Sorkin and colleagues have derived an equivalent of the second law of thermodynamics for living systems, which establishes a relation between such a cell’s active uptake of energy and its random-looking path in terms of entropy production.
The clearest picture yet of LUCA suggests it was a relatively complex organism living 4.2 billion years ago, a time long considered too harsh for life to flourish.
Yizhi 'Patrick' Cai of the University of Manchester is coordinating a global effort to write a complete synthetic yeast genome. The resulting cell would be the artificial life most closely related to humans to date.
Physicist Sara Imari Walker is using principles of physics to redefine the concept of life. She introduces Assembly Theory, which measures molecular complexity to distinguish living from non-living systems. This approach could help detect unfamiliar life forms on other planets and better understand life on Earth.
The paper proposes the "law of increasing functional information," a new law of nature that could help explain the evolution of complex systems across multiple scales in the universe, from atoms and molecules to stars and brains.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
An article by Phillip Morrison exploring the concept of machine thinking in the age of computers. The author discusses the recurring question of why life is different from non-life, with each new generation of machines that simulate aspects of life.
P1. Loss function. In any evolving system, there exists a loss function of time-dependent variables that is minimized during evolution.
P2. Hierarchy of scales. Evolving systems encompass multiple dynamical variables that change on different temporal scales (with different characteristic frequencies).
P3. Frequency gaps. Dynamical variables are split among distinct levels of organization separated by sufficiently wide frequency gaps.
P4. Renormalizability. Across the entire range of organization of evolving systems, a statistical description of faster-changing (higher-frequency) variables is feasible through the slower-changing (lower-frequency) variables.
P5. Extension. Evolving systems have the capacity to recruit additional variables that can be utilized to sustain the system and the ability to exclude variables that could destabilize the system.
P6. Replication. In evolving systems, replication and elimination of the corresponding information-processing units (IPUs) can take place on every level of organization.
P7. Information flow. In evolving systems, slower-changing levels absorb information from faster-changing levels during learning and pass information down to the faster levels for prediction of the state of the environment and the system itself.
P2. Hierarchy of scales. Evolving systems encompass multiple dynamical variables that change on different temporal scales (with different characteristic frequencies).
P3. Frequency gaps. Dynamical variables are split among distinct levels of organization separated by sufficiently wide frequency gaps.
P4. Renormalizability. Across the entire range of organization of evolving systems, a statistical description of faster-changing (higher-frequency) variables is feasible through the slower-changing (lower-frequency) variables.
P5. Extension. Evolving systems have the capacity to recruit additional variables that can be utilized to sustain the system and the ability to exclude variables that could destabilize the system.
P6. Replication. In evolving systems, replication and elimination of the corresponding information-processing units (IPUs) can take place on every level of organization.
P7. Information flow. In evolving systems, slower-changing levels absorb information from faster-changing levels during learning and pass information down to the faster levels for prediction of the state of the environment and the system itself.
