Mechanistic Models Predict Efficacy of CCR5-Deficient Stem Cell Transplants in HIV Patient Populations

Combination antiretroviral therapy (cART) effectively suppresses viral load in HIV‐infected individuals, but it is not a cure. Bone marrow transplants using HIV‐resistant stem cells have renewed hope that cure is achievable but key questions remain e.g., what percentage of stem cells must be HIV‐resistant to achieve cure?. As few patients have undergone transplants, we built a mechanistic model of HIV/AIDS to approach this problem. The model includes major players of infection, reproduces the complete course of the disease, and simulates crucial components of clinical treatments, such as cART, irradiation, host recovery, gene augmentation, and donor chimerism. Using clinical data from 172 cART‐naïve HIV‐infected individuals, we created virtual populations to predict performance of CCR5‐deficient stem‐cell therapies and explore interpatient variability. We validated our model against a published clinical study of CCR5‐modified T‐cell therapy. Our model predicted that donor chimerism must exceed 75% to achieve 90% probability of cure across patient populations.

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

Where is VEGF in the body? A meta-analysis of VEGF distribution in cancer

Vascular endothelial growth factor (VEGF) is a major target for the inhibition of tumour vascularisation and the treatment of human cancer. Many tumours produce large quantities of VEGF, and as a result, diagnosis and prognosis of cancer may be predicted by measuring changes in VEGF concentrations in blood. In blood, the VEGF may be located in the plasma, or in the blood-borne cells and formed elements, in particular, platelets and leukocytes. In this study, we collate the measurements of VEGF in platelets, leukocytes, plasma and serum for breast, prostate, colorectal and other cancers. In addition, we analysed the concentration of VEGF in tumour tissue itself, as well as for other tissues in the human body. Although the concentration of VEGF in tumours is high, the size of tumours is small compared to other tissues, in particular, skeletal muscle. Thus, the total quantity of VEGF in tumours and in blood is small compared to the quantity in muscles. This large reservoir of VEGF may have important implications for the treatment of cancer.

Where is VEGF in the body? A meta-analysis of VEGF distribution in cancer

Vascular endothelial growth factor (VEGF) is a major target for the inhibition of tumour vascularisation and the treatment of human cancer. Many tumours produce large quantities of VEGF, and as a result, diagnosis and prognosis of cancer may be predicted by measuring changes in VEGF concentrations in blood. In blood, the VEGF may be located in the plasma, or in the blood-borne cells and formed elements, in particular, platelets and leukocytes. In this study, we collate the measurements of VEGF in platelets, leukocytes, plasma and serum for breast, prostate, colorectal and other cancers. In addition, we analysed the concentration of VEGF in tumour tissue itself, as well as for other tissues in the human body. Although the concentration of VEGF in tumours is high, the size of tumours is small compared to other tissues, in particular, skeletal muscle. Thus, the total quantity of VEGF in tumours and in blood is small compared to the quantity in muscles. This large reservoir of VEGF may have important implications for the treatment of cancer.