Living organisms rely on numerous biological processes that involve the communication between cells and other molecular components. These processes are crucial for the proper functioning of organisms, and understanding their energetic costs is of great significance. In a recent study conducted at Yale University, researchers introduced a new tool that could be used to analyze cellular networks and gain a better understanding of their function.
The research by Benjamin B. Machta and Samuel J. Bryant builds upon earlier studies conducted in the late 90s by Simon Laughlin and his collaborators. Laughlin and his team attempted to determine the amount of energy neurons expend when transmitting information. Their findings revealed that the energy expenditure ranged between 104-107 KBT/bit, which is significantly higher than the “fundamental” bound known as the Landauer bound. This raised questions about the potential wastefulness of biological systems and whether there are other costs that need to be considered.
Machta and Bryant’s study aimed to calculate the energetic cost of information transfer and explore the reasons behind the use of distinct physical mechanisms in different situations. For example, while neurons primarily communicate through electrical signals, other types of cells rely on diffusion of chemicals. By understanding the energy cost per bit for each mechanism, the researchers sought to identify the regime in which they are most efficient.
The calculations performed by Machta and Bryant considered a physical channel in which currents of physical particles and electrical charges are carried based on a cell’s physics. They also took into account the thermal noise present in the cellular environment. By using relatively simple models, the researchers were able to establish conservative lower bounds on the energy required for information transfer in biological systems. The geometric prefactor, which depends on the size of the sender and receiver, played a crucial role in determining the cost per bit. This factor could vary significantly depending on the distance between the sender and receiver. For ion channels, which are a few nanometers across but transmit information over longer distances, the cost per bit could be much higher than the predicted lower bound.
The results of the study confirmed the substantial energetic cost associated with the transfer of information between cells. These findings offer a potential explanation for the high energy expenditure observed in experimental studies of information processing. While not as fundamental as the Landauer bound, the calculations provide insights into the efficiency and energy limitations of biological systems. The researchers suggest that the details of neurons and ion channels play a significant role in determining their efficiency.
The study by Machta and Bryant has the potential to inform future biological studies. The researchers introduced a ‘phase diagram’ that represents the optimal use of different communication strategies, such as electrical signaling or chemical diffusion, in various situations. This diagram could help uncover the design principles behind different cell signaling strategies. For example, it could shed light on why neurons use chemical diffusion at synapses but rely on electrical signals for long-distance information transmission. Additionally, it could explain why certain bacteria, like E. coli, utilize diffusion to communicate information about their chemical environment. The researchers are now focused on applying their framework to concrete signal transduction systems and understanding the flow of information in complex networks.
The energetic cost of information transfer between cells is a complex and significant aspect of cellular networks. The study conducted by Machta and Bryant introduces a new tool for calculating these costs and sheds light on the efficiency of biological systems. By understanding the energetic costs associated with various communication mechanisms, researchers can gain insights into the design principles and limitations of cellular networks. This knowledge has the potential to pave the way for future advancements in our understanding of biological processes and help us unravel the complexities of living organisms.