Modeling Cell Division

Is molecular biology ready to use modeling to inform experimental work?

The cell cycle is a holy grail of biology. The first mutants that dissected some of its regulatory apparatus, the CDC mutants of Saccharomyces cerevisiae (yeast), electrified the field and led to a Nobel prize. These were temperature sensitive mutants, making only small changes to the protein sequence that rendered that protein inactive at high temperature (thus inducing a cell cycle arrest phenotype), while allowing wild-type growth at normal temperatures. In the fifty years since, a great deal of the circuitry has been worked out, with the result that it is now possible, as a recent paper describes, to make a detailed mathematical model of the process that claims to be useful in the sense of explaining existing findings in a unified model and making predictions of places to look for additional actors.

At the center of this regulatory scheme are transcription activators, SBF/MBF, that are partly controlled by, and in turn control the synthesis of, a series of cyclins. Cyclins are proteins that were observed (another Nobel prize) to have striking variations in abundance during the cell cycle. There are characteristic cyclins for each phase of the cell cycle, which goes from G1, a resting phase, to S, which is DNA replication, to G2, a second resting phase, and then M, which is mitosis, which brings us back to G1. Cyclins work by binding to a central protein kinase, Cdc28, which, as regulated by each distinct cyclin, phosphorylates and thus regulates distinct sets of target proteins. The key decision a cell has to make is whether to commit to DNA replication, i.e. S phase. No cell wants to run out of energy during this process, so its size and metabolic state needs to be carefully monitored. That is done by Cyclin 3 (Cln3), Whi5, and Bck2, which each influence whether the SBF/MBF regulators are active. 

Some highly simplified elements of the yeast cell cycle. Cyclins (Cln and Clb) are regulators of a central protein kinase, Cdc28, that direct it to regulate appropriate targets at each stage of the cell cycle. Cyclins themselves are regulated by transcriptional control (here, the activators SBF and MBF), and then destroyed at appropriate times by proteolysis, rendering them abundant only at specific times during the cell cycle. Focusing on the "START" process that starts the process from rest (G1 phase) to new bud formation and DNA replication (S phase), Cln3 and Bck2 respond to upstream nutritional and size cues, and each activate the SBF/MBF transcription activator.

As outlined in the figure above, Cyclin 3 is the G1 cyclin, which, in complex with Cdc28 phosphorylates Whi5, turning it off. Whi5 is an inhibitor that binds to SBF/MBF, so the Cyclin 3 activation turns these regulators on, and thus starts off the cell cycle under the proper conditions. Incidentally, the mammalian version of Whi5, Rb (for retinoblastoma), is a notorious oncogene, that, when mutated, releases cells from regulatory control over cell division. SBF and MBF bind to genes for the next series of cyclins, Cln1, Cln2, Clb5, Clb6. The first two are further G1 cyclins that orchestrate the end of G1. They induce phosphorylation and inactivation of Sic1 and Cdc6, which are inhibitors of Clb5 and Clb6. These latter two are then the initiators of S phase and DNA replication. Meanwhile, Cln3 stays around till M phase, but is then degraded in definitive fashion by the proteases that end M phase. Starvation conditions lead to rapid degradation of Cln3 at all times, and thus to no chance of starting a new cell cycle.

Charts of the abundance of some cyclins through the cell cycle. Each one has its time to shine, after which it is ubiquitinated and sent off to the recycling center / proteasome.

Bck2 is another activator of SBF/MBF that is unrelated to the Cln3/Whi5 system, but also integrates cell size and metabolic status information. Null mutants of Cln3 (or Bck2) are viable, if altered in cell cycle, while double null mutants of Cln3 and Bck2 are dead, indicating that these regulators are each important, in a complementary way, in cell cycle control. Given that little is known about Bck2, the modelers in this paper assume various properties and hope for the best down the line, predicting that cell size (at the key transition to S phase) is more affected in the Cln3 null mutant than in the Bck2 null mutant, since in the former, excess active Whi5 soaks up most of the available SBF/MBF, and requiring extra-high and active levels of Bck2 to overcome this barrier and activate the G1 cyclins and other genes.

The modelers are working from the accumulated, mostly genetic data, and in turn validate their models against the same genetic data, plus a few extra mutants they or others have made. The models are mathematical representations of how each node (i.e protein, or gene) in the system responds to the others, but since there are a multitude of unknowns, (such as what really regulates Bck2 from upstream, to cite just one example), the system is not really able to make predictions, but rather fine-tunes/reconciles what knowledge there is, and, at best, points to gaps in knowledge. It is a bit like AI, which magically recombines and regurgitates material from a vast corpus based on piece-wise cues, but is not going to find new data, other than through its notorious hallucinations.

For example, a new paper came out after this modeling, which finds that Cln3 affects Cln2 abundance by mechanisms quite apart from its SBF/MBF transcriptional control, and that it regulates cell size in large part at M phase, not through its G1/S gating. All this comes from new experimental work, unanticipated by the modeling. So, in the end, experimental work always trumps modeling, which is a bit different than how things are in, say, physics, where sometimes the modeling can be so strong that it predicts new particles, forces, and other phenomena, to be validated later experimentally. Biology may have its master predictive model in the theory of evolution, but genetics and molecular biology remain much more of an empirical slog through the resulting glorious mess.

• Bitcoin isn't a currency, but rather just another asset class, one without any fundamental or socially positive value. A little like gold, actually, except without gold's resilience against social / technological disruption.
• The disastrous post-Soviet economic transition, on our advice.
• The enormous labor drain, and resource drain, from global South to North.