In-silico models of stem cell and developmental systems

Abstract

Understanding how developmental systems evolve over time is a key question in stem cell and developmental biology research. However, due to hurdles of existing experimental techniques, our understanding of these systems as a whole remains partial and coarse. In recent years, we have been constructing in-silico models that synthesize experimental knowledge using software engineering tools. Our approach integrates known isolated mechanisms with simplified assumptions where the knowledge is limited. This has proven to be a powerful, yet underutilized, tool to analyze the developmental process. The models provide a means to study development in-silico by altering the model’s specifications, and thereby predict unforeseen phenomena to guide future experimental trials. To date, three organs from diverse evolutionary organisms have been modeled: the mouse pancreas, the C. elegans gonad, and partial rodent brain development. Analysis and execution of the models recapitulated the development of the organs, anticipated known experimental results and gave rise to novel testable predictions. Some of these results had already been validated experimentally. In this paper, I review our efforts in realistic in-silico modeling of stem cell research and developmental biology and discuss achievements and challenges. I envision that in the future, in-silico models as presented in this paper would become a common and useful technique for research in developmental biology and related research fields, particularly regenerative medicine, tissue engineering and cancer therapeutics.

A Computational Model of Cellular Engraftment on Lung Scaffolds

“…This complements other recent studies on the use of computational modeling to elucidate cellular behavior such as genetic expression in individual stem cells, 21,22 the role of stem cells in tumor development and metastasis, 23,24 and embryogenesis in the pancreas. 25 …”

Calibrant-Free Analyte Quantitation via a Variable Velocity Flow Cell

“…16 Convective-driven immunosorbent assays function through analyte complexation at antibody-functionalized addresses, 46 typically under nonequilibrium conditions. 47 Depending on binding kinetics, this process may not necessarily be transport limited and results in constantly changing address areas and analyte affinity, and depleted protein concentrations ( C o ) at downstream addresses.…”

“…These three calibration methods address the historical needs of accurate analyte quantitation, but as the benefits of simultaneously monitoring ever increasing numbers of analytes – analyte panels – are contemplated, 16–23 implementation of historical calibration methods becomes more awkward 19 and, in some cases, intractable. For example, when attempting to analyze hundreds of markers in a limited-volume sample in which matrix effects cannot be easily overcome, it is not possible to employ a traditional calibration scheme.…”

PEAR1 gene polymorphism in a Chinese pedigree with pulmonary thromboembolism

“…[1] PTE usually develops during pathological processes of hemostasis, coagulation, and anticoagulation disorders, and platelets are the basic factors underlying these functions and exerting very important roles in thrombosis. [2–5] As a platelet aggregation receptor, platelet endothelial aggregation receptor-1 ( PEAR1 ) is mainly distributed in the platelet membrane and expressed in unactivated platelets.…”