In a groundbreaking development, scientists from leading institutions, including Johns Hopkins Medicine and Indiana University, have unveiled a computer program capable of simulating human and animal cell behavior across various body parts. This innovative tool, designed to enhance biological process testing and drug response predictions, promises to reduce the need for costly live cell experiments.
The project, spearheaded by researchers from Johns Hopkins Medicine, Indiana University, the University of Maryland School of Medicine, and Oregon Health & Science University, aims to create a “digital twin” for testing drug effects on conditions such as cancer. The findings, funded by the Jayne Koskinas Ted Giovanis Foundation and the National Institutes of Health, were published on July 25 in the journal Cell.
Advancing Biological Simulations
Dr. Genevieve Stein-O’Brien, the Terkowitz Family Rising Professor of Neuroscience and Neurology at Johns Hopkins University School of Medicine, explained that the research originated from a workshop focused on an earlier software version named PhysiCell. Developed by Dr. Paul Macklin, an engineering professor at Indiana University, PhysiCell uses “agents” or math robots to mimic cell behavior based on DNA and RNA rules.
These agents allow scientists to digitally manipulate cell interactions with other cells and environmental factors, such as therapeutics and oxygen. By tracking these interactions, researchers can observe tumor emergence, immune system responses, and brain cell organization.
Making Complex Models Accessible
Dr. Macklin highlighted that while traditional computer modeling programs require advanced mathematical and coding skills, PhysiCell introduces a new “grammar” that simplifies the process for biologists. “It used to take months to write the code for these models,” Macklin noted. “Now, we can teach other scientists to create a basic immunology model in an hour or two.”
This user-friendly approach enables the modeling of spatial transcriptomics, a significant scientific goal, to visualize cell types and functions in 3D tissue and tumor replicas.
From Cells to Circuits
Stein-O’Brien’s lab, in collaboration with Dr. Daniel Bergman from the University of Maryland School of Medicine, is advancing the software to simulate complex brain circuits. The new coding grammar, described as “an Excel spreadsheet,” aligns cell types with rules in human-readable syntax. This innovation allows for automatic translation into mathematical equations that guide cell behavior.
David Zhou, a former Johns Hopkins University Neuroscience undergraduate, and Zachary Nicholas, a Ph.D. candidate, contributed to developing a brain development model using data from the Allen Brain Atlas. This model represents a pioneering effort in simulating brain cell interactions over time.
Real-World Applications and Validation
In a validation experiment, Dr. Jeanette Johnson, a postdoctoral fellow, used the program to simulate how macrophages, a type of immune cell, invade breast tumors by increasing expression of the EGFR pathway, which typically promotes cancer growth. The simulation accurately predicted tumor growth due to increased cell movement, confirmed by laboratory observations.
“We still have a lot of work to do to add more cell behavior data to the program,” Johnson stated, emphasizing the project’s potential as a “virtual cell laboratory.”
Future Directions and Implications
The team envisions using artificial intelligence to further refine simulation models, enhancing the digital twin’s accuracy and applicability in medical research. This approach could prioritize hypotheses and therapeutic targets before conducting live cell experiments.
Funding for the research came from numerous sources, including the National Institutes of Health, the Lustgarten Foundation, and the National Foundation for Cancer Research, among others. The collaborative effort involved a diverse team of scientists across multiple institutions.
As the project progresses, the virtual cell lab promises to transform how researchers approach drug testing and disease modeling, potentially leading to more efficient and targeted therapeutic strategies.
