National Mathematics Day: Mathematics in Crisis and R&D
A COVID-19 model predicted 510,000 UK deaths without intervention, prompting lockdowns that shaped global responses. Mathematics relates to crises in three ways: picturing them via models, constituting them through algorithms, and formatting responses via predictions. For biotech and pharma R&D leaders, these dynamics highlight mathematics’ role in modeling drug crises, economic risks in trials, and regulatory forecasts.
Mathematics Pictures Crises
Mathematical models represent crises like epidemics. Epidemic models use R (infections per case) where if R>1, infections grow exponentially: N(t) = R^t, total S(t) = (R^{t+1}-1)/(R-1). Imperial College’s 2020 report modeled unmitigated COVID-19, forecasting high mortality and urging suppression via isolation.
Insight: Models picture but may have similarity gaps between predictions and reality.
Mathematics Constitutes Crises
Mathematics drives financial transactions via algorithms in payments and trading. Popular financial crisis involved math in housing models and algorithmic trading accelerating crashes. It is plausible that math can fuel inequality via flawed models in finance.
Mathematics Formats Crises
Climate models with atmosphere-ocean equations format responses by identifying sensitive parameters for intervention. Pandemic models format actions like distancing.
Pharma Innovation takeaway: Math formats regulatory strategies, testing disease-modifying therapy assumptions.
Relevance to Biotech R&D
Ontologies structure biomedical data as mathematics (rigor), code (utility), or Esperanto (interoperability). Rzhetsky and Evans identify views: mathematics seeks unified logic; code fits purposes; Esperanto links domains.
| View | Focus | Example |
| Mathematics | Consistency, upper ontologies like BFO | Reasoning across biology |
| Computer Code | Custom utility per task | GO for gene annotation |
| Esperanto | Cross-linking datasets | Mapping FMA to pathways |
Life Sciences Data Services use ontologies for Data Analytics in drug discovery targets.
In drug discovery, ontologies map phenotypes for targets. Preclinical models use ODEs from math biology. Regulatory paths benefit from traceable ontology rules.
Leading Applications
Gene Ontology (GO) uses is-a, part-of relations for targets. Foundational Model of Anatomy (FMA) networks body parts. BioCyc enables pathway reasoning.
Artificial Intelligence in R&D can leverage ontologies to reduce bias in models.
Saturo Global Integration
Saturo Global aids Biotech R&D via:
- Data Curation & Management: Harmonize genomic/clinical data with ontologies.
- Indexing & Abstracting: Annotate patents/research for queries.
- Strategic Patent Support: Map IP landscapes using ontology analytics.
- Data Visualization: Link datasets to visuals for insights.
Key Drivers in 2026 Technology Transfer
- Mathematics and Predictive Modeling: Advanced mathematical and computational techniques are crucial for developing sophisticated AI and ML models used in fields like medical imaging and drug discovery. Predictive analytics, a core mathematical application, will enable early intervention strategies and forecast disease outbreaks or other crises, allowing for proactive public health responses.
- Federated Ontologies and Data Interoperability: Ontologies provide standardized frameworks for knowledge representation, which is critical for tackling data heterogeneity issues across different systems and organizations. When combined with federated learning (FL), this allows for training robust AI models on decentralized datasets (e.g., from multiple hospitals or pharmaceutical companies) without moving sensitive information, thereby ensuring privacy and security. This approach is already being used in projects to predict cancer treatment responses and improve tumor segmentation.
- Enabling Treatments and Crisis Response: This technology transfer model allows for the rapid development and deployment of solutions, which is vital during crises.
- Treatments: AI-guided biomarker discoveries and hyper-personalized medicine are accelerating cancer treatment options and enabling non-opioid pain relief. Quantum computing and AI are also speeding up drug discovery by simulating molecular interactions at an unprecedented scale.
- Crisis Response: Federated learning can improve emergency response, especially in resource-limited settings, by providing mobile diagnostic capabilities and point-of-care therapeutics. The development of smart sensing networks with IoT and Edge AI facilitates real-time data analysis for effective disaster management and community resilience.
In essence, 2026 is marking a turning point where AI and advanced mathematical and data management techniques move beyond experimentation to deeply embedded, outcome-driven solutions that foster collaboration and accelerate societal benefits across various crises and medical challenges. Data-driven partnerships ensure verifiable modelling.
References
Skovsmose O. 2021. Mathematics and crises. Educ Stud Math. doi:10.1007/s10649-021-10037-0.
Stypinska J. 2022. AI ageism: a critical roadmap. AI Soc. doi:10.1007/s00146-022-01553-5.
Rzhetsky A, Evans JA. 2011. War of Ontology Worlds. PLoS Comput Biol. doi:10.1371/journal.pcbi.1002191.
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