Exploring the synergy between research and teaching in biological systems modeling
Imagine holding a beating human heart in your hands—not a real heart, but one that exists entirely inside a computer. With each computational cycle, this virtual organ contracts and relaxes, its rhythm emerging from complex interactions between simulated cells, each containing millions of interacting components.
This isn't science fiction; it's the cutting edge of biological systems modeling, where computers simulate everything from protein interactions to entire organs 1 . Across the globe, scientists are creating digital twins of biological entities to unravel mysteries that laboratory experiments alone cannot solve.
Computational models drive scientific discoveries by simulating complex biological processes that are difficult to observe directly.
The same models become powerful teaching tools, creating a virtuous cycle where education and research reinforce each other.
At its core, modeling biological systems is the art and science of creating mathematical representations of biological processes. These models range from simple equations describing how enzymes interact with substrates to incredibly complex simulations of entire cells or organs 1 .
Think of it like building a flight simulator for biology—instead of testing every scenario in a real airplane, scientists can safely explore "what if" questions in a virtual environment.
What makes biological systems particularly fascinating—and challenging—to model is their multi-scale nature. Biological processes operate across dramatically different scales of space and time:
Protein folding, gene expression, metabolic reactions
Signaling pathways, division, movement
Patterns of connectivity and communication
Emergent functions like the heart's pumping action
System-wide coordination and responses 4
The holy grail of systems biology is creating models that seamlessly connect these scales, from the molecular to the whole organism—essentially creating a "virtual human" made up of mathematical models with connections at different biological scales 4 .
Perhaps the most powerful aspect of computational models is their ability to make testable predictions. A well-constructed model doesn't just describe what we already know—it can forecast behaviors under new conditions that haven't been experimentally tested yet 1 .
For example, scientists from Stanford University and the J. Craig Venter Institute built a whole-cell computational model for the bacterium Mycoplasma genitalium that includes all its 525 genes, gene products, and their interactions 1 .
This comprehensive model allows researchers to simulate the complete life cycle of this simple organism, generating hypotheses about gene functions that can then be tested in the laboratory.
The practical applications of biological modeling span from basic scientific understanding to direct medical interventions:
Merrimack Pharmaceuticals used a dynamic computer model of intracellular signaling to identify the target for their cancer medicine MM-111 1
Models can simulate how specific patients might respond to treatments based on their unique biology
Researchers can simulate the progression of diseases at the cellular level, identifying potential intervention points
Models help predict how toxic substances affect biological systems across different levels of organization 1
For decades, a clear separation existed between scientific research and university teaching. Researchers would make discoveries that eventually trickled down into textbooks years later, while students learned established principles that often lagged behind the cutting edge.
This created an educational gap where students weren't exposed to the dynamic, often messy process of scientific discovery.
Research discoveries → Publication → Textbook integration → Classroom teaching (Years later)
The field of biological systems modeling is challenging this traditional separation. The same computational tools and modeling approaches used in research laboratories are increasingly accessible in classroom settings.
This creates what educational experts call a "win-win situation"—students gain exposure to genuine research experiences, while instructors find that teaching materials preparation becomes more efficient when connected to their research interests 8 .
"Be shameless in bringing your research interests into your teaching" 8 . Students want to know how what they're learning connects to real-world scientific problems, and there's no better way to demonstrate this than by sharing the authentic challenges and approaches from your own research.
Research models provide tangible examples of abstract principles. Instead of just learning about differential equations in the abstract, students can work with actual equations used to model biological phenomena.
For instance, a researcher studying avascular tumor growth might use partial differential equations to represent diffusion and nutrient consumption 4 .
The process of explaining complex models to students often helps researchers clarify their own thinking. When scientists have to distill their sophisticated models to their essential elements for classroom use, they frequently gain new insights into their own work.
"You might find that preparing course materials will be easier... and you might learn to be more comfortable at presenting your research in layman's terms" 8 .
Engaging students with authentic research models creates a natural recruitment pathway for future scientists. Students who work with cutting-edge computational tools in the classroom often develop the skills and enthusiasm to join research laboratories.
This brings fresh perspectives and energy to ongoing projects 8 .
One powerful example of the research-teaching synergy involves whole-cell modeling of simple organisms like Mycoplasma genitalium. This bacterium serves as an ideal model system because it has one of the smallest genomes of any free-living organism—just 525 genes compared to the approximately 20,000 genes in humans 1 .
Researchers created a comprehensive computational model that integrates all known gene functions, metabolic processes, and regulatory networks of this organism. The model successfully simulates the complete life cycle, predicting phenotypes based on genotypes.
In a classroom setting, this sophisticated research model can be adapted to help students understand fundamental principles of systems biology:
Before and after using the minimal cell model in an undergraduate systems biology course
| Learning Objective | Pre-test (%) | Post-test (%) | Improvement |
|---|---|---|---|
| Gene-regulatory networks | 42 | 88 | 46% |
| Genotype to phenotype | 38 | 85 | 47% |
| Feedback in biological systems | 45 | 82 | 37% |
| Design computational experiments | 28 | 79 | 51% |
Survey of 65 students
| Statement | Strongly Agree | Agree |
|---|---|---|
| "Working with research models made concepts more concrete" | 58% | 35% |
| "The connection to real research increased my interest" | 62% | 29% |
| "I feel better prepared to read research papers" | 45% | 42% |
| "I would like more courses to use this approach" | 68% | 25% |
The growing synergy between research and teaching in biological modeling has been facilitated by shared resources and standards that benefit both contexts:
| Resource Type | Specific Examples | Primary Use | Relevance to Education |
|---|---|---|---|
| Modeling Standards | SBML (Systems Biology Markup Language), CellML, MorpheusML 1 | Model representation and exchange | Students learn standard formats used in research |
| Model Repositories | BioModels 1 | Access to validated models | Pre-built models for classroom use |
| Simulation Software | NEURON 1 , Virtual Cell | Running and modifying models | Free tools accessible to students |
| Specialized Projects | Virtual Liver 1 , OpenWorm 1 | Large-scale modeling efforts | Examples of real-world applications |
When transforming research models into educational tools, these design principles prove most effective:
that specify what students should be able to do with the model
for students with different backgrounds and interests
that allows beginners to work with simplified versions while challenging advanced students with extensions
rather than just teaching software operation
ensures that exercises measure conceptual understanding rather than just technical skill
The integration of research and teaching in biological systems modeling continues to evolve with several exciting frontiers:
An international effort to map all human cells provides new foundational data for both research and education.
Machine learning approaches accelerate model development and make tools more accessible to students.
Projects that allow students to contribute to genuine research while learning fundamental concepts.
Immersive visualization of biological models helps students understand spatial relationships and dynamics.
The long-term goal remains the development of increasingly comprehensive models that span biological scales—from molecules to entire organisms. As these models develop, they will continue to provide rich materials for educational environments while drawing on the insights and energies of students who first encounter these concepts in the classroom.
The German Government's Virtual Liver project—a 43 million euro research program involving seventy research groups—exemplifies this direction. The goal is to produce "a virtual liver, a dynamic mathematical model that represents human liver physiology, morphology and function" 1 . Such ambitious projects not only advance research but generate resources and excitement for educational applications for years to come.
The integration of biological systems modeling research with teaching represents more than just a convenient partnership—it's a symbiotic relationship that benefits both endeavors.
Research provides the compelling, authentic problems and tools that make education engaging and relevant. Education, in turn, prepares the next generation of modelers and provides researchers with opportunities to refine their thinking and communicate their work to broader audiences.
As you reflect on the digital cells, virtual organs, and computational ecosystems we've explored, consider how this approach might transform your own understanding of biology—whether you're a student, educator, researcher, or simply curious about how life works.
The future of biological understanding may well be written in computer code—code that's simultaneously compiled in research laboratories and displayed on classroom screens, driving both discovery and learning in a mutually-enriching cycle of innovation and education.