NOTES

The leaves are falling in the northeast, which can only mean one thing—it's open enrollment season. Time to wade through excruciating details in order to choose the level of health coverage that you deem necessary, covering every possible (high) price point. Why do people in the United States do it this way?

Notes on Engineering Health

The combination of years of “Big Data” hype and obviously flawed inferences, of overpromising and under-delivering, has led to pervasive online tracking and a miasma of distrust. It is simultaneously too difficult to deploy novel consumer-facing information technology and avoid the sale or at least use of personal information.

Engineering Biology

It is particularly easy these days to find ourselves focusing on our differences as humans—different political beliefs, cultures, religions, and diets, to name just a few. However, one thing almost all of us share across the globe is interaction with animals.

Notes on Animal Health

The story goes that an angry father confronted Target employees after his daughter was mailed coupons for maternity products unnecessarily, only to find out later that she was pregnant. A triumph of big data combined with statistical learning, and a creepy portent of the future, right? That’s how the story went at least.

Engineering Biology

In the US, the number of people alive aged 65 or older increased from 4.9 million (4.7% of the total U.S. population) in 1920 to 55.8 million (16.8%) in 2020. While this demographic shift is a testament to progress in healthcare, and a decrease in fertility rate, it also presents unique challenges, including the increasing prevalence of frailty among older adults…

Notes on Engineering Health

My focus in writing over the past three months has been the interplay between powerful new computational methods, digital technologies, and operational processes. It began with the observation that successful Machine Learning (ML) integrated biopharma companies have a moat in data generation and the scientific application of computation to these data—not in machine learning itself. Operational excellence is requisite for these companies, not merely a nice-to-have.

Engineering Biology

From the organism to the organ, the cell to the organelle, the molecule to the atom, biologists have descended into the living matter not only describing it but seeing and photographing it. Whether to convince by their explanatory power or compel by their beauty, images of biological phenomena have transformed how we approach the field.

Notes on Engineering Health

I was on a panel about digitization and the data revolution at the annual Academy of Management meeting last weekend. My co-panelist and I were there to give an operational perspective on how data are used in biopharma for everything from R+D to commercialization and how it compared to the empirical studies from a variety of industries presented earlier in the session.

Engineering Biology

The integration of Machine Learning (ML) into scientific work exists on a continuum between whole-scale replacement of human processes and providing inputs to complement the judgment of a human arbiter. As I’ve argued previously, current models are insufficient at best for fully substituting human knowledge in biology for all but base-level tasks…

Engineering Biology

While we have written about other aspects of climate change in these notes before, we have not yet addressed the core aspect of climate change: the heat itself. What are the consequences of too much heat on humans, animals, and societies? In other words, how hot is too hot?

Notes on Engineering Health

We have heard stories about how computation and Machine Learning (ML) are poised to change medicine for well over a decade now. Conferences, press reports, promising results – yet little has changed…

Engineering Biology

“On the Diseases and Cures of Women” is considered the oldest surviving medical text by a woman. Written by Metrodora (meaning gift of the uterus), a physician and medical writer, the document dates from the 5th century AD and is kept in the Laurentian Library…

Notes on Engineering Health

The past weeks have seen a flurry of articles debating the efficacy (and proof thereof) of “AI” in drug discovery and biotech writ large, kicked off by a large layoff at Benevolent, an “AI” drug developer. I would argue the lesson of the recent AI boom in biopharma is a simple one: If you don't have novel and effective science in the first place, no amount of data science will save you: Data Science and Machine Learning* (hereafter ds/ml) will be most successful in biology where they sit atop transformative science that needs no special analytics.

Engineering Biology

Back in 2017, when I was just starting to build out data science at Indigo, Tristan Bepler joined us as a summer intern. We had a large and growing amount of sequencing data from microbial communities both their composition from marker genes and whole genomes of organisms of interest. Both of these datasets resisted conventional methods. The mathematical modeling of microbial communities remains underdeveloped with heuristic methods that produce nonsense and potentially more correct ones that are difficult to implement.

Engineering Biology

I’m excited to announce I’m joining Digitalis Ventures as an Entrepreneur-in-Residence. The arc of my career has been driven by a fundamental belief that we can identify emergent simplicity from the complexity of biology, provided enough data and algorithms that model the underlying science. At each step along the way, however…

Notes on Engineering Health

“Xenotransplantation is the future, and always will be”. The apocryphal quote by the late Stanford surgeon and heart transplantation pioneer Norman Shumway highlights the hopes and difficulties of this endeavor. Xenotransplantation, or transplanting an organ from one species to another, concentrates many biological challenges and some thorny ethical considerations.

Notes on Engineering Health

The hope with Machine Learning has long been that we can eliminate complex, slow, and expensive physical processes with accurate predictions, inferred directly from data. As I’ve written previously, the complexity and scarcity of data make supervised learning like this less relevant in problems of biology. Unlike internet companies, generating reams of labeled data daily, our experimental throughput is orders of magnitude lower and our data modalities considerably more complex.

Engineering Biology

Where does Machine Learning belong in biology? Nearly all successful efforts fall into one of three categories: Exploration—Summarizing large complex datasets that cannot be fathomed by the human mind: gene sequences, chemical structures, images, etc and enabling scientists to explore them. Scaling—Automating, standardizing, and debiasing heuristics and calculations. Prediction—Estimation of how a complex process will perform on a new element.

Engineering Biology

Decisions in times of crisis can have long-lasting effects on an entire system, and be hard to change when incentives created by short-term decisions then become deeply entrenched. One perfect example of this phenomena is the health insurance system in the United States. Why does your employer pay for your health insurance? Is this a good thing?

Notes on Engineering Health

How do we build models under resource constraints? We almost never have enough data to adjust for every possible confounding factor, nor do we know what all those factors are.

Engineering Biology

We don’t know what the hard problems are going to be. Most of us were trained as academic scientists in a culture of finding winding paths through the dark forest of the unknown. Today, we are much closer to engineers — using data and computation to industrialize the production of knowledge. Biology presents an endless series of learning and inference problems for us to solve.

Engineering Biology

“The role of the infinitely small in nature is infinitely large.” Louis Pasteur. Fermentation is the first example of biotechnology in human history. While spontaneous fermentation predates the human species, the earliest human efforts at fermentation date from the Neolithic period (7,000 to 1,700 BCE)…

Notes on Engineering Health

How does data science fit into the biopharma tech stack? The analytical operations involved are certainly more complex than the transformation and aggregation of data. This might suggest that data science is an artisanal, intellectual operation built off of the core data repository; in essence, an extension of laboratory science to computation. While tempting, this pattern only leads to confusion, frustration, and a misuse of human and silicon capital. Just as we are industrializing biological discovery and drug development, so must we with data science.

Engineering Biology

Technology in a biopharma company tends to grow by accretion rather than design. Tools and systems are brought in house as functions are brought on line. LIMS comes with the establishment of a lab, a compound registry with the first experiments with small molecules, a chem informatics tool when it’s time to start digging into SAR. Growth reflects staffing and capabilities — much as you don’t hire a medicinal chemist until it’s time to design small molecules, you don’t bring in the systems they would use until the function is present

Engineering Biology

The subject du jour is undeniably the emergence and rapid progress of generative AI models such as ChatGPT. While a lot has already been written on the topic, both by humans and robots, the increasing role of AI…

Notes on Engineering Health