Genes, Galaxies, & Discoveries: Engineering New Advances in Modern Science | Global Conference 2024

Shift from Isolated Research to Global Moonshot Collaboration

• Historical Context: The panel identifies a paradigm shift from small-scale, investigator-initiated projects (e.g., Jonas Salk) to large-scale, multi-domain "moonshot" goals (e.g., Human Genome Project, Apollo missions).
  • Driver: Complex problems now require ecosystem-level coordination, sharing risk, and structural changes in funding and incentives.
  • Analogy: Progress mirrors the transition from the Cold War's monolithic, competitive space programs to the collaborative International Space Station model.

Commercialization of Space and the Lunar Economy

• Market Transformation: U.S. National Security Council's 2018 declaration of the moon as a strategic interest triggered a shift from government-built infrastructure to commercial procurement.
  • Policy Mechanism: The Commercial Lunar Payload Services program offered fixed-price contracts (~$100 million) to private entities to deliver payloads, replacing decades-long, billion-dollar government programs.
  • Cost Reduction: Technological maturation has lowered the cost of landing on the moon from ~4% of GDP (Apollo era) to approximately $100 million per mission.
  • Innovation Model: Private startups (e.g., Intuitive Machines) utilize advanced manufacturing (3D printing high-nickel steel injectors) to solve intractable engineering problems in days rather than years.
  • Future Outlook: Companies are evolving from single-launch vendors to providers of a "cislunar economy," offering services for command, control, navigation, and surface operations.

Advancements in Biomedical Research and Data Sharing

• All of Us Research Program (NIH):
  • Goal: Enroll 1 million diverse participants to advance medical breakthroughs relevant to all populations.
  • Strategy: Move beyond hypothesis-driven cohorts to "disease-neutral" large-scale resources to enable discovery of rare genetic variations and cross-disease insights.
  • Impact: Rapidly generated virtual datasets during the COVID-19 pandemic by leveraging pre-existing, harmonized electronic health record standards.
• Global Alliance for Genomics and Health (GA4GH):
  • Scale: Currently coordinates over 100 countries to aggregate and share clinical and genomic data.
  • Technology: Genome sequencing turnaround has compressed from 13 years ($3 billion) to 3 days (record: 18 hours).
  • Solution to Borders: Implementation of "federation" models allows data to remain sovereign within national clouds while analysis is brought to the data, enabling global interoperability without violating privacy laws.
• Philanthropy's Role:
  • Case Study (Alzheimer's): Philanthropists identified clinical trial failure was due to a lack of diagnostic tools, not just drug efficacy. Funding a blood-based biomarker solved the "messy middle" problem, causing biotech companies to re-enter the market.
  • Funding Model: Philanthropy acts as a risk-tolerant catalyst to solve specific roadblocks (e.g., diagnostics) that de-risk the field for larger federal and private investment.

Venture Capital and Investment Criteria

• Investment Priorities:
  • New Modalities: Preference for "white canvas" technologies (e.g., epigenetic gene editing) that solve limitations of existing tech like CRISPR (off-target mutations).
  • Market Gaps: Identifying assets from failed companies due to non-scientific reasons (e.g., founder disputes) that possess high-value, overlooked scientific platforms.
  • People: Heavy weighting on trusting and betting on repeatable, meritocratic entrepreneurs over unproven concepts.
• Cross-Sector Application:
  • Immune System: Technologies developed for cancer (immune dysregulation) are increasingly applied to autoimmune diseases (immune over-attack).
  • Biotech to Climate: Gene therapy research is being piloted for soil microbiome manipulation in agriculture due to fewer regulatory hurdles.
  • Data Transfer: Space data platforms (e.g., lunar reconnaissance) are using healthcare cloud architecture and AI to manage petabytes of planetary data.

Policy, Economics, and Clinical Implementation

• Economic Viability: Genomic testing has demonstrated 5x higher diagnostic rates at 1/4 the cost compared to traditional methods for pediatric syndromes, driving national health system adoption (e.g., UK, Australia).
  • Cost Offset: Early genomic diagnosis prevents lifelong disability (e.g., epilepsy), shifting the health system model from expensive treatment to cost-effective prevention.
• Precision Medicine Barriers:
  • Insurance Gaps: While diagnostics (e.g., blood tests) are often covered, expensive curative therapies face significant insurer resistance.
  • Clinical Practice: Lack of uniform treatment protocols (e.g., for bipolar disorder) creates 100% variance in patient outcomes; "learning health systems" are being designed to real-time sync data with clinical practice.
• Policy Misalignment: A disconnect exists between rapid scientific breakthroughs (e.g., cancer cures) and lagging public health policies regarding leading causes of death (e.g., obesity has overtaken smoking as the primary cancer risk factor).

Artificial Intelligence and Data Ethics

• AI Risks: Algorithms trained on non-representative data (e.g., single-site, European-centric) fail when applied to diverse populations.
  • Example: A glaucoma surgery prediction algorithm trained on a single site performed no better than a coin flip on the diverse "All of Us" dataset until retrained with broader data.
• Data Gaps: Significant disparity exists between the depth of data in oncology versus serious mental illness, and the vast majority of genomic data still comes from individuals of European descent.

Five-to-Ten Year Forward-Looking Projections

• Space: Commercialization of the lunar surface will mature into a blueprint for celestial space commerce, moving beyond landing to a full service economy.
• Genomics:
  • Cost & Standardization: Genome sequencing costs will drop to ~$100, becoming standard of care and integrated into permanent medical records ("read once, use often").
  • Diversity: Goal to move from 90% European ancestry in genomic studies to a globally reflective dataset within a decade.
  • Discovery: Diversification will reveal the function of 2/3 of currently unknown genes and identify protective mechanisms for diseases.
• Therapeutics: Expansion of gene therapies and next-gen immunotherapies from monogenic disorders (e.g., sickle cell) to complex, non-monogenic diseases over the next decade.
• Culture: A fundamental shift from "discovery" to "implementation," where returning genetic risk data to participants becomes routine to enable early intervention.