The motivation for this book is to provide an approach for understanding and integrating the complex functions of cells that shape tissues and drive growth and differentiation. Correlative information in the catalogs of DNA sequences, mRNA levels, interactomes, and biochemical reactions involved in complex cellular functions provides an insufficient understanding. Having the parts list of a complex machine does not enable one to assemble that machine, much less to understand how it works. In contrast, a bioengineering approach that focuses on the physical aspects of cells and tissues provides only a part of the function. A detailed engineering diagram of the steps involved and how the physical parameters relate to the biochemical functions is needed to understand most cell functions. For example, the biochemical reactions involved in the uncontrolled cell proliferation of cancer cells are known, as are the physical behaviors of tumor cells, but how physical signals from the cell environment are integrated with the biochemistry of growth is not known. In normal cells, physical feedback from matrix rigidity and morphology controls normal cell growth, whereas in cancer cells, those same parameters are misread, resulting in uncontrolled growth. By considering the reverse engineering of complex cell functions as a problem similar to reverse engineering a complex automobile, it should be possible to understand cellular functions.

The premise of this book is that cells are small, self-replicating machines that exploit fundamental physical and physical–chemical principles of mesoscale objects to pass on the DNA of the organism that they form. From the revolution in molecular biology, we know the sequences of many genomes, as well as the sequences of the mRNAs in many specific cell types, which identify the proteins that are present. Further, we know many of the mutations that correlate with specific genetic diseases or altered phenotypes. Thus, we possess a cellular parts list. Fortunately, there are now ways to measure subcellular forces, protein positions at the nanometer level, and protein dynamics. By coupling those tools with real-time observations of cell functions, it will be possible to determine the steps in very complex functions.

Our approach is to model the cell as a complex machine that has been selected for robustness over many millions of years.