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Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
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GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.

Carlos Martí-Gómez1, David M McCandlish1, Justin B Kinney1

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Computational biology models have ambiguous parameters ("gauge freedoms") hindering interpretation. GaugeFixer, a new Python package, resolves these ambiguities with linear scaling, enabling analysis of large sequence-function landscapes.

Keywords:
EpistasisFitness landscapeGauge fixingGeneralized one-hot modelsKronecker productParameter non-identifiabilitySequence-function relationshipsShine-Dalgarno sequence

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Mathematical modeling

Background:

  • Sequence-function relationship models are crucial in computational biology.
  • Model parameters often have ambiguities, termed "gauge freedoms," preventing direct interpretation.
  • Existing methods for resolving gauge freedoms are computationally intensive, limiting scalability.

Purpose of the Study:

  • To introduce GaugeFixer, a Python package for efficiently resolving gauge freedoms in sequence-function models.
  • To enable the interpretation of complex sequence-function landscapes previously intractable due to computational limitations.
  • To provide a practical tool for analyzing biological sequence data.

Main Methods:

  • Developed GaugeFixer, a Python package that implements gauge-fixing projections with linear computational scaling.
  • Exploited the mathematical structure of gauge-fixing projections to overcome quadratic memory requirements.
  • Applied GaugeFixer to analyze an empirical fitness landscape for translation initiation.

Main Results:

  • GaugeFixer achieves linear scaling, allowing analysis of models with millions of parameters.
  • The package successfully resolved ambiguities in a translation initiation fitness landscape.
  • Analysis revealed conserved and varied ribosome binding preferences around the start codon.

Conclusions:

  • GaugeFixer provides an efficient and scalable solution for interpreting sequence-function models.
  • The tool facilitates deeper biological insights into sequence-function relationships.
  • GaugeFixer addresses a critical unmet need in computational biology tools.