Signal integration and information transfer in an allosterically regulated network

Abstract

10A biological reaction network may serve multiple purposes, processing more than one input and impacting downstream processes via more than one output. These networks operate in a dynamic cellular environment in which the levels of network components may change within cells and across cells. Recent evidence suggests that protein concentration variability could explain cell fate decisions. However, systems with multiple inputs, multiple outputs, and changing input concentrations have not been studied in detail due to their complexity. Here, we take a systems biochemistry approach, combining physiochemical modeling and information theory, to investigate how cyclooxygenase-2 (COX-2) processes simultaneous input signals within a complex interaction network. We find that changes in input levels affect the amount of information transmitted by the network, as does the correlation between those inputs. This, and the allosteric regulation of COX-2 by its substrates, allows it to act as a signal integrator that is most sensitive to changes in relative input levels.

Biphasic responses in cell signalling: A unified approach

“…Other forms of coupling are possible with enzymatic modifications. For example, a single enzyme can effect modifications on multiple substrates allowing for a sophisticated degree of cross talk and coupling between pathways [45,46,47,48]. We studied the propensity for biphasic responses in the system with enzyme sharing between two covalent modification cycles.…”

Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells

“…Analysis of the transfer of information in biochemical networks by entropy-based methods pointed to the particular importance of noise (Cheong et al, 2011;Shockley et al, 2019;Suderman et al, 2017). In particular, Suderman et al (2017) investigated information transfer at the single-cell and the cell-population level.…”

Emergent signal execution modes in biochemical reaction networks calibrated to experimental data

“…Seminal works in the field have shown that the maximum channel capacity of a biochemical process is context dependent and could require the cooperativity of multiple cells to achieve actual information transfer 21 . In recent work, our laboratory showed a non-linear dependence between channel capacity and reaction flux through a network 22 . Despite these advances to explore network-driven processes, a truly systems-level interpretation of non-equilibrium dynamics in a network and how these transient dynamics are impacted by parameter uncertainty remains a standing challenge in the field.…”

“…Multiple approaches have been used to extract mechanistic insights from mathematical models of network-driven processes. These approaches range from simple tracking of one or multiple species in the network [14][15][16][17][18] to more complex applications, such as Information Theoretic concepts to estimate the channel capacity between nodes in a network [19][20][21][22] . The latter approaches have provided a quantitative paradigm to study input/output relationships in networks [20][21][22][23][24] .…”

“…These approaches range from simple tracking of one or multiple species in the network [14][15][16][17][18] to more complex applications, such as Information Theoretic concepts to estimate the channel capacity between nodes in a network [19][20][21][22] . The latter approaches have provided a quantitative paradigm to study input/output relationships in networks [20][21][22][23][24] . Seminal works in the field have shown that the maximum channel capacity of a biochemical process is context dependent and could require the cooperativity of multiple cells to achieve actual information transfer 21 .…”