AI Article Synopsis
- Modern stem cell research increasingly integrates quantitative sciences, like bioinformatics and biostatistics, to identify factors crucial for differentiation and drug discovery.
- Understanding differentiation and reprogramming requires both qualitative and quantitative models to grasp complex biological systems, though current models often yield modest predictions.
- The review discusses popular models related to stem cell behavior and anticipates new insights from computational modeling in the future.
Article Abstract
Modern stem cell research, as well as other fields of contemporary biology involves quantitative sciences in many ways. Identifying candidates for key differentiation or reprogramming factors, tracing global transcriptome changes, or finding drugs is now broadly involves bioinformatics and biostatistics. However, the next key step, understanding the underlying reasons and establishing causal links leading to differentiation or reprogramming requires qualitative and quantitative biological models describing complex biological systems. Currently, quantitative modeling is a challenging science, capable to deliver rather modest results or predictions. What model types are the most popular and what features of stem cell behavior they are capturing? What new insights do we expect from the computational modeling of stem cells in the foreseeable future? Current review attempts to approach these essential questions by considering published quantitative models and solutions emerging in the area of stem cell research.
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| http://dx.doi.org/10.1016/bs.ctdb.2015.11.040 | DOI Listing |
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