Inquiryresearch

The Nitty Gritty of Complexity Science

Our Framework: Complexity Science in Action

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Basic Science Designed for Complexity

  • We reject the false choice between “clean” reductionist experiments and “messy” real-world systems. Instead, we:
    • Design modular experiments that serve as building blocks for larger SoS models (e.g., studying how pollution particles alter not just lung cells but their communication with immune and nervous systems)
    • Embed complexity controls—intentionally introducing environmental/social variables in phased ways to map nonlinear effects

Complexity principle applied: Emergent properties can only be understood by studying interactions, not isolated components.

When Lab Meets World: SoS Modeling

  • For diseases where traditional models fail (e.g., metabolic syndrome, autoimmune disorders), we build “complexity-aware” computational platforms that:
    • Integrate data across biological scales (molecules → organs) and environmental contexts (pollution → policy)
    • Use adaptive network modeling to identify leverage points where small interventions could have outsized effects
  • Complexity principle applied: Systems exhibit critical transitions—we model tipping points between health and disease.

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Translation Through Feedback Loops

  • We close the loop between models and reality via:
    • Community-engaged research that grounds models in lived experience (e.g., partnering with urban farmers to study how neighborhood greenspace impacts microbiome resilience)

Digital twin pipelines that continuously update with real-world exposure data.

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SoS-Driven Basic Science

  • We design experiments that either:
    • Explicitly test interactions between biological, environmental, and social factors, or
    • Generate high-quality mechanistic data to feed into larger multi-scale disease models
  • Example: Instead of studying tumor genetics in isolation, we investigate how air pollution alters immune-tumor crosstalk.

Complex Disease Modeling When Reductionism Fails

  • For diseases where lab experiments cannot capture real-world complexity (e.g., obesity, Alzheimer’s), we build SoS computational models that:
    • Integrate molecular, clinical, and environmental data
    • Simulate emergent disease behaviors
  • Example: An agent-based model of cardiovascular disease incorporating diet, stress hormones, and neighborhood food access.

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Translational Impact

  • We bridge the gap between sterile lab models and human populations by:
    • Partnering with community health organizations to gather real-world exposure data
    • Validating models using longitudinal patient cohorts.

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Our Public-Integrated Framework​

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Community-Driven Research Priorities

  • We host quarterly “Mysteries of Medicine” forums where the public submits and votes on unanswered health questions to guide our research
  • Current priorities include:
    • Unexplained infertility: Examining the system of hormonal, environmental, and lifestyle interactions
    • Treatment-resistant depression: Mapping the biological-social feedback loops
    • Medical mysteries: Cases where patients don’t fit existing disease categories.

Citizen Science Programs

  • “Complex Health Trackers”: Mobile apps that let participants contribute real-world health, environmental and lifestyle data
  • Community labs where patients can work with researchers to design studies about their conditions
  • Story-collection initiatives that document illness experiences as qualitative data for our models.

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Transparent Science Communication

  • Interactive dashboards showing how public questions are being studied
  • “Complexity Explained” video series breaking down our systems approaches
  • Annual “Putting It All Together” symposium where researchers and community members co-interpret findings.

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