Publication

Genetische Bauteile à la carte – Lernen von Hochdurchsatz-Experimenten

2026

Abstract

Understanding how sequence changes shape biological function is a central challenge in bioengineering, but the vast number of possible sequences makes exhaustive experimental testing of genetic parts infeasible. Advances in high-throughput experimentation and machine learning allow new and disruptive ways to address this conundrum. We highlight how these technologies can synergize to enable a shift from empiric, case-by-case optimization to the systematic, data-driven design of genetic parts.

Official source

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2026

Abstract

Official source

Related concepts (21)

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

Genetic programming

In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs. The operations are: selection of the fittest programs for reproduction (crossover), replication and/or mutation according to a predefined fitness measure, usually proficiency at the desired task.

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Genetische Bauteile à la carte – Lernen von Hochdurchsatz-Experimenten

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