rules. In layman’s terms, you can have your own encrypted, distributed database that interacts with a larger distributed ledger.

All of your personal IOT devices would serve as either edge nodes or full nodes on your network while transmitting their data throughout your personal sub-Tangle T(you.) Only you would control access to the unencrypted information on T(you) through the use of your private key. Your Digital Encrypted Anonymous Twin (DEAT) would be the combination of T(you) and the following components: