Exploring the groundbreaking interdisciplinary research presented at the International Symposium on Mathematical and Computational Biology
Imagine trying to read the story of life itself, not with a microscope, but with mathematics. This is the fundamental quest that brought brilliant minds from across the globe to Brasilia, Brazil, in August 2009 for the International Symposium on Mathematical and Computational Biology. For nearly a decade, the BIOMAT consortium has served as a unique interdisciplinary crossroads where biologists, mathematicians, physicists, and computer scientists collectively translate the complex language of living systems into the precise formulations of equations and algorithms 5 .
The BIOMAT 2009 symposium, held from August 1st to 6th at the University of Brasilia, represented a pivotal gathering in this ongoing scientific conversation. Unlike specialized conferences that dive deep into narrow fields, this symposium embraced the breathtaking scope of applying mathematical reasoning to everything from the microscopic dance of proteins to the sweeping dynamics of entire ecosystems 1 4 . Its proceedings reveal a scientific community steadily forging a new predictive framework for biology—one where theoretical models don't just explain what has happened, but can forecast biological outcomes with startling accuracy 5 .
Bringing together experts from diverse scientific fields
Developing frameworks to forecast biological outcomes
Addressing challenges from cellular to ecosystem levels
At the smallest scales, researchers demonstrated how mathematical patterns govern biological processes. Studies revealed that seemingly irregular cell colonies exhibit fractal behavior 1 4 , suggesting deeply embedded mathematical principles guide microscopic biological organization.
Researchers explored control mechanisms regulating molecular systems, crucial for understanding diseases like cancer. Presentations tackled protein-protein interactions using computational models and applied graph theory to analyze biochemical networks 4 .
Long before COVID-19, researchers were refining techniques to track pathogen spread. Presentations focused on tuberculosis reinfection dynamics and models mapping HIV spread within immune systems 4 .
Remarkably, researchers presented a real-time forecasting model for influenza pandemics in the UK, integrating multiple surveillance datasets 4 . This work proved prescient as similar models became indispensable during the H1N1 swine flu pandemic.
A mathematical detective story illuminating persistent medical puzzles
The research team constructed systems of differential equations to model TB dynamics 4 . Their compartmental model divided populations into distinct groups: susceptible individuals, active TB cases, successfully treated patients, and reinfected cases.
The model incorporated real-world biological parameters, including transmission rates, treatment efficacy, and immunity levels. This framework tested how different levels of natural immunity would influence long-term TB dynamics at population level 4 .
The modeling revealed a counterintuitive relationship: individuals with latent TB infection demonstrated a dramatically lower risk—79% lower— of developing progressive tuberculosis after reinfection compared to completely uninfected individuals 2 .
Long-term projections showed that reducing transmission would significantly shorten elimination time in US-born populations, but highlighted the need for tailored public health strategies for different demographic groups 2 .
| Research Question | Methodology | Key Finding |
|---|---|---|
| What is the relative risk of progressive TB after reinfection in previously exposed vs. naive individuals? | Comparative simulation of disease progression in different population compartments | Previously exposed individuals have 79% lower risk of progressive TB after reinfection |
| Can TB be eliminated through reduced transmission alone? | Long-term projection of TB prevalence under different intervention scenarios | Reduced transmission would shorten time to elimination in US-born but not foreign-born populations |
| How does immunity affect long-term TB dynamics? | Sensitivity analysis of model parameters related to immune protection | Partial immunity following infection significantly alters long-term disease prevalence patterns |
Computational and analytical resources for translating biological questions into mathematical terms
Describe how quantities change continuously relative to other variables
Used in: TB reinfection dynamics, HIV spread modeling 4Represent probabilistic transitions between different states
Used in: Biological evolution processes 4Use random sampling to solve deterministic problems
Used in: Protein folding simulations 4Study relationships between discrete objects
Used in: Biological networks, chemosystematics 4Through tutorials and mini-courses offered to young researchers, the consortium invested in interdisciplinary thinkers who would carry this work forward 5 .
The symposium's acceptance of only about 22% of submitted papers ensured that published contributions represented the cutting edge of the field 5 .
Creating conversations between specialists who might never otherwise connect accelerated innovation across multiple domains 5 .
BIOMAT 2009 demonstrated that mathematical biology had matured from a speculative niche to an essential scientific approach with practical applications.
"The scientists who gathered in Brasilia helped establish a foundation for a more predictive, quantitative understanding of life that continues to yield insights and innovations today."
Championed vital conversations between biology, mathematics, and computer science
Established foundations for forecasting biological outcomes with mathematical accuracy
Transformed theoretical models into tools for addressing real-world challenges