This paper is pretty technical but might be fun for people who are into signaling systems inside cells. Just my opinion: this is the kind of paper (and scientific work) that should interest those who care about design in biology.
Abstract:
Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations in which the concentrations of monomer inputs determine the concentrations of dimer outputs. Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks of 3–6 monomers are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough, can perform nearly all potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.
Highlights:
• Competitive dimerization networks can be considered as signal-processing systems
• These networks can compute diverse non-monotonic, multi-input functions
• The concentrations of “accessory” monomers tailor computations for different contexts
• Networks with random interactions can be tuned to perform most functions
Excerpt from Introduction (reference citations removed for readability):
Despite their prevalence and significance, the computational capabilities of dimerization networks remain poorly understood. Dimerization is a relatively limited type of biochemical interaction that does not consume energy and is stoichiometric rather than catalytic. In contrast to enzymatic networks and transcriptional regulation, dimerization is incapable of amplifying the magnitude of input signals. While the experimental characterization of particular dimerization networks in nature has provided great insights, and some dimerization networks have recently been studied computationally, we lack a fundamental, systems-level understanding of dimerization network computation—including which computations are (and are not) possible, to what extent a single network can perform different computations in different cell contexts, and how parameters such as network size and connectivity influence their computational power. Addressing these questions is essential for understanding the prevalence, architectures, expression patterns, and signal-processing functions of natural dimerization networks, as well as for engineering synthetic dimerization networks.