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Accurate phenotype prediction based on genetic information has numerous societal applications, such as crop design or cellular factories. Epistasis, when biological components interact, complicates modelling phenotypes from genotypes. Here we show an approach to mitigate this complication for polarity establishment in budding yeast, where mechanistic information is abundant. We coarse-grain molecular interactions into a so-called mesotype, which we combine with gene expression noise into a physical cell cycle model. First, we show with computer simulations that the mesotype allows validation of the most current biochemical polarity models by quantitatively matching doubling times. Second, the mesotype elucidates epistasis emergence as exemplified by evaluating the predicted mutational effect of key polarity protein Bem1p when combined with known interactors or under different growth conditions. This example also illustrates how unlikely evolutionary trajectories can become more accessible. The tractability of our biophysically justifiable approach inspires a road-map towards bottom-up modelling complementary to statistical inferences. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Accurate phenotype prediction based on genetic information has numerous societal applications, such as crop design or cellular factories. Epistasis, when biological components interact, complicates modelling phenotypes from genotypes. Here we show an approach to mitigate this complication for polarity establishment in budding yeast, where mechanistic information is abundant. We coarse-grain molecular interactions into a so-called mesotype, which we combine with gene expression noise into a physical cell cycle model. First, we show with computer simulations that the mesotype allows validation of the most current biochemical polarity models by quantitatively matching doubling times. Second, the mesotype elucidates epistasis emergence as exemplified by evaluating the predicted mutational effect of key polarity protein Bem1p when combined with known interactors or under different growth conditions. This example also illustrates how unlikely evolutionary trajectories can become more accessible. The tractability of our biophysically justifiable approach inspires a road-map towards bottom-up modelling complementary to statistical inferences. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
One of the biggest scientific challenges to be tackled this century is how traits of living organisms originate from genes, the so-called genotype-phenotype map, and conversely how traits influence genes through a process called evolution. The solution will yield a large societal impact, with applications in food (e.g., engineering drought-resistant crops), industry (e.g., material production through microorganisms) and health care (e.g., personalized medicine). The complexity of the genotype-phenotype map lies in how it typically spans multiple, interwoven scales (e.g., in size). This dissertation builds on the ambition that ultimately, a solution is found by generalizations of simpler systems. Therefore, we unravel here the map for a tractable example, polarization in budding yeast, and make insightful how evolution can couple to the map.
During polarization, the unicellular organism budding yeast chooses a direction in which it will divide. This involves self-organizing many proteins, in particular Cdc42p, to a single region on its cell membrane. While starting on the molecular scale, the process ultimately affects population traits such as doubling time. To understand the transition in scales in detail, we start bottom-up by experimentally verifying the molecular theory behind polarity success for different genetic backgrounds. The theoretical model treats, amongst others, proteins that activate Cdc42p, which are mechanistically included for the first time. Concretely, we test resulting predictions on sharp lower Cdc42p concentration bounds for viability using, inter alia, growth assays on strains variably producing fluorescent Cdc42p. The experiments confirmed the theory that allows reconstitution of molecular mechanisms underlying polarity establishment.
To advance to population traits, I constructed a tractable growth model, fed by simple rules emerging from the aforementioned theory (only implicitly encompassing the molecular information). Essentially, Cdc42p is stochastically produced, diluted by basic volume expansion, and must exceed a concentration threshold to divide. Despite disregarding many details, quantitative agreement between unintuitive, experimentally validated traits documented in literature and those from model simulations is reached.
The simplicity of the model assumptions also allows new insights in evolution. I elaborate theoretically how lucky cells that by chance produce above average amounts of protein, proliferate better to bias the observed population. Therefore, protein levels promptly adapt non-genetically, also in response to e.g., environmental changes, in a reversible and almost automatic manner. Based on existing experimental data, I predict this noise-based mechanism to notably expand the ease of evolution for essential genes (in yeast for 25%-60% of these). Due to its simple nature, I conjecture that it should be found in many organisms.
In conclusion, we find a successful strategy to tractably analyze the genotype-phenotype map in yeast polarity. The map can be expanded to other functions than polarity, provided that sufficient bio-functional information is available. The analysis also elucidates a new evolutionary coupling to this map. At a step above genes, noisy protein production can freely be utilized for short-term adaptation. Experimentally confirming the presence of this evolutionary mechanism in other model systems, and applying to these the same strategy to predict traits, will generate a completer picture of how traits of living systems are formed and shaped by evolution.
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One of the biggest scientific challenges to be tackled this century is how traits of living organisms originate from genes, the so-called genotype-phenotype map, and conversely how traits influence genes through a process called evolution. The solution will yield a large societal impact, with applications in food (e.g., engineering drought-resistant crops), industry (e.g., material production through microorganisms) and health care (e.g., personalized medicine). The complexity of the genotype-phenotype map lies in how it typically spans multiple, interwoven scales (e.g., in size). This dissertation builds on the ambition that ultimately, a solution is found by generalizations of simpler systems. Therefore, we unravel here the map for a tractable example, polarization in budding yeast, and make insightful how evolution can couple to the map.
During polarization, the unicellular organism budding yeast chooses a direction in which it will divide. This involves self-organizing many proteins, in particular Cdc42p, to a single region on its cell membrane. While starting on the molecular scale, the process ultimately affects population traits such as doubling time. To understand the transition in scales in detail, we start bottom-up by experimentally verifying the molecular theory behind polarity success for different genetic backgrounds. The theoretical model treats, amongst others, proteins that activate Cdc42p, which are mechanistically included for the first time. Concretely, we test resulting predictions on sharp lower Cdc42p concentration bounds for viability using, inter alia, growth assays on strains variably producing fluorescent Cdc42p. The experiments confirmed the theory that allows reconstitution of molecular mechanisms underlying polarity establishment.
To advance to population traits, I constructed a tractable growth model, fed by simple rules emerging from the aforementioned theory (only implicitly encompassing the molecular information). Essentially, Cdc42p is stochastically produced, diluted by basic volume expansion, and must exceed a concentration threshold to divide. Despite disregarding many details, quantitative agreement between unintuitive, experimentally validated traits documented in literature and those from model simulations is reached.
The simplicity of the model assumptions also allows new insights in evolution. I elaborate theoretically how lucky cells that by chance produce above average amounts of protein, proliferate better to bias the observed population. Therefore, protein levels promptly adapt non-genetically, also in response to e.g., environmental changes, in a reversible and almost automatic manner. Based on existing experimental data, I predict this noise-based mechanism to notably expand the ease of evolution for essential genes (in yeast for 25%-60% of these). Due to its simple nature, I conjecture that it should be found in many organisms.
In conclusion, we find a successful strategy to tractably analyze the genotype-phenotype map in yeast polarity. The map can be expanded to other functions than polarity, provided that sufficient bio-functional information is available. The analysis also elucidates a new evolutionary coupling to this map. At a step above genes, noisy protein production can freely be utilized for short-term adaptation. Experimentally confirming the presence of this evolutionary mechanism in other model systems, and applying to these the same strategy to predict traits, will generate a completer picture of how traits of living systems are formed and shaped by evolution.
A bottom-up route towards predicting evolution relies on a deep understanding of the complex network that proteins form inside cells. In a rapidly expanding panorama of experimental possibilities, the most difficult question is how to conceptually approach the disentangling of such complex networks. These can exhibit varying degrees of hierarchy and modularity, which obfuscate certain protein functions that may prove pivotal for adaptation. Using the well-established polarity network in budding yeast as a case study, we first organize current literature to highlight protein entrenchments inside polarity. Following three examples, we see how alternating between experimental novelties and subsequent emerging design strategies can construct a layered understanding, potent enough to reveal evolutionary targets. We show that if you want to understand a cell's evolutionary capacity, such as possible future evolutionary paths, seemingly unimportant proteins need to be mapped and studied. Finally, we generalize this research structure to be applicable to other systems of interest.
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A bottom-up route towards predicting evolution relies on a deep understanding of the complex network that proteins form inside cells. In a rapidly expanding panorama of experimental possibilities, the most difficult question is how to conceptually approach the disentangling of such complex networks. These can exhibit varying degrees of hierarchy and modularity, which obfuscate certain protein functions that may prove pivotal for adaptation. Using the well-established polarity network in budding yeast as a case study, we first organize current literature to highlight protein entrenchments inside polarity. Following three examples, we see how alternating between experimental novelties and subsequent emerging design strategies can construct a layered understanding, potent enough to reveal evolutionary targets. We show that if you want to understand a cell's evolutionary capacity, such as possible future evolutionary paths, seemingly unimportant proteins need to be mapped and studied. Finally, we generalize this research structure to be applicable to other systems of interest.
How can a self-organized cellular function evolve, adapt to perturbations, and acquire new sub-functions? To make progress in answering these basic questions of evolutionary cell biology, we analyze, as a concrete example, the cell polarity machinery of Saccharomyces cerevisiae. This cellular module exhibits an intriguing resilience: it remains operational under genetic perturbations and recovers quickly and reproducibly from the deletion of one of its key components. Using a combination of modeling, conceptual theory, and experiments, we propose that multiple, redundant self-organization mechanisms coexist within the protein network underlying cell polarization and are responsible for the module’s resilience and adaptability. Based on our mechanistic understanding of polarity establishment, we hypothesize that scaffold proteins, by introducing new connections in the existing network, can increase the redundancy of mechanisms and thus increase the evolvability of other network components. Moreover, our work gives a perspective on how a complex, redundant cellular module might have evolved from a more rudimental ancestral form.
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How can a self-organized cellular function evolve, adapt to perturbations, and acquire new sub-functions? To make progress in answering these basic questions of evolutionary cell biology, we analyze, as a concrete example, the cell polarity machinery of Saccharomyces cerevisiae. This cellular module exhibits an intriguing resilience: it remains operational under genetic perturbations and recovers quickly and reproducibly from the deletion of one of its key components. Using a combination of modeling, conceptual theory, and experiments, we propose that multiple, redundant self-organization mechanisms coexist within the protein network underlying cell polarization and are responsible for the module’s resilience and adaptability. Based on our mechanistic understanding of polarity establishment, we hypothesize that scaffold proteins, by introducing new connections in the existing network, can increase the redundancy of mechanisms and thus increase the evolvability of other network components. Moreover, our work gives a perspective on how a complex, redundant cellular module might have evolved from a more rudimental ancestral form.
