(py)oscode: fast solutions of oscillatory ODEs

Agocs, F. J.

doi:10.21105/joss.02830

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…As we delve into more complex systems, computational hurdles become prominent. The complexity of the ODE system significantly impacts in silico performance [ 130 , 131 ]. We are considering optimization strategies such as smarter caching of common genotypes, data-oriented design, and adaptive step-size algorithms.…”

Section: Discussionmentioning

confidence: 99%

The distribution of fitness effects during adaptive walks using a simple genetic network

O’Brien,

Holland,

Engelstädter

et al. 2024

PLoS Genet

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The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

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Section: Discussionmentioning

confidence: 99%

The distribution of fitness effects during adaptive walks using a simple genetic network

O’Brien,

Holland,

Engelstädter

et al. 2024

PLoS Genet

View full text Add to dashboard Cite

show abstract

“…As we delve into more complex systems, computational hurdles become prominent. The complexity of the ODE system significantly impacts in silico performance (Agocs 2020; Städter et al 2021). We are considering optimization strategies such as smarter caching of common genotypes, data-oriented design, and adaptive step-size algorithms.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The distribution of fitness effects during adaptive walks using a simple genetic network

O’Brien,

Holland,

Engelstädter

et al. 2023

Preprint

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The tempo and mode of adaptation is dependent on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains inconclusive. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution to phenotypic variance arising from gene interactions. In this study, we present a novel approach to modeling quantitative traits as products of genetic networks via systems of ordinary differential equations so we can mechanistically explore the effects of network structures on adaptation. We present the first step of this approach by studying a simple gene regulatory network, the negative autoregulation motif (NAR). Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and NAR models. A key expectation from adaptive walk theory is that the distribution of fitness effects (DFE) of new beneficial mutations is exponential. We found that although both models strayed from expectation, harboring fewer beneficial mutations than expected, they took a similar number of adaptive steps to reach the optimum. NAR populations were 10% less likely to reach the optimum and took longer to adapt. This was partly due to a complex and largely deleterious DFE from new mutations, and by requiring coordinated changes among its molecular components. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can hinder adaptation by generating complex and multi-modal distributions of fitness effects.

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“…Obviously, computing a highly oscillatory dynamics is a long-standing problem occurring in many relevant applications (Engquist et al, 2009;Hairer et al, 2006). However, we are not aware of other software packages with similar purpose, excepting the very recent (py)oscode package (Agocs, 2020) which combines WKB techniques and standard integration methods to ensure a user-specified tolerance.…”

Section: Related Research and Softwarementioning

confidence: 99%