Madison Chapel
@chapelmadison.bsky.social
MSc, bioinformatics (UBC)
Head full of yarn scraps, bloodstream full of bubble tea
Head full of yarn scraps, bloodstream full of bubble tea
12/ For more results and full details on the simulation framework, take a look at our preprint! www.biorxiv.org/content/10.1...
Evolutionary simulations reveal role for genomic recombination in the evolution of gene regulatory network complexity and robustness
The gene regulatory networks (GRNs) of eukaryotes are dramatically more complex than the GRNs of prokaryotes, but we lack a complete picture of the selective pressures that have shaped this difference...
www.biorxiv.org
September 3, 2025 at 7:25 PM
12/ For more results and full details on the simulation framework, take a look at our preprint! www.biorxiv.org/content/10.1...
11/ Our simulations suggest that, rather than being a direct target of selection, GRN complexity may arise as a byproduct of other evolutionary processes. And to close things out, here’s an animation of a GRN evolving. Watch how fitness and complexity change as the population evolves ! 🎥
September 3, 2025 at 7:25 PM
11/ Our simulations suggest that, rather than being a direct target of selection, GRN complexity may arise as a byproduct of other evolutionary processes. And to close things out, here’s an animation of a GRN evolving. Watch how fitness and complexity change as the population evolves ! 🎥
10/ We saw that complexity emerged more rapidly under changing environmental conditions. For recombining populations, this effect was particularly pronounced.
September 3, 2025 at 7:25 PM
10/ We saw that complexity emerged more rapidly under changing environmental conditions. For recombining populations, this effect was particularly pronounced.
9/ We simulated changing environments by shifting the expression goal for a subset of genes. You can see fitness decrease sharply as the environment changes, followed by recovery as populations adapt.
September 3, 2025 at 7:25 PM
9/ We simulated changing environments by shifting the expression goal for a subset of genes. You can see fitness decrease sharply as the environment changes, followed by recovery as populations adapt.
8/ Recombination also modulated the rate at which complexity emerged. In static environments, recombination delayed the emergence of complexity – likely by purging deleterious mutations and slowing drift. But real environments are rarely static !
September 3, 2025 at 7:25 PM
8/ Recombination also modulated the rate at which complexity emerged. In static environments, recombination delayed the emergence of complexity – likely by purging deleterious mutations and slowing drift. But real environments are rarely static !
7/ So what did recombination do? First off, recombining populations were consistently more 𝗿𝗼𝗯𝘂𝘀𝘁 than non-recombining ones – they were better at maintaining an expression profile after mutational perturbation. This matches observations from many previous studies!
September 3, 2025 at 7:25 PM
7/ So what did recombination do? First off, recombining populations were consistently more 𝗿𝗼𝗯𝘂𝘀𝘁 than non-recombining ones – they were better at maintaining an expression profile after mutational perturbation. This matches observations from many previous studies!
6/ When all other factors are matched, recombining and non-recombining populations converge, rather than diverge, in complexity. This suggests that the complexity 𝘭𝘪𝘮𝘪𝘵 may be defined not by the reproductive strategy but by other features of the GRN system, which we held constant.
September 3, 2025 at 7:25 PM
6/ When all other factors are matched, recombining and non-recombining populations converge, rather than diverge, in complexity. This suggests that the complexity 𝘭𝘪𝘮𝘪𝘵 may be defined not by the reproductive strategy but by other features of the GRN system, which we held constant.
5/ Surprisingly, recombination didn’t change the final complexity. Neither mutation rate nor initial binding affinity mattered, either. Given enough time, everything converged to ‘random’ GRN complexity; the same level seen in populations evolved 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗻𝘆 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻.
September 3, 2025 at 7:25 PM
5/ Surprisingly, recombination didn’t change the final complexity. Neither mutation rate nor initial binding affinity mattered, either. Given enough time, everything converged to ‘random’ GRN complexity; the same level seen in populations evolved 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗻𝘆 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻.
4/ To investigate this, we built a biochemically-inspired GRN model. TF affinities and concentrations determine gene expression. The closer expression levels are to a specific goal, the more fit a GRN is. Using this model, we simulated 1 million generations of evolution.
September 3, 2025 at 7:25 PM
4/ To investigate this, we built a biochemically-inspired GRN model. TF affinities and concentrations determine gene expression. The closer expression levels are to a specific goal, the more fit a GRN is. Using this model, we simulated 1 million generations of evolution.
3/ Here’s our idea: complexity in eukaryotic GRNs may have evolved in response to recombination. In simple GRNs, recombination could introduce differently acting transcription factors (TFs) that alter gene expression. In complex GRNs, expression would be buffered by additional TFs
September 3, 2025 at 7:25 PM
3/ Here’s our idea: complexity in eukaryotic GRNs may have evolved in response to recombination. In simple GRNs, recombination could introduce differently acting transcription factors (TFs) that alter gene expression. In complex GRNs, expression would be buffered by additional TFs
2/ Prokaryotic GRNs are relatively simple – there’s specific binding between TFs and their target sites. But in complex eukaryotic GRNs, TFs recognize identical sites throughout the genome, and expression of any one gene depends on multiple TFs binding in combination.
September 3, 2025 at 7:25 PM
2/ Prokaryotic GRNs are relatively simple – there’s specific binding between TFs and their target sites. But in complex eukaryotic GRNs, TFs recognize identical sites throughout the genome, and expression of any one gene depends on multiple TFs binding in combination.
10/ We saw that complexity emerged more rapidly under changing environmental conditions. For recombining populations, this effect was particularly pronounced.
September 3, 2025 at 7:15 PM
10/ We saw that complexity emerged more rapidly under changing environmental conditions. For recombining populations, this effect was particularly pronounced.
9/ We simulated changing environments by shifting the expression goal for a subset of genes. You can see fitness decrease sharply as the environment changes, followed by recovery as populations adapt.
September 3, 2025 at 7:15 PM
9/ We simulated changing environments by shifting the expression goal for a subset of genes. You can see fitness decrease sharply as the environment changes, followed by recovery as populations adapt.
6/ When all other factors are matched, recombining and non-recombining populations converge, rather than diverge, in complexity. This suggests that the complexity 𝘭𝘪𝘮𝘪𝘵 may be defined not by the reproductive strategy but by other features of the GRN system, which we held constant.
September 3, 2025 at 7:15 PM
6/ When all other factors are matched, recombining and non-recombining populations converge, rather than diverge, in complexity. This suggests that the complexity 𝘭𝘪𝘮𝘪𝘵 may be defined not by the reproductive strategy but by other features of the GRN system, which we held constant.
4/ To investigate this, we built a biochemically-inspired GRN model. TF affinities and concentrations determine gene expression. The closer expression levels are to a specific goal, the more fit a GRN is. Using this model, we simulated 1 million generations of evolution.
September 3, 2025 at 7:15 PM
4/ To investigate this, we built a biochemically-inspired GRN model. TF affinities and concentrations determine gene expression. The closer expression levels are to a specific goal, the more fit a GRN is. Using this model, we simulated 1 million generations of evolution.
10/ It’s time to shift how we think about variants – instead of associating each variant with a discrete impact on disease risk, they instead have context-dependent distributions of effects. For more details – and more examples! – check out our preprint! www.biorxiv.org/content/10.1...
Variant effects depend on polygenic background: experimental, clinical, and evolutionary implications
Both rare and common genetic variants contribute to human disease, and emerging evidence suggests that they combine additively to influence disease liability. However, due to the non-linear relationsh...
www.biorxiv.org
January 8, 2025 at 8:24 PM
10/ It’s time to shift how we think about variants – instead of associating each variant with a discrete impact on disease risk, they instead have context-dependent distributions of effects. For more details – and more examples! – check out our preprint! www.biorxiv.org/content/10.1...
9/ Similarly, the dependence on polygenic background means that selection coefficients for the same variant vary across populations, time, individuals, and environments – instead of a single value, they may be better represented as a distribution.
January 8, 2025 at 8:24 PM
9/ Similarly, the dependence on polygenic background means that selection coefficients for the same variant vary across populations, time, individuals, and environments – instead of a single value, they may be better represented as a distribution.
8/ Because the same variant has different phenotypic consequences in different genetic backgrounds, it could persist in low-risk backgrounds indefinitely, while being strongly selected against in individuals with high polygenic risk!
January 8, 2025 at 8:24 PM
8/ Because the same variant has different phenotypic consequences in different genetic backgrounds, it could persist in low-risk backgrounds indefinitely, while being strongly selected against in individuals with high polygenic risk!
7/ Polygenic background also changes how selection acts! More alleles contributing to a trait widens the genetic risk distribution, increasing selective pressure as individuals are pushed to extremes. Highly polygenic traits lead to smaller, more uniform effect sizes.
January 8, 2025 at 8:24 PM
7/ Polygenic background also changes how selection acts! More alleles contributing to a trait widens the genetic risk distribution, increasing selective pressure as individuals are pushed to extremes. Highly polygenic traits lead to smaller, more uniform effect sizes.
6/ What about in the clinic? Genetic testing for rare variants could be complimented by PGS info to better stratify patient risk. This would help prioritize those at highest risk of disease for early intervention while providing peace of mind to those at lower risk.
January 8, 2025 at 8:24 PM
6/ What about in the clinic? Genetic testing for rare variants could be complimented by PGS info to better stratify patient risk. This would help prioritize those at highest risk of disease for early intervention while providing peace of mind to those at lower risk.
5/ For researchers: selecting cell lines with an appropriate genetic background may be as crucial as choosing the right cell type when characterizing disease-associated variants. Low-risk backgrounds could mask variant effects that would be revealed in other genetic contexts!
January 8, 2025 at 8:24 PM
5/ For researchers: selecting cell lines with an appropriate genetic background may be as crucial as choosing the right cell type when characterizing disease-associated variants. Low-risk backgrounds could mask variant effects that would be revealed in other genetic contexts!
4/ Disease prevalence scales non-linearly with disease liability. A 1-unit increase in PGS leads to very different changes in disease prevalence depending on whether it occurs in a low-risk (green) or high-risk (red) genetic background. Same variant, totally different outcomes!
January 8, 2025 at 8:24 PM
4/ Disease prevalence scales non-linearly with disease liability. A 1-unit increase in PGS leads to very different changes in disease prevalence depending on whether it occurs in a low-risk (green) or high-risk (red) genetic background. Same variant, totally different outcomes!
3/ That’s a pretty straightforward observation, but it has widespread consequences for experimental design, clinical genetics, and evolution. The key idea is that you cannot fully understand a variant’s effect without considering the genetic background it occurs in. Here’s why:
January 8, 2025 at 8:24 PM
3/ That’s a pretty straightforward observation, but it has widespread consequences for experimental design, clinical genetics, and evolution. The key idea is that you cannot fully understand a variant’s effect without considering the genetic background it occurs in. Here’s why: