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Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Bioprinting Cellularized Constructs Using a Tissue-specific Hydrogel Bioink

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Published on: April 21, 2016

Towards generative digital twins in biomedical research.

Jiqing Wu¹, Viktor H Koelzer^1,2

¹Department of Biomedical Engineering, University of Basel, Basel, Switzerland.

Computational and Structural Biotechnology Journal

|October 22, 2024

Summary

Generative digital twins (GDTs) create high-fidelity virtual replicas of biological systems. These advanced models enable personalized medicine through in silico simulations for tailored diagnostics and treatments.

Keywords:

Digital twin Generative AI Multiplexed imaging Spatial omics

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Last Updated: May 7, 2026

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Multimodal 3D Printing of Phantoms to Simulate Biological Tissue

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Published on: January 11, 2020

Area of Science:

Biomedical Research
Computational Biology
Artificial Intelligence in Healthcare

Background:

Biological processes are inherently spatial, necessitating context-aware modeling.
Personalized healthcare requires advanced tools for simulating individual patient biology.
High-volume spatial omics data is becoming increasingly available.

Purpose of the Study:

To introduce and define Generative Digital Twins (GDTs) in biomedical research.
To highlight the potential of GDTs for personalized medicine and in silico interventions.
To review recent advancements, challenges, and future directions in GDT development.

Main Methods:

Leveraging generative artificial intelligence (AI) for model creation.
Utilizing high-volume biomedical data profiled with spatial technologies.
Developing spatially-resolved digital representations of biological entities.

Main Results:

High-fidelity virtual replicas of biological entities (cells, organs, organisms) can be generated.
GDTs enable compelling in silico modeling for simulated interventions.
Applications demonstrated in computational pathology, oncology, and cardiology.

Conclusions:

GDTs offer a powerful platform for advancing personalized healthcare.
In silico causal scenario exploration facilitates tailored clinical diagnostics and treatments.
Further research is needed to address challenges and unlock the full potential of GDTs.