Science Fare

Here is Part 2 of the conversation with Hashim Al-Hashimi, professor of biochemistry and molecular physics at Columbia University, who talks about his March 2023 paper in the Proceedings of the National Academy of Sciences (PNAS) entitled, “Turing, von Neumann, and the computational architecture of biological machines,” in which he writes about an opportunity for better understanding biological problems: seeing biological molecules as computing machines.  

We discuss:

*Quick recap of Part 1 [1:05];

*DNA polymerase and its transition states (spoiler alert: it’s like Pac-Man) [1:57];

*The different shapes, or contortions, of biomolecules can be seen as computing transition states [5:52];

*Right now in biology, there is a lot of focus on protein structure, and too little focus on the protein’s program [7:25];

*Not all computers (and therefore biological molecules) are Turing machines — computer scientists have developed a hierarchy of computers [8:27];

*The simplest machine is the finite state machine, with no external memory, and so the states are a form of memory [8:37];

*Computation as anything that follows instructions to solve a problem [10:30]; 

*Push-down automaton as the next computer in the hierarchy [11:00];

*Bounded tape computer as the next [13:00];

*How to begin building transition tables for biological molecules? Exploit the growing database of structures, to start. [14:00];

*Weakness of transition rules: they don’t include time information, critical to doing something like simulating a cell [16:00]; 

*Moving forward and building momentum around the effort to build transition tables [18:00];

*Quantum computing and its potential future role in determining transition states [19:50]; 

*Could we use this in the future to simulate complicated systems, like clinical trials, for example? [22:02];

*Relevance to a particular New York State high school science “disciplinary core idea” in the life sciences: “although DNA replication is tightly regulated and remarkably accurate, errors do occur and result in mutations, which are also a source of genetic variation,” [25:09] and how we can think about the ‘sweet spot’ of errors for evolving complexity but not harming an organism (or a computer!) [26:00];

*Hashim’s memory from high school science in Wales [32:35];

*Hashim’s advice to high school students today interested in studying science [34:30]