Field of Science

Chandra and Johnny come close to discovering black holes

This is from Jagdish Mehra and Helmut Rechenberg's monumental "The Historical Development of Quantum Mechanics, Vol. 6, Part 2". With Chandrasekhar's facility with astrophysics and von Neumann's with mathematics, there is little doubt in my mind that they would have succeeded.


As it happened, it was Oppenheimer and his student Hartland Snyder who wrote the decisive paper describing black holes in 1939. 


The timing was bad, though; on the same day that the paper came out in the Physical Review, Germany attacked Poland and started World War 2. Far more consequential was another paper published on the same day in the same issue - John Wheeler and Niels Bohr's liquid drop model of nuclear fission.

"Hawking Hawking" and Michio Kaku

Two items of amusement and interest. One is a new biography of Hawking by Charles Seife, coming out tomorrow, that attempts to close the gap between Hawking’s actual scientific accomplishments and his celebrity status. Here's a good review by top science writer and online friend Philip Ball:


Seife's Hawking is a human being, given to petty disputes of priority and oneupmanship and often pontificating with platitudes on fields beyond his expertise. I used to have similar thoughts about Hawking myself but thought that his pronouncements were largely harmless fun. My copy of Seife's book arrives tomorrow and I am looking forward to his views, especially his take on how much it was the media rather than Hawking himself who fueled the exaggerations and the celebrity status.

The second item is an interview with Michio Kaku which seems to have ruffled a lot of feathers in the physics and science writing communities. 


The critics complain that he distorts the facts and says highly misleading things like string theory directly leading to the standard model. I hear the complaints as legitimate, but my take on Kaku is different. I don’t think of him as a science writer but as a futurist, fantasist and storyteller. I think of him rather like E. T. Bell whose “Men of Mathematics”, while highly romanticized and inaccurate regarding the details, nevertheless served to get future scientists Freeman Dyson and John Nash interested in math as kids. I doubt whether either Kaku himself or his readers take the details in his books very seriously.

I think we should always distinguish between writers who write about the facts and writers who tell stories. While you should be as rigorous as possible while writing about facts, you are allowed considerable leeway and speculation while telling stories. If not for this leeway, there wouldn't be any science writers and certainly on science fiction writers. A personal memory: my father was a big fan of Alvin Toffler's "Future Shock" and other futuristic musings. But he never took Toffler seriously as a writer on technology; rather he thought of him as an "ideas man" whose ideas were raw material for more serious considerations. If Kaku's writings get a few kids excited about science and technology the way "Star Trek' did, his purpose would be served.

Six lessons from the biotech startup world

Having worked for a few biotech startups over the years, while I am not exactly a grizzled warrior, I have been around the block a bit and have drawn some conclusions about what seems to work and not work in the world of small biopharma. I don't have any kind of grand lessons related to financial strategy, funding or IPOs or special insights, just some simple observations about science and people based on a limited slice of the universe. My suspicion is that much of what I am saying will be familiar.

1. It's about the problem, not about the technology: 

Many startups are founded with a particular kind of therapeutic area in mind, perhaps a particular kind of cancer or metabolic disease to address. But some are also founded on the basis of an exciting new platform or technology. This is completely legitimate as long as there is also a concomitant list of problems that can be addressed by that platform. If there aren't, then you are in the proverbial hammer-trying-to-find-a-nail territory, trying to be tool-oriented rather than problem-oriented. The best startups I have seen do what it takes to address a problem, sometimes even pivoting from their original toolkit. The not so great ones fall in love with the platform and technology so much that they keep on generating results from it in a frenzy that may or may not be applicable to a real problem. No matter how amazing your platform may be, it's key to find the right problem space as soon as you can. Not surprisingly, this is especially an issue in Silicon Valley where breathless new technology is often the basis for the founding platform for companies. Now I am as optimistic and excited about new technology as anyone else, but with new technological vision must come rigorous scrutiny that allows constant validation of the path that you are on and course-correction if that path looks crooked.

A corollary of this obsession with tools comes from my own field of molecular modeling and structure-based drug design. I have said before that the most important reason computational chemistry stays at the periphery rather than core of drug discovery is because it's not matched to the right problem. And while technical challenges still play a big role in the failure of the field - the complexity of biology usually far overshadows the utility of the tools - the real problem in my view is cultural. In a nutshell, modelers are not paid for saying "no". A modeler constantly has to justify his or her utility by applying the latest and greatest tools to every kind of problem. It doesn't matter if the protein structure is poorly resolved; it doesn't matter if the SAR is sparse; it doesn't matter if you have one static structure for a dynamic protein with many partners - the constant clink of your hammer in that corner office must be heard if your salary is to be justified. It's even more impressive, and correspondingly more futile, if you are using The Cloud or a whole bank of GPUs for your calculations (there are certainly some cases where sheer computing power can make a difference, but these are rare). There are no incentives for you to say, "You know what, computational tools are really not the best approach to this problem given the paucity and quality of data." (as Werner Heisenberg once said, the definition of an expert is someone who knows what doesn't work).

But it goes both ways. Just like management needs to not just allow but reward this kind of judicious selection and rejection of tools, it really helps if modelers know something about assays, synthesis and pharmacology so that they can provide an alternative suggestion to using modeling, otherwise you are just cursing the dark instead of lighting a candle. They don't need to be experts, but having enough knowledge to make general suggestions helps. In my view, having a modeler say, "You know what, I don't think current computational tools are the best way to find inhibitors for this protein, but have you tried biophysical assay X" can be music to the ears.

2. Assays are everything

In all the startups I have worked at, no scientist has been more important to success in the early stages of a drug discovery project than the assay expert. Having a well designed assay that mirrors the behavior of a protein under realistic conditions is worth a thousand computer models or hundreds of hours spent around the whiteboard. Good assays can both test and validate the target. Conversely, a badly designed assay, one that does not recapitulate the real state of the protein, can not only doom the project but lead you down a rabbit hole of false positives. No matter what therapeutic area or target you are dealing with, there are going to be few more important early hires than people who know the assays. And assays are all about the details - things like salt and protein concentration, length of construct, mutations, things only known by someone who has learnt them the hard way. The devil is always in the details, but he really hides in the assays.

3. Outsourcing works great, except when it doesn't

Most biotechs now outsource key aspects of their processes like compound synthesis, HTS and biophysical assays to CROs. And this works fine in many cases, except when that devil in the details rears his head. The problem with many CROs is that while they may be doing a good job of executing on the task, they then throw the results over the wall. The details are lost, and sometimes you don't even know you are going down a rabbit hole when that happens. I remember one example where the contamination of a chip in a SPR binding assay was throwing off our results for a long time, and it took a lot of forensic work and back and forth to figure this out. Timelines were set back substantially and confusion reigned. CROs need to be as collaborative and closely involved as internal scientists, and when this doesn't happen you can spend more time fixing that relationship than actually solving your problem - needless to say, the best CROs are very good at doing this kind of collaborative work. And it's important not just to have collaborative CROs but to have access to as many details as possible in case a problem arises, which it inevitably does.

4. Automation works great, except when it doesn't

The same problems that riddle CRO collaborations riddle automation. These days some form of automation is fairly common for tools like HTS, what with banks of liquid handling robots hopping rapidly and merrily over hundreds of wells in plates. And it again works great for pre-programmed protocols. But simple problems of contamination, efficiency and breakdowns like spills and robotic arms getting stuck can afflict these systems, especially in the more cutting-edge areas like synthesis - one thing you constantly discover that the main problem with automation is not the software but the hardware. I have found that the same caveats apply to automation that Hans Moravec applied to AI - the hard things are easy and the simple things are hard. Getting that multipipetting robot to transfer nanoliters around blazingly fast is beyond the ability of human beings, but that robot won't be able to look at a powder and determine if it's fluffy or crystalline. Theranos is a good example of the catastrophe that can result when the world of well-defined hard robotic grippers and vials meets the messy world of squishy cells, fluffy chemicals and messy fluids like blood (for one thing, stuff behaves very differently at small scale). You know your automation has a problem when you are spending more time babysitting the automation than doing things manually. It's great to be able to use automation to free up your time, but you need to make sure that it's actually doing so as well as generating accurate results without needing babysitting.

5. The best managers delegate

Now a human lesson. I have had the extraordinary good fortune of working for some truly outstanding scientists and human beings, some of whom have become good friends. And I have found that the primary function of a good manager is not to get things done from their reports but to help them grow. The best way to encapsulate sound manager thinking is Steve Jobs's famous quote - "It doesn't make sense to hire good people and tell them what they should do. We hire good people so that they can tell us what to do." The best managers I have worked with delegate important responsibilities to you, trust that you can get the job done, and then check in occasionally on how things are going, leaving the details and execution to you. Not only does this provide a great learning experience but more importantly it helps you feel empowered. If your manager communicates to you how important the task entrusted to you is for the entire company and how they trust you to do it well, the sense of empowerment this brings is enormous and you will usually do the job well (if you don't, it's a good sign for both you and your manager that things are not going well and a conversation is to be had). 

Bad managers are of course well known - they micromanage, constantly tell you what you should do and are often not on top of things. And while this is an uncomfortable truth to hear, often the best scientists are also the poorest managers (there's exceptions of course - Roy Vagelos who led Merck during its glory days excelled at both). One of the best scientists I have ever encountered wisely and deliberately stay away from senior managerial positions that repeatedly came his way. There are few managers worse than distracted scientists.

6. Expect trouble and enjoy the journey

I will leave the most obvious observation for last. Biology and drug discovery are devilishly complicated, hard and messy. After a hundred years of examining life at the molecular level, we still haven't figured it out. Almost every strategy you will adopt, every inspired idea you will have, every new million-dollar tranche of funding you will sink into your organization, will fail. No model will be accurate enough to capture the real life workings of a drug in a cell or a gene that's part of a network of genes, and you will have to approximate, simplify, build model systems and hope for the best. And on the human side, you will have disagreements and friction that should always be handle with considerateness and respect. Be forgiving of both the science and the people since both are hard. But in that sense, getting to the right answer in biotechnology is like building that "more perfect union" that Lincoln talked about. It's a goal that always seems to be one step beyond where you are, but that's precisely why you should enjoy the journey, because you will find that the gems you uncover on the way make the whole effort worth it.