Input-output biomolecular systems

Abstract

The ability of cells to sense and respond to their environment is encoded in biomolecular reaction networks, in which information travels through processes such as production, modification, and removal of biomolecules. These reaction networks can be modeled as input-output systems, where the input, state and output variables are concentrations of the biomolecules involved in these reactions. Tools from non-linear dynamics and control theory can be leveraged to analyze and control these systems. In this thesis, we study two key biomolecular networks. In part 1 of this thesis, we study the input-output behavior of signaling systems, which are responsible for the transmission of information both from outside and from within the cells, and are ubiquitous, playing a role in cell cycle progression, survival, growth, differentiation and apoptosis. A signaling pathway transmits information from an upstream system to downstream systems, ideally in a unidirectional fashion.

A key obstacle to unidirectional transmission is retroactivity, the additional reaction flux that affects a system once its species interact with those of downstream systems. In this work, we identify signaling architectures that can overcome retroactivity, allowing unidirectional transmission of signals. These findings can be used to decompose natural signal transduction networks into modules, and at the same time, they establish a library of devices that can be used in synthetic biology to facilitate modular circuit design. In part 2 of this thesis, we design inputs to trigger a transition of cell-fate from one cell type to another. The process of cell-fate decision-making is often modeled by means of multistable gene regulatory networks, where different stable steady states represent distinct cell phenotypes. In this thesis, we provide theoretical results that guide the selection of inputs that trigger a transition, i.e., reprogram the network, to a desired stable steady state.

Our results depend uniquely on the structure of the network and are independent of specific parameter values. We demonstrate these results by means of several examples, including models of the extended network controlling stem-cell maintenance and differentiation.