Genomics is Dying…and The Future is Not What You Think

By Christopher Gregg PhD

For the past several decades, we have put DNA at the center of the biomedical universe. Huge efforts to sequence human genomes and define impactful variants have been at the core of medical research and a primary focus for the NIH. We did amazing experiments and have an extraordinary depth of knowledge about the genome. The genome sequences of thousands of species, millions of people, and thousands of tumor samples were described. There will always be more intriguing details to uncover, but the major wave of the revolution is over (see Fig. 1).

Fig. 1 - Declining stock price (US$) for Illumina Inc. – the leader in genome sequencing technology

Criticisms of the precision genome medicine revolution are now abundant. James Tabery laid recently many of the primary issues to bare in his excellent book, The Tyranny of the Gene, and for the sophisticated reader, Philip Ball’s wonderful new book, How Life Works, is a damning critique of the problems with our DNA centric view of biology and disease and a wonderful synthesis of a better viewpoint. I strongly recommend both.

We now know of several problems with the DNA centric precision genome medicine view. One is that genome data is not a very effective predictor for diagnosis or treatment. It works well for rare genetic diseases, but not the more common complex diseases. DNA sequence does not explain enough phenotypic variance to be diagnostic or point to clear mechanisms for treatment in most cases. I, and many others in this field, support Jonathon Pritchard’s “omnigenic model” of complex traits, which proposes that thousands of genes can affect any given trait or complex disease process directly or indirectly through gene networks (1,2).

Another problem is economics. If a drug is designed for precision medicine to target a genetic mechanism/defect that only applies to a subset of patients, then the market for that drug is vastly shrunk, yet the cost for developing the drug remains high - an estimated $2.6B, when one includes R&D costs. Necessarily, the price tag for such precision genome medicines in the market becomes impossibly high. The precision/personalized genome medicine revolution didn’t work as well as we hoped. The revolution is over. It won’t be a major focus for much longer.

So, what should you think about if you are a young innovator, a company, or an institution trying to invest in the future?

If you said A.I….you are only partially right. Like electricity and math, AI is a tool, not a solution. You need to aim it carefully at the right problem.

The Right Problem to Focus on is Understanding Modularity and Emergent Properties in Complex Biological Systems

The next conceptual and technical revolution will be in understanding modularity and emergent properties across different levels of complex biological systems. All biological systems spontaneously evolve modular and hierarchical architectures, which is key to understanding their organization and function (3). These properties are observed at the levels of DNA sequence, protein sequence, transcriptional networks, protein interaction networks, cells, organs, animal body plans, brain structure, neural circuits, and even at the levels of social and ecological food networks (4-7) Hierarchical, modular architectures have evolutionary advantages that include

(i) increasing system robustness, such that perturbations of one component are not lethal,

(ii) enabling iterative optimizations of the parts of a module and system via natural selection, and

(iii) enhancing evolvability such that existing modules can be reorganized, duplicated and modified without recreating their constituent parts and structure de novo. Modules may emerge spontaneously in biology during phenotypic phase transitions (7).

Brain networks involve hierarchically organized modules of specialized brain functions that increase computational speed and efficiency, enhance robustness and improve functionality in changing environments (8). Somehow, this architecture enables emergent properties and phenomena at each level that cannot be related back to individual component parts through reductionist science, only the whole.

We will seek to define modules within and across different levels of organization in biological systems to discover the identity, nature, and logic of the emergent properties of biological systems that are so perplexing to us – How do cells work? How does consciousness emerge? What are the processes causing complex disease? How does behavior work? How does aging work?

By defining functionally important modular components of biological not just within one level of analysis, like gene expression, but across levels of analysis (DNA+RNA+protein) and between cells, tissues, and organs, we will better understand how life works at all levels  – molecular, cellular, physiological, behavioral.

As we gain improved insights into how the human body and brain works, how it can break, and how to control it – we will learn how to fix it.

A modular conceptual framework will be key.

What are the missing tools?

We need new technologies to identify and functionally study genes, proteins, cellular organelles, brain-body signaling, brain functions, behavior, and more. One goal is to discover and functionally validate a module and show how it is built from component pieces, define its emergent properties, and construct models showing how its emergent properties manifest from its components.

I think that the connectomics field that captures massive image sets at the nanoscale level to understand neural circuits is headed in the right direction (9), but much more innovation is needed

The future is video

Nothing provides more information than video. The future will be videos of live biological processes that show all components interacting and behaving at all levels - molecular, cellular, tissue/organ, organismal, and behavioral. Millions of measurements will be captured from normal and perturbed systems and fed into AI models that help reveal the modular components, architecture, and logic of the system…and how the behaviors of the components predict emergent properties.

Nothing captures more information rich and useful data than video. A picture is 1000 words, but the right video will leave you speechless. By seeing how it all really works, extraordinary insights are gained

However, we currently lack effective platforms to capture and understand video data of complex living biological systems or behaving organisms – especially humans.

As a scientist and cancer patient, I feel urgency to build momentum in this area and make real progress.

At Storyline Health Inc., we are building video-based AI technologies to understand human behavior and disease symptoms to define the modular components of complex human behavioral phenotypes and cognitive processes. Storyline Health Inc. is leading the behavioral AI revolution and the demand for this technology has exploded into many areas beyond medicine.

At Primordial AI Inc., we are working to use AI to uncover modular components of biological systems at the molecular and cellular levels to improve drug and treatment development, with an emphasis on metabolism. The mechanisms involved in the evolution of different traits in hundreds of different species is helping us crack the code.

Modules, emergent properties, AI, and health. Stay tuned! 

1. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell 169, 1177–1186 (2017).

2. Liu, X., Li, Y. I. & Pritchard, J. K. Trans Effects on Gene Expression Can Drive Omnigenic Inheritance. Cell 177, 1022-1034.e6 (2019).

3. Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. Proc National Acad Sci 102, 13773–13778 (2005).

4. Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47-52 (1999).

5. Melo, D., Porto, A., Cheverud, J. M. & Marroig, G. Modularity: Genes, Development, and Evolution. Annu Rev Ecol Evol Syst 47, 1–24 (2016).

6. Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nat Rev Genet 8, 921–931 (2007).

7. Lorenz, D. M., Jeng, A. & Deem, M. W. The emergence of modularity in biological systems. Phys Life Rev 8, 129–160 (2011).

8. Meunier, D., Lambiotte, R. & Bullmore, E. T. Modular and Hierarchically Modular Organization of Brain Networks. Front Neurosci-switz 4, 200 (2010).

9. Shapson-Coe, A. et al. A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science 384, eadk4858 (2024).