Living organisms rely on numerous biological processes that involve communication between cells and molecular components. This communication occurs through various mechanisms such as diffusion, electrical depolarization, and mechanical waves. Researchers at Yale University have recently conducted a study aimed at calculating the energetic cost of this information transfer between cells and molecular components. Their findings, published in Physical Review Letters, introduce a new tool that could potentially enhance our understanding of cellular networks and their functions.
This study by Benjamin B. Machta and Samuel J. Bryant draws inspiration from earlier research conducted in the late 1990s by Simon Laughlin and his collaborators. Laughlin and his team attempted to determine the amount of energy neurons expend when transmitting information. They discovered that this energy expenditure ranged from 10^4 to 10^7 kBT/bit, which is significantly higher than the theoretical lower limit known as the Landauer bound. This led Machta and Bryant to investigate whether this apparent inefficiency was due to biology being wasteful or if there were other factors at play.
Another objective of this study was to understand the reasons behind the use of different physical mechanisms for communication between molecular systems. For example, neurons primarily communicate through electrical signals, while other cells use diffusion of chemicals. Machta and Bryant aimed to determine the energy cost per bit in each communication regime. Their calculations considered information sent through a physical channel, from a sender to a receiver. Thermal noise in the cellular environment was taken into account as well.
The researchers employed relatively simple models to estimate energy costs, which allowed them to establish conservative lower bounds. Thermal noise presented a challenge, but the team was able to calculate its spectrum using the fluctuation dissipation theorem. The estimations took into account the geometry and physical details of the system. The researchers discovered that the cost per bit increased with factors such as the size of the sender and receiver and the distance between them. For ion channels, which are a few nanometers across but transmit information over microns, the cost could be orders of magnitude higher than the theoretical lower bound.
Overall, the calculations performed by Machta and Bryant confirm the high energetic cost associated with information transfer between cells. These findings could potentially provide an explanation for the high energy consumption observed in experimental studies of information processing. While their explanation is less fundamental than the Landauer bound, as it depends on the specific details of the system, it suggests that biology may face real energy limitations. These calculations are not sufficient to determine efficiency in any particular system, but they do highlight the substantial energy costs associated with sending information through space.
Moving forward, the research by Machta and Bryant has the potential to inform new biological studies. The researchers introduced a “phase diagram” that represents situations where specific communication strategies, such as electrical signaling or chemical diffusion, are optimal. This diagram could aid in understanding the design principles behind different cell signaling strategies. It may explain why neurons use chemical diffusion at synapses but switch to electrical signals when transmitting information over longer distances. Additionally, studying the energetics of specific signal transduction systems could provide further insights into the flow of information within complex networks.
The study conducted by Machta and Bryant sheds light on the energetic cost of information transfer in cellular networks. It challenges the notion of inefficiency in biological systems and highlights the importance of considering the specific details and geometry of the system when analyzing energy costs. This research opens up new avenues for investigating the design principles of cell signaling strategies and understanding the flow of information in complex biological networks.
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