Biology subjects

Levien, E.

Publications and source records attributed to Levien, E..

7 recordsLinked to original sources

bioRxiv2026

How many mutations does it take to evolve a new function?

Biochemical and genetic networks in cells evolve to produce new functions. Evolution is guided by the genotype to phenotype map which describes how mutations at the molecular scale lead to changes in network function at the cellular scale. The number of genotypes is astronomically large which makes these maps very high dimensional. In this context, the simple question: How many mutations does it take to evolve a new function?, is usually hard to answer. Here we consider this question in the context of a simple Boolean-network model of a genotype-phenotype map. The nodes and edges of the Boolean network represent the genotype while the Boolean function the network computes is the phenotype. The network evolves by rewiring of its edges, while keeping the number of nodes fixed. We compute the number of mutations needed to achieve a new Boolean function as a function of the number of nodes in the network. We show that the number of mutations scales with the number of nodes of the network as a power-law with an exponent determined by local geometrical features of the genotype-phenotype map. Our theoretical study emphasizes the role of the genotype-phenotype map in determining the time to evolve a new function.

evolutionary biology↗

bioRxiv2025

Slow trajectories generate divergent cell fates following antibiotic stress

Single-cell microfluidic experiments have shown that upon abrupt exposures to antibiotics, genetically homogeneous microbial populations undergo divergent cell fates. The mechanism underlying this divergence is not clear and in particular, the emergence of a range of distinct slow-growing phenotypes cannot be explained by models relying on bistability alone. Here, we propose a model for gene expression and growth dynamics during antibiotic exposures, which is informed by well-known scaling relations connecting proteome allocation and cell growth. In our model, resources available for transcription and translation of resistance genes act like generalized momenta and their initial variation is predictive of cell fate. Our model reproduces key experimental observations, including the prediction of specific phenotypes and a critical threshold in initial resource allocation that predicts cell survival. These results offer an alternative mechanism for the emergence of phenotypic diversity where slow cell growth effectively stabilizes cellular states along the trajectory that are far from the final stable states predicted by fixed points.

microbiology↗

bioRxiv2025

Stochasticity in mammalian cell growth rates drives cell-to-cell variability independently of cell size and divisions

Cell growth rates exhibit cell-intrinsic cell-to-cell variability, which influences cell fitness and size home-ostasis from bacteria to cancer. Whether this variability arises from noise in cell growth or cell division processes, or originates from cell-size-dependent growth rates, remains unclear. To separate these potential sources of growth variability, single-cell growth rates need to be examined across different timescales. Here, we study cell-intrinsic size and growth regulation by tracking lymphocytic leukemia cell mass accumulation with high precision and minute-scale temporal resolution along long ancestral lineages. We first show that cell-size-dependent growth regulation and asymmetric division of cell size do not explain cell-to-cell growth variability. We then isolate growth fluctuations from overlapping cell-cycle-dependent growth using a Gaussian process regression analysis. We find that these growth fluctuations drive cell-to-cell growth variability within ancestral lineages despite being independent of cell divisions, cell cycle, and cell size. Overall, our results indicate that cell-intrinsic long-term patterns in cell growth are a byproduct of short-term growth fluctuations.

systems biology↗

bioRxiv2022

Evolutionary dynamics in non-Markovian models of microbial populations

In the past decade, great strides have been made to quantify the dynamics of single-cell growth and division in microbes. In order to make sense of the evolutionary history of these organisms, we must understand how features of single-cell growth and division influence evolutionary dynamics. This requires us to connect processes on the single-cell scale to population dynamics. Here, we consider a model of microbial growth in finite populations which explicitly incorporates the single-cell dynamics. We study the behavior of a mutant population in such a model and ask: can the evolutionary dynamics be coarse-grained so that the forces of natural selection and genetic drift can be expressed in terms of the long-term fitness? We show that it is in fact not possible, as there is no way to define a single fitness parameter (or reproductive rate) that defines the fate of an organism even in a constant environment. This is due to fluctuations in the population averaged division rate. As a result, various details of the single-cell dynamics affect the fate of a new mutant independently from how they affect the long-term growth rate of the mutant population. In particular, we show that in the case of neutral mutations, variability in generation times increases the rate of genetic drift, and in the case of beneficial mutations, variability decreases its fixation probability. Furthermore, we explain the source of the persistent division rate fluctuations and provide analytic solutions for the fixation probability as a multi-species generalization of the Euler-Lotka equation.

evolutionary biology↗

bioRxiv2022

Cooperative assembly confers regulatory specificity and long-term genetic circuit stability

In eukaryotes, links in gene regulatory networks are often maintained through cooperative self-assembly between transcriptional regulators (TRs) and DNA cis-regulatory motifs, a strategy widely thought to enable highly specific regulatory connections to be formed between otherwise weakly-interacting, low-specificity molecular components. Here, we directly test whether this regulatory strategy can be used to engineer regulatory specificity in synthetic gene circuits constructed in yeast. We show that circuits composed of artificial zinc-finger TRs can be effectively insulated from aberrant misregulation of the host cell genome by using cooperative multivalent TR assemblies to program circuit connections. As we demonstrate in experiments and mathematical models, assembly-mediated regulatory connections enable mitigation of circuit-driven fitness defects, resulting in genetic and functional stability of circuits in long-term continuous culture. Our naturally-inspired approach offers a simple, generalizable means for building evolutionarily robust gene circuits that can be scaled to a wide range of host organisms and applications.

synthetic biology↗

bioRxiv2022

Single-cell microfluidic analysis unravels individual cellular fates during Double-Strand Break Repair

Trinucleotide repeat expansions are responsible for two dozen human disorders. Contracting expanded repeats by Double-Strand Break Repair (DSBR) might be a therapeutic approach. Given the complexity of manipulating human cells, recent assays were made to quantify DSBR efficacy in yeast, using a fluorescent reporter. In this study DSBR is characterized with an interdisciplinary approach, linking large population dynamics and individual cells. Time-resolved molecular measurements of changes in the population are first confronted to a coupled differential equation model to obtain repair processes rates. Comparisons with measurements in microfluidic devices, where the progeny of 80-150 individual cells are followed, show good agreement between individual trajectories and mathematical and molecular results. Further analysis of individual progenies shows the heterogeneity of individual cell contributions to global repair efficacy. Three different categories of repair are identified: high-efficacy error-free, low-efficacy error-free and low-efficacy error-prone. These categories depend on the type of endonuclease used and on the target sequence.

bioengineering↗

bioRxiv2019

Quantifying phenotypic variability and fitness in finite microbial populations

In isogenic microbial populations, phenotypic variability is generated by a combination of intrinsic factors, specified by cell physiology, and environmental factors. Here we address the question: how does phenotypic variability of a microbial population affect its fitness? While this question has previously been studied for exponentially growing populations, the situation when the population size is kept fixed has received much less attention. We show that in competition experiments with multiple microbial species, the fitness of the population can be determined from the distribution of phenotypes, provided all variability is due to intrinsic factors. We then explore how robust the relationship between fitness and phenotypic variability is to environmental fluctuations. We find that this relationship breaks down in the presence of environmental fluctuations, and derive a simple formula relating the average fitness of a population to the phenotype distribution and fluctuations in the instantaneous population growth rate. Using published experimental data we demonstrate how our formulas can be used to discriminate between intrinsic and environmental contributions to phenotypic diversity.

systems biology↗