Release date: 2016-07-28
Nowadays, the “Parts List†provided by the Human Genome Project for researchers has become a familiar concept, and scientists are interested in the genes discovered by far more than the genome project; computer model technology, which will take multiple biological scales. Incorporating clinical data into new computer algorithms may provide us with a way to propose some research hypotheses that can be tested. In the past 10 years, more and more biologists and clinical researchers have begun to enthusiastically pursue computer modeling research, which will undoubtedly help researchers to swim in the ocean of biological data.
James Glazier: CompuCell3D (a three-dimensional C++ software environment that integrates multiple mathematical models to simulate multiple biological complexity issues)
Ten years ago, biologists might pass the data to a data modeler for analysis, giving a theoretical explanation to clarify whether the results could be returned to the clinical situation; now from Indiana University, Bloomington Researcher James Glazier and his colleagues spent more than 15 years developing and applying a development-based model platform called CompuCell3D that helps biologists use data at the molecular, cellular, and organizational levels. To build your own model system.
In a recent study, researchers used the CompuCell3D platform to study the etiology of polycystic kidney disease. Researcher Robert Bacallao pointed out that cell adhesion molecules in diseased kidney tissue often behave differently than normal kidney tissue. These adhesion molecules play two major roles, which help the cells unite and send signals to other cells to grow in the blank space, but researchers are not aware of the role of adhesion molecules in the pathogenesis of the disease.
The researchers say that the simulation process takes time after surgery, but it can help reveal the mechanisms involved in the disease and clarify which defects cause differences in the behavior and shape of the kidney cysts, and the researchers can also be in the patient's kidneys. These predictions were confirmed in the cells.
Using other programs requires writing 40,000 lines of code to simulate the kidneys, but using the CompuCell3D platform, you only need to write 100 lines of code to simulate the kidneys. Later researchers Glazier will continue to "refin" the kidney model. The new platform is open, so it can serve as a template for more model research; according to Robert Bacallao, it takes 10-20 years to perform the necessary experiments on mice to reach the same conclusion.
Researcher Glazier has formed a team of modelers to work with clinicians. He said that when we often solve problems in tissues such as the eyes, kidneys or liver, a special "workflow" is slowly formed. "And the researchers believe that they will standardize the entire process and make the CompuCell3D platform more widely used.
Kristin Swanson: Patient-specific model
How do you determine the effectiveness of a particular treatment being performed by a cancer patient? Do cancer patients need another treatment? The clinician compares the response of each patient to the average response data of all patients in the clinical study, but the problem is that the researchers do not know the position of any patient on the curve.
As a graduate of mathematics biology who graduated in the 1990s, Kristin Swanson, a researcher from the University of Washington, analyzed the individual patient's condition and choice of therapy, and she was also immersed in the patient's clinical decision-making study, provided by Swanson. During her medical service, she underwent an in-depth study of glioblastoma, a lethal brain tumor with an average survival of only 15 months.
Swanson's father died of lung metastases in lung cancer, partly because of this reason she decided to study glioblastoma, and researchers Swanson and colleagues added a series of imaging data to a "virtual controller". To help understand the patient's response to the body's tumors without receiving therapy and standard therapy, this virtual control can give clinicians a better baseline to help measure patient response to treatment changes.
When exploring new therapies, researchers need to know what tools should be used to help with development; recently, researchers used virtual control methods to study four patients who received new experimental therapies for glioblastoma, and the results were in the patient's body. Early signs of patient response were discovered, and these signals were not detected using standard tools.
In a study of approximately 250 patients with glioblastoma, the team of researchers Swanson used a special model approach to classify patients based on the marginal dispersion of the patient's body tumors and found that the body's tumor margins were the most diffuse. The patient is least suitable for surgery, but should take normal first-line treatment. In the field of cancer research, researchers do not take too long to match patients with appropriate therapies. For many years, researcher Swanson has been a mathematician in the field of oncology research, but with the constant field of mathematical oncology Development, more and more quantitative scientists have also joined the research of clinical decision-making.
Paul Macklin: In situ ductal carcinoma
Since he was a postdoctoral fellow from the Texas Health Science Center in 2007, researcher Paul Macklin is eager to use model research in clinical applications. He and his colleagues met the mammary gland from the MD Anderson Cancer Center. Pathologists Mary Edgerton, Mary Edgerton and Paul Macklin share the same hobbies, so they began collaborating to study ductal carcinoma in situ. In situ ductal carcinoma (DCIS) is a precursor form of breast cancer, and ductal carcinoma in situ is present in the breast. In the milk duct, but these cancer cells easily invade into the surrounding breast tissue.
Macklin, a researcher from the University of Southern California, and colleagues began to simulate the natural process of ductal carcinoma in situ. First, they built a two-dimensional model of a single DCIS cell in a mammary duct. The researchers said that usually a single biopsy can Provide some important clues to understand the balance between cell proliferation and cell death. In order to predict the progression of ductal carcinoma in situ, the researchers also added parameters such as cell cycle time and cell life to conduct more in-depth research and analysis. .
By performing a special simulation, the researchers found that ductal carcinoma in situ actually grew very slowly and stably, which may reflect the findings of the researchers in the clinic and help the researchers continue to follow up the study, which Macklin and colleagues later constructed. A more complex and malleable model for the mammary duct wall, it was found that in situ ductal cancer cells can slowly penetrate into the duct wall cells by regularly "chewing" the mammary duct wall.
Researcher Macklin said that some things happen, either changing the phenotype of the cell or changing the communication between cells in the whole system; the establishment of a new model will clearly reveal the mechanism of communication between different cell types, and now we can not only Simulating tumor cells, we can also simulate the entire system. This year, researcher Macklin has released new code that can simulate 10-15 in situ ductal cancer microenvironment divergence signals, and they plan to release more code next.
Despite the complexity of the model developed by Macklin, he is very surprised that early models can be paired with clinical data very well. Of course, it is important to develop a new treatment for the late use of the model to study the pathogenesis of cancer.
Source: Bio Valley
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