Biological Intelligence (BI) refers to computing systems built using living human brain cells (neurons) cultured on silicon chips, pioneered by companies such as Cortical Labs and other wetware-computing startups.
Wetware computing merges biology with synthetic interfaces. The living neurons are grown on Multi-Electrode Arrays (MEAs) that send and receive real electrical signals. The tissue does not simply read code; it experiences the input as a pattern of physical, energetic simulation.
Right now, the wetware industry is centered in European, American and Australian biotech hubs. To build these biological processors, companies use Induced Pluripotent
Stem Cells (iPSCs), which are human skin or blood cells reprogrammed into embryonic-like stem cells state and then developed into human brain neurons.
However, global genomic databases are overwhelmingly skewed towards populations of European descent, creating significant biases in medical Al.
The Biological Origin of the Cells Themselves
It is important to note that Yoruba population of West Africa for example have been extensively represented in genomic research datasets, including the Yoruba in Ibadan (YRI) reference population. There are also documented cases of commercial exploitation, unauthorised secondary use, weak consent, cross-border extraction of genomic data and biological specimens, and disputes over who owns or benefits from biological materials.
There are usually three different things that are often conflated:
1. Physical biological material — blood, tissue, DNA, immortalised cell lines, etc.
2. Genomic information — sequencing data derived from those materials.
3. Cultural/indigenous knowledge — traditional medicinal knowledge, ancestry, ecological knowledge, linguistic information and cultural information.
Why Legislation is Important ?
Companies can potentially extract enormous value from genomic data, while the legal protections surrounding different forms of biological information and material can vary considerably. There is a long history of problematic extraction.
— The HeLa Case —
A classic example is the HeLa cell line. Cells taken from Henrietta Lacks in 1951 were cultured and subsequently became one of the most important biological research
resources in history, despite the fact that she had not consented to their subsequent use. Her cells were later commercialised and distributed extensively.
— The Havasupia Case —
The Havasupai case is even more relevant to questions surrounding cultural and population-specific genetics. Blood samples were collected for diabetes research, but were subsequently used for research involving schizophrenia, inbreeding and migration without the community’s agreement. The dispute ultimately resulted in a settlement, compensation and return of the DNA samples. Researchers have described this broader phenomenon using terms such as biocolonialism, bioprospecting or genetic extractivism.
— Nigerian example is 54gene —
54gene was established around African genomic diversity and built a biobank involving Nigerian hospitals and health systems. It developed relationships with major international genomics, technology and pharmaceutical organisations and ultimately raised more than $45 million in 2019. Its subsequent financial collapse generated significant questions about what happens to physical biological specimens and genomic data when a genomics company becomes financially distressed. Policymakers are now wrestling with a related problem: digital sequence information.
— The Genomic Data Resource —
Once a physical biological sample is sequenced, the resulting digital genetic information can potentially circulate globally without the physical specimen ever crossing a border. International policymakers have therefore been debating whether existing biodiversity and genetic-resource benefit sharing rules adequately cover digital genetic information.
The eco-system currently looks something like this:
human cells → genomic/epigenomic data → AI models → synthetic biology → engineered cell lines → drug discovery → patents
The commercially valuable asset could ultimately ultimately consist of:
the biological sample + genomic dataset + phenotype + cultural/environmental metadata + AI-derived models + engineered derivatives.
At that point, asking “Who owns the cell?” is no longer enough enough.
One has to ask:
• Who owns the information extracted from the cell?
• Who controls derivative datasets?
• Who owns AI models trained on that information?
• Does the community have rights over derived discoveries?
• Should descendants have any rights?
• Can a company sell the dataset to another company?
• Can genetic information from an indigenous population be combined with cultural knowledge to develop a commercial product?
Conclusion
Everybody stands to benefit from stronger protections. By protecting genomic data, Africans can help prevent global biotechnology and Al monopolies from using the biological information of its people to develop drugs and diagnostics without receiving an appropriate share of the intellectual property. Africa is protecting the biological information that could shape the identity and opportunities of future
generations.
— The Local Advantage —
African engineers can begin thinking about computing architectures that operate at fractional costs, potentially powered by solar energy and trained or developed using
high-context local data, by adopting alternative paradigms such as Biological Intelligence (BI) and Wetware Computing.