Julian's Polymath Explorations

Graph databases have an easygoing laissez-faire attitude: "express yourself (almost) however you want"... By contrast, relational databases come across with an attitude along the lines of a micro-manager:  "my way or the highway"... Is there a way to take the best of both worlds and distance oneself from their respective excesses, as best suited for one's needs? (Note: this is part2 of a 2-part series on Graph Databases and Neo4j.   For part 1, see here . This part2 is currently at a draft stage) Let's Get Concrete Consider a simple scenario with data such as the Sample, Experiment, Study, Run Result , where Samples are used in Experiments, and where Experiments are part of Studies and produce Run Results.  That’s all very easy and intuitive to represent and store in a Labeled Graph Database such as Neo4j .   For example, a rough draft might go like this:   The “labels” (black tags) represent the Class of the data – akin to table names of relational d

(UPDATED 8/2022) - Are we ready to embark on a next-generation detailed quantitative modeling of complex biological systems , including whole-cell simulations?  An anticipated up-jump in computing power may be imminent from Photonics computers (which I discuss here ), and GPU's are rapidly gaining power as well...  Are we in ready state to put existing - and upcoming - power to good use? This is a manifest, and a call to action What's Life123? It's about detailed quantitative modeling of biological systems in 1-D, 2-D and full 3-D, as well as a multi-faceted software platform for doing so. What's (pseudo-)1D?  For now, let's say it's like the inside of a long, thin tube - with no interactions with the tube.  Likewise, (pseudo-)2D can be thought of as a Petri dish, with no interactions with the lid or the bottom. Website : https://life123.science A purposeful decision to also utilize 1D and 2D But why?  Yes, it's in part about "walk before you run&quo

"You are incredibly prescient!"   I woke up to those words from a former colleague on Jan. 19, 2022: the bombshell announcement that the Chief Science Officer of pharma giant GSK, where I worked until recently, will become the CEO at the new, $3 BILLION longevity science company Altos (presumably also funded by Amazon's Jeff Bezos.) Big Pharma is at long last embracing Longevity Science. The corollary: longevity science is entering Mainstream (with capital "M") But let me backtrack... The Decade of Longevity Science When Harvard professor David Sinclair declared the 2020's to be the " decade of the paradigm shift about age reversal ", one could perhaps be dismissive of it as just an outburst of enthusiasm... But in the past couple of years, we're seeing strong evidence that his forecast is right on the mark! While I worked at GlaxoSmithKline - a giant, top-10, pharma company - I vigorously advocated forming a Longevity Science dept., and sp

[UPDATED MAY 2022]  D3.js is a very powerful visualization tool, especially for specialized/custom needs...  On the flip side, it's rather hard to use - with a steep learning curve. Even worse if one also wants interactivity ! But why is D3 so hard/clunky to use?  And what can be done about it? Spoiler alert: Vue.js (or other modern front-end framework) to the rescue - if done right... All code in the examples is available in this GitHub repository . The Root of the Problem In a nutshell, what makes D3 awkward to use is that, for historical reasons, it tries to do too much : most painfully, it uses an old way to do direct DOM manipulation (i.e. restructuring the page layout) - an operation that nowadays is superbly handled in a far more friendly way by modern front-end frameworks, such as Vue.js Document Object Model ( DOM ) is a programming interface for web documents.  In simple terms, it's the structure of the elements on a web page (text, images, etc.) Let the front-e