Evidence map›Paper›PMID 42817818›Full record

ReviewGenome biology and evolution2026

Synthetic Approaches as Empirical Tools for Testing Evolutionary Theory.

Xueying C Li, Paco Majic, Cauã Antunes Westmann

Abstract readReview
In one paragraph

Review in Genome biology and evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xueying C LiMinistry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China.ORCID 0000-0002-1100-4694
Paco MajicDevelopmental Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany.ORCID 0000-0003-0670-7413
Cauã Antunes WestmannInstitute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.ORCID 0000-0003-1572-2279

Funding

EMBL Interdisciplinary Postdoctoral ProgrammeEPFL School of Life Sciences ELISIRFundamental Research Funds for the Central Universities and Young Scientists Fund of the National Natural Science Foundation of China #32400495Marie Skłodowska-Curie Actions COFUND 101217201
6 · The paper itself

Abstract

Evolutionary biology has traditionally inferred process from patterns in extant organisms and the fossil record, leaving many foundational questions constrained by their historical nature. Over the past two decades, a growing family of synthetic approaches-high-throughput mapping such as deep mutational scanning, reconstructive experiments such as ancestral sequence reconstruction, controlled evolution, and synthetic biology in the narrower sense-have made it possible to test these questions directly by systematically exploring sequence space, reconstructing historical states, replaying evolution under defined conditions, and engineering biological systems. Here, we review how these approaches reshape several foundational questions: the distribution of mutational effects, the structure and navigability of fitness landscapes, the evolution of evolvability, developmental constraint, historical contingency, and the engineering of evolutionary systems. Across these domains, these experiments are exposing unexpected mechanistic detail that refines and extends classical theory-revealing, for example, how strongly mutational effects depend on environmental and genetic context, how ruggedness can coexist with broad accessibility on fitness landscapes, how genotype-phenotype maps are intrinsically biased and heterogeneous, and how random sequences carry latent functional potential that may serve as raw material for later innovation. As these technologies continue to expand the empirical reach of evolutionary biology, theory in turn sharpens the questions they are best suited to address-an iterative dialogue between experiment and theory that brings us closer to understanding how life evolves.

Indexed as

Biological EvolutionEvolution, MolecularSynthetic BiologyAnimalsGenetic FitnessModels, GeneticMutationadaptive landscapesancestral sequence reconstructionevolutionary theoryhistorical contingenciesmolecular evolutionsynthetic biology

Identifiers

PMID42817818
PMCPMC13669622

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.