Signaling Systems: Transferring information without distortion

Despite employing diverse molecular mechanisms, many different cell signaling systems avoid losing information by transmitting it in a linear manner.

the cells building a multicellular organism sense chemical gradients to determine their position and send and receive chemical signals to communicate.

signaling is far more complex, with different pathways operating via different molecular components and biochemical mechanisms.

such complex signaling mechanisms can carry out sophisticated signal processing.

wonder why so many quantitative biologists neglected to study linear input-output signaling for so long

the consequences of this linearity included maximal information transmission and an increased robustness of the output to random downstream molecular events

Signaling pathways as linear transmitters

One challenge in biology is to make sense of the complexity of biological networks.

We find an unexpected convergence: the three pathways behave in some physiological contexts as linear signal transmitters.

linearity arises through different means in each pathway

Linearity is a desired property in engineering where it facilitates fidelity and superposition in signal transmission. Our findings illustrate how cells tune different complex networks to converge on the same behavior.

Cells must continually sense, interpret, and respond to their environment. This is orchestrated by signaling pathways: networks of multiple proteins that transmit signals and initiate cellular response

the canonical Wnt pathway, the ERK pathway, and the Tgfβ pathway have converged upon a shared strategy of linear signal transmission.

 

despite their distinct architectures, the three signaling pathways behave in some physiological contexts as linear transmitters.

complexity of each pathway might afford tunability, in the sense that parameters can be tuned to produce different behaviors in different contexts.

many biochemical processes are inherently nonlinear, meaning that linearity does not arise from a reduction in complexity.

linearity emerges from complex interactions

analogous to engineered systems, complexity in the biochemical pathways we analyzed here might have evolved in part to produce linearity.