We have generated a single-chain antibody which recognizes murine CR1/2 and carries a genetically fused influenza hemagglutinin derived peptide. Theoretically such a construct is able to crosslink the B cell antigen receptor and CR1/2 on peptide specific B cells. The construct was able to reach its CR1/2 positive target cells, yet intraperitoneal delivery of the construct elicited an IgM response only slightly exceeding that induced by the free peptide. Providing T cell help by the injection of peptide specific lymphocytes did not alter the response in essence, that is anti-peptide IgG was not detectable even after booster immunizations. When used as a booster vaccine following injection of the peptide in adjuvant, the construct even inhibited the development of IgG1 and IgG3 anti-peptide antibodies.
Diverse complex systems such as cells, Internet and society can be mapped into networks by simplifying each constituent as a node and their interaction as a link. Traditionally it has been considered that these networks are random, but recent series of studies show that they are far from being random and have common inhomogeneous topology through generic self-organizing process. In this chapter, we briefly introduce the network analysis methods which were re-developed in statistical physics community recently. First, we introduce basic complex network models such as Erdős-Rènyi model, small-world model, scale-free model which were developed to describe complex systems. And then, we applied these methods to biological system, such as metabolic network and protein-protein interaction network of E. coli. We measure the global and local characteristics of the network structure. Finally we briefly review recent works on biological networks especially on dynamic aspect.
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There are even more aspects of the cell biology that are critical for understanding development and differentiation. While gene-to-gene interactions are essential for the cells to differentiate, cell-to-cell communications are crucial for the embryo to balance the required quantity of each cell type, and to develop tissues and organs. As an example, the ICM and EPI cells secrete the Fgf4 signal, which binds to the Fgfr2 receptor on the membrane of TE and PE cells (Figs. 5 and 6). The development of TE and PE cells are significantly influenced by this signal [31, 32]; for instance the increased Fgf4 concentration results in enhanced PE and diminished EPI cells [33]. As a result, the proportion of the cells that differentiate into either EPI or PE would be balanced, which is another mechanism of developmental robustness. In absence of signals and intercellular communication, development would terminate in a salt-and-pepper mixture of differentiated cell types without any pattern. 2ff7e9595c
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