The Center for Computational Life Sciences and Biology (CLSB) focuses on the operation of the smallest biological machines: proteins and RNA. Broad computational and theoretical perspectives are used in studies that vary from basic physical principles of molecular motions and reaction to the use of machine learning algorithms and statistical approaches to make predictions at the genomic scale. Theory and software are developed to address the vast range of time scales of biophysical motions and biochemical reactions (from femto seconds (10-15 s) to seconds). We study protein folding allosteric transitions, ion migration through channels, and protein ligand interactions by novel methods for coarse graining times and trajectory arc-lengths. We have recently extended our biophysical investigations to include interactions of pairs of molecules (e.g. protein-RNA interactions) an essential component of living systems, that allows for transmission of information, building of networks, and enhancement in complexity which is required for better control of cellular processes. On another front we are developing a machine learning approach and software for automated prediction of protein structures using homology (folding proteins according to their sequences using known shapes of evolutionary related proteins). We are expanding this study to investigate plausible evolutionary processes focusing on the significant stability of protein shapes to changes in the sequences. We introduce the network of sequence flow, a network that provides a minimal framework to study the dynamics of protein evolution.