Cool! I used to work on molecular dynamics of viral proteins through computational simulations in undergrad, but had never heard it be called theoretical biology before haha.
Cool! There’s probably a small factor differentiating the two, but it’s not that noticeable.
I did a research project looking at (iirc) kinase cascades, in which we were using a molecule-by-molecule simulation to look at cascading signals in hypothetical signaling networks, and varied the levels of phosphorylation required for activation required at each tier, and showed how the different topologies/rules governed the relationship between input and output signals, and their relationship to noise tolerance (since chemical networks can be quite noisy). It was very abstract in that we weren’t reconstructing known networks, but rather using sandbox physics to explore the idea.
During my graduate research, our lab space was next to the cell modeling department and I would catch a talk here or there. Always found it a super interesting approach because it really tries to make sense of what we’ve learned from traditional biology and generates really nice hypotheses/theories for testing out in biological models. I also love how you can apply so much abstract mathematics to biological systems for biologically meaningful findings. Most of these types of cell modeling papers go above my head, but I still really appreciate them from outside.
Cool! I used to work on molecular dynamics of viral proteins through computational simulations in undergrad, but had never heard it be called theoretical biology before haha.
Cool! There’s probably a small factor differentiating the two, but it’s not that noticeable.
I did a research project looking at (iirc) kinase cascades, in which we were using a molecule-by-molecule simulation to look at cascading signals in hypothetical signaling networks, and varied the levels of phosphorylation required for activation required at each tier, and showed how the different topologies/rules governed the relationship between input and output signals, and their relationship to noise tolerance (since chemical networks can be quite noisy). It was very abstract in that we weren’t reconstructing known networks, but rather using sandbox physics to explore the idea.
During my graduate research, our lab space was next to the cell modeling department and I would catch a talk here or there. Always found it a super interesting approach because it really tries to make sense of what we’ve learned from traditional biology and generates really nice hypotheses/theories for testing out in biological models. I also love how you can apply so much abstract mathematics to biological systems for biologically meaningful findings. Most of these types of cell modeling papers go above my head, but I still really appreciate them from outside.