Living organisms rely on various biological processes that involve the communication between cells and molecular components. These processes, such as diffusion and electrical depolarization, play a crucial role in supporting the behaviors, physiology, and existence of organisms. Researchers at Yale University have recently conducted a study to determine the energetic cost associated with the transfer of information between cells and molecular components. This study introduces a new tool that could help analyze cellular networks and gain a better understanding of their function.
The recent work by Benjamin B. Machta and Samuel J. Bryant builds upon efforts made in the late 90s by Simon Laughlin and his collaborators. Laughlin’s research group aimed to experimentally determine the energy expenditure of neurons when transmitting information. According to Laughlin’s findings, neurons spend a significant amount of energy in the range of 104-107 KBT/bit. This energy cost is far higher than the theoretical “fundamental” limit known as the Landauer bound.
Machta and Bryant’s study sought to understand the reasons behind the high energetic costs of molecular systems when communicating using distinct physical mechanisms. While neurons primarily use electrical signals for communication, other types of cells employ diffusion of chemicals. The researchers aimed to determine the energy cost per bit for each communication mechanism.
The calculations in the study involved considering a physical channel in which currents of physical particles and electrical charges are carried according to the cell’s physics. The team also factored in the presence of thermal noise in the cellular environment, which affects the channel’s performance. By using relatively simple models, the researchers established conservative lower bounds on the energy required for information transfer. A geometric prefactor multiplied by “KBT/bit” was introduced to account for the geometry of the sender and receiver. This prefactor highlighted the significance of the size of the sender and receiver in determining the energy cost per bit.
Machta and Bryant’s calculations confirmed the high energy cost associated with intercellular information transfer. These findings may provide the groundwork for explaining the costly nature of information processing observed in experimental studies. While their explanation may not be as “fundamental” as the Landauer bound, it emphasizes the importance of geometric details and specific characteristics of neurons and ion channels.
The study also introduced a phase diagram, which represents optimal situations for different communication strategies. This phase diagram could serve as a valuable tool for understanding the design principles of various cell signaling strategies. For example, it could explain why neurons use chemical diffusion at synapses but resort to electrical signals when transmitting information over larger distances. Additionally, the diagram may shed light on why E. coli bacteria rely on diffusion to communicate information about their chemical environment.
Moving forward, Machta and his colleagues aim to apply their framework to concrete signal transduction systems. They plan to explore information processing networks and understand the flow of information within these networks. This application of their tool could lead to further insights into the energetics of biological systems and pave the way for new biological studies.
The study conducted by Machta and Bryant reveals the high energetic costs associated with cellular communication. Their research provides valuable insights into the energy requirements of information transmission and highlights the significance of geometric factors and specific characteristics of biological systems. By introducing a new tool and a phase diagram, the researchers offer a means to analyze and better understand the complex communication strategies employed by living organisms. This work opens up avenues for future research and deeper exploration into the energetics of cellular networks.
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