From immunology research to every classroom
ModelIt was born in a university research lab where Dr. Tomas Helikar was building computational models of the human immune system. The same technology that helps scientists discover drugs and fight disease now helps K-12 students think like scientists.
A computational biologist asks a different question
Most scientists in Dr. Helikar's field build computational models to understand disease. He did too. But then he asked: what if students could use these same tools to understand the world around them?
Modeling the Human Immune System
Dr. Helikar's lab builds comprehensive computational models of CD4+ T-cell signaling, mapping how the immune system decides to fight infection. These models identify drug targets for rheumatoid arthritis, multiple sclerosis, and cancer.
Puniya et al., PLOS Computational Biology, 2021
Building Digital Twins of Immunity
His team co-authored the roadmap for building personalized digital twins of the immune system — computational replicas that could one day predict exactly how your body will respond to a treatment, a vaccine, or a new infection.
Laubenbacher, Helikar et al., NPJ Digital Medicine, 2022
Fighting COVID-19 with Computation
When COVID-19 hit, Dr. Helikar joined an international consortium building the COVID-19 Disease Map — a massive computational knowledge repository mapping how SARS-CoV-2 hijacks human cells. Computation met crisis.
Ostaszewski et al., Molecular Systems Biology, 2021
Discovering Drugs Through Models
The same digital modeling approach powers drug discovery in immuno-oncology. Dr. Helikar's meta-analysis of quantitative systems pharmacology models integrated with machine learning revealed how computational modeling accelerates finding new treatments.
Aghamiri, Amin & Helikar, J. Pharmacokinetics, 2021
From research breakthrough to classroom tool
This is the story of a technology that traveled from a university biochemistry lab to K-12 classrooms across the country. Every step was backed by peer-reviewed research.
The breakthrough: emergent decision-making
Published in the Proceedings of the National Academy of Sciences, Dr. Helikar demonstrated that cellular networks function as pattern recognition systems. Digital models can capture how cells make decisions — sharp, non-fuzzy classifications even in the face of noise. The mathematical foundation was laid.
Cell Collective is born
What if you didn't need to be a programmer to build a computational model? Dr. Helikar and his team created Cell Collective — a web-based platform where anyone could construct, simulate, and analyze digital models collaboratively. No code. No advanced math. Just science. Universities took notice — Carnegie Mellon, UCLA, Caltech, Brown, Columbia, and dozens more adopted it for systems biology courses.
Proof: it works in the curriculum
Cell Collective was successfully implemented both as a standalone In Silico Biology course and as modules integrated into existing biology courses. Students didn't just learn — they produced conference presentations and peer-reviewed publications. The integration model that would later power ModelIt was validated.
The neuroscience proof
Using fMRI brain imaging, researchers showed that students who simulated computational models built fundamentally different neural representations than students who simply read about the same systems. Even when test scores appeared similar, simulation activated deeper brain pathways. This was neuroscience-level proof: hands-on computational modeling changes how the brain processes science.
The manifesto: K-12 needs this
Writing in Trends in Molecular Medicine during the COVID-19 pandemic, Dr. Helikar made the case that changed everything. NGSS mandates computational modeling and systems thinking. But no tools existed that were both research-grade and accessible to students. Cell Collective occupied the critical middle ground. The question was no longer whether to bring it to K-12. It was how fast.
ModelIt goes to school
Discovery Collective launched ModelIt — 282 NGSS-aligned lessons spanning kindergarten through 12th grade. Every lesson ships with a Student Presentation, Student Activity Pack, and Teacher's Guide. The research-grade computational modeling platform that started in immunology now helps a kindergartner ask 'What do plants need to grow?' and a 12th grader model gene regulation in CRISPR systems. Same platform. Same scientific rigor. Every student.
"Students need access to research-grade modeling tools, not simplified educational toys. The gap between what students need and what they have access to is the problem we are solving."— Dr. Tomas Helikar, Trends in Molecular Medicine, 2021
The biomedical profession operates at the intersection of Biology, Technology, and Computation. Researchers build computational models of biological systems, simulate them, test predictions against real data, and refine. This is how drug discovery, precision medicine, and digital twins work.
ModelIt brings this exact cycle into K-12 classrooms. Students study biological systems, use computational technology to build models, apply computational thinking to analyze behavior, and revise their models based on evidence. They are doing real science — the same workflow used by researchers at universities and pharmaceutical labs worldwide.
10 core biotech modeling skills
Every ModelIt lesson develops a subset of 10 modeling skills, organized into five progressive categories. These mirror the competencies used by professional biomedical researchers and computational biologists. Students practice them across grade bands, building fluency from kindergarten through 12th grade.
Model Construction
Simulation Setup
Model Analysis
Simulation Analysis
Debugging & Revision
Model Construction → Simulation Setup → Model Analysis → Simulation Analysis → Debugging & Revision
Drop ModelIt into your existing unit
ModelIt is a supplement, not a replacement for your science curriculum. Each lesson stands alone, but it becomes even more powerful when students revisit their model throughout a unit. Six ways to weave it in:
At the start of a unit
Use the lesson to introduce the phenomenon. Students build a 'first draft' model based on what they already know. Save it.
After a reading or lab
Have students reopen their model and update it: 'Based on what you just learned, would you add a component? Change a relationship?' Takes 10–15 minutes.
As a "what-if" tool
When students ask questions during class, say: 'Great question — go test that in your model!' Students run new scenarios and report back.
For peer discussion
Partners compare models: 'Why does your model have this arrow and mine doesn't?' Differences spark scientific debate using evidence.
As a presentation tool
Students project their model and walk the class through it live — showing components, running scenarios, answering audience challenges in real time.
At the end of a unit
Compare Day 1 model to final model. The growth IS the assessment — students see how their thinking evolved.
282 lessons. Five frameworks. Every grade.
Every lesson is mapped to NGSS Performance Expectations and four additional national standards frameworks. Use the full alignment guide to plan units, fill in scope-and-sequence documents, or pitch ModelIt to your curriculum director.
Next Generation Science Standards
The primary science content standards. Each lesson targets one or more NGSS Performance Expectations.
International Society for Technology in Education
Technology and computational thinking. Lessons align to ISTE standards 1, 3, 4, 5, 6, and 7 through digital modeling, simulation, and data analysis.
National Health Science Standards
Career and technical education for health science pathways. Lessons address Foundation Standards 1, 3, 7, 9, and 11.
California Health Education Standards
Lessons addressing human body systems, disease, nutrition, and wellness align to grade-appropriate CA Health Ed content areas.
ModelIt Modeling Competencies
Ten skills across five categories that define computational modeling proficiency, K-12 to AP/honors.
Standards Alignment & Curriculum Guide
Full PDF with all 282 lessons, NGSS PE codes, ISTE standards, NCHSE foundation standards, biotech skill coverage, and CA Health Ed alignment by grade band and curriculum level.
Used by researchers at universities around the world
Researchers, teachers, and students across the world use Cell Collective — the same engine that powers ModelIt — to learn, build, and simulate biological models. The platform has been adopted at universities for systems biology, computational modeling, and biomedical research courses.
From research labs to K-12 classrooms
The same platform trusted by research universities is now available to K-12 students through ModelIt. Every lesson uses the Cell Collective engine, giving young learners access to the same computational tools used by professional scientists.
Measured, published, peer-reviewed
Every claim is backed by research. Here is what the data shows.
Learning gain for students using the platform, compared to 0% for the control group
Booth et al., CBE—Life Sciences Education, 2021
Gender bias detected. The platform produces equitable outcomes across all students.
Booth et al., 2021
Instructors across 37 institutions studied. Learning-focused features drove adoption (effect size 0.309).
Song, Helikar et al., CBE—LSE, 2023
Neuroimaging confirmed simulation activates deeper brain processing than reading alone
Clark, Helikar & Dauer, CBE—LSE, 2020
Built on the international standard for biological model exchange. Dr. Helikar co-authored the specification.
Keating et al., Molecular Systems Biology, 2020
Compliant with emerging NIH-funded standards for Credible, Understandable, Reproducible, Extensible models.
Sauro, Helikar et al., ArXiv, 2025
The peer-reviewed evidence base
These published studies, led by Dr. Tomas Helikar and his team at Helikar Labs (University of Nebraska-Lincoln) and Discovery Collective, document best practices for teaching with the Cell Collective / ModelIt platform.
Bergan-Roller, H. E., Galt, N. J., Chizinski, C. J., Helikar, T., & Dauer, J. T. (2018). Simulated computational model lesson improves foundational systems thinking skills and conceptual knowledge in biology students. BioScience, 68(8), 612–621.
https://doi.org/10.1093/biosci/biy054
Dauer, J., Dauer, J., Lucas, L., Helikar, T., & Long, T. (2022). Supporting university student learning of complex systems: An example of teaching the interactive processes that constitute photosynthesis. In O. Ben Zvi Assaraf & M.-C. P. J. Knippels (Eds.), Fostering understanding of complex systems in biology education (pp. 63–82). Springer.
https://doi.org/10.1007/978-3-030-98144-0_4
Lucas, L., Helikar, T., & Dauer, J. T. (2022). Revision as an essential step in modeling to support predicting, observing, and explaining cellular respiration system dynamics. International Journal of Science Education, 44(13), 2152–2179.
https://doi.org/10.1080/09500693.2022.2114815
King, G. P., Bergan-Roller, H. E., Galt, N. J., Helikar, T., & Dauer, J. T. (2019). Modelling activities integrating construction and simulation supported explanatory and evaluative reasoning. International Journal of Science Education, 41(13), 1764–1786.
https://doi.org/10.1080/09500693.2019.1640914
Booth, C. S., Song, C., Howell, M. E., Rasquinha, A., Saska, A., Helikar, R., Sikich, S. M., Couch, B. A., van Dijk, K., Roston, R. L., & Helikar, T. (2021). Teaching metabolism in upper-division undergraduate biochemistry courses using online computational systems and dynamical models improves student performance. CBE—Life Sciences Education, 20(1), ar13.
https://doi.org/10.1187/cbe.20-05-0105
Clark, C. A. C., Helikar, T., & Dauer, J. T. (2020). Simulating a computational biological model, rather than reading, elicits changes in brain activity during biological reasoning. CBE—Life Sciences Education, 19(3), ar45.
https://doi.org/10.1187/cbe.19-11-0237
Bring research-grade science to your students
282 lessons. Every grade. Complete materials. Built on peer-reviewed computational biology research.