Machine learning methods better describe many biological systems than traditional mathematical formulations. What does this say about how biological systems are organized and how they function?
I think the deeper shift isn’t really about machine learning per se. ML is just a powerful method for tuning parameters in large models. What’s more fundamental is the move from explaining (or trying to) complex systems with tidy equations to simulating them.
This sea change is driven by our capacity to run massive simulations on modern computers. Before, with only paper, pen, and people, we had no choice but to rely on equations and closed-form solutions. Now, we can build models that work without needing them to be comprehensible or formalized in the traditional sense.
But I agree, this isn’t just a technical shift. It’s philosophical and even aesthetic. We’re moving away from the old ideal of mathematics as the perfect, transparent language of the universe to something more pragmatic and engineering-driven: make it work as best you can. Not because it’s beautiful, but because it behaves like the thing we’re studying. It’s a humbler, messier approach, but a truer one for real complex systems.
Of course, but it's a matter of how much (and to what extend this makes a difference for a specific goal).
Assuming a model is trained on a sufficient quantity of relevant data it's likely that less context will be lost as compared to common systems biology approaches, such as modeling systems with series of ODEs.
Still, context will be lost, though that's not always a bad thing. The idea that "the map is not the territory" feels relevant here. Too little context and we run into issue, but too much detail and the models would be as complex, and indecipherable, as the biological processes they aim to make more manage.
So, thank you for writing this and publishing it. Much of the greyness of biology cannot be captured in the formalisms of math, or at least not in the manner with which we switch between analytical complexity (arithmetic, algebra, statistics to calculus (stocastic statistics)). Yes, I agree that ML may be able to approximate biology better and I wonder what the final ML method will be that could capture the sum of transcriptional switches. I am thinking of something more basic such as say fetal, to embryonic to adult Hb isoforms, where limiting factors can be estimated a bit better in an engineering context. Yeah, I think I might use that today. Again, thank you.
I think the deeper shift isn’t really about machine learning per se. ML is just a powerful method for tuning parameters in large models. What’s more fundamental is the move from explaining (or trying to) complex systems with tidy equations to simulating them.
This sea change is driven by our capacity to run massive simulations on modern computers. Before, with only paper, pen, and people, we had no choice but to rely on equations and closed-form solutions. Now, we can build models that work without needing them to be comprehensible or formalized in the traditional sense.
But I agree, this isn’t just a technical shift. It’s philosophical and even aesthetic. We’re moving away from the old ideal of mathematics as the perfect, transparent language of the universe to something more pragmatic and engineering-driven: make it work as best you can. Not because it’s beautiful, but because it behaves like the thing we’re studying. It’s a humbler, messier approach, but a truer one for real complex systems.
Is the biological context lost when you decide on a limited set of training data for a model?
Of course, but it's a matter of how much (and to what extend this makes a difference for a specific goal).
Assuming a model is trained on a sufficient quantity of relevant data it's likely that less context will be lost as compared to common systems biology approaches, such as modeling systems with series of ODEs.
Still, context will be lost, though that's not always a bad thing. The idea that "the map is not the territory" feels relevant here. Too little context and we run into issue, but too much detail and the models would be as complex, and indecipherable, as the biological processes they aim to make more manage.
So, thank you for writing this and publishing it. Much of the greyness of biology cannot be captured in the formalisms of math, or at least not in the manner with which we switch between analytical complexity (arithmetic, algebra, statistics to calculus (stocastic statistics)). Yes, I agree that ML may be able to approximate biology better and I wonder what the final ML method will be that could capture the sum of transcriptional switches. I am thinking of something more basic such as say fetal, to embryonic to adult Hb isoforms, where limiting factors can be estimated a bit better in an engineering context. Yeah, I think I might use that today. Again, thank you.
Hi Jonathan, thanks for taking the time to read piece and for your feedback - I appreciate it!