A group of scientists has put forth a groundbreaking machine-based theory of life that challenges traditional biological perspectives. This theory suggests that life is not just a product of organic processes but is also heavily influenced by external, non-biological factors such as machines and technology. The proposition puts forward the idea that life is a result of an interplay between biological and
In a new machine-based theory of life Tsvi Tlusty and Albert Libchaber suggest that life is an intricate cascade of machines, from molecular level to entire biospheres. They have developed a conceptual framework and a simplified language to describe living matter as an almost infinite double cascade. This theory identifies a critical point where self-replicating machines interface with their environment, which is essential for the emergence of life. This critical point is marked by specific temporal and spatial scales of 1,000 seconds and 1 micron, corresponding to microbial life. This theory provides a mathematical foundation for understanding life and its complexity
In a new machine-based theory of life Tsvi Tlusty and Albert Libchaber suggest that life is an intricate cascade of machines, from molecular level to entire biospheres. They have developed a conceptual framework and a simplified language to describe living matter as an almost infinite double cascade. This theory identifies a critical point where self-replicating machines interface with their environment, which is essential for the emergence of life. This critical point is marked by specific temporal and spatial scales of 1,000 seconds and 1 micron, corresponding to microbial life. This theory provides a mathematical foundation for understanding life and its complexity
- Evolution is seen as a highly path-dependent process due to its historical nature, but outcomes could have varied.
- Convergence and constraints significantly limit evolutionary designs, suggesting that not all possibilities are realized.
- Fundamental constraints are inherent in the logic of living matter, influencing evolutionary outcomes.
- Examples of constraints include thermodynamics in living systems, linear molecular information, cellular composition, multicellularity, cognitive system computations, and ecosystem architecture.
- The study provides evidence for these constraints and proposes pathways for a defined theoretical framework.
- Convergence and constraints significantly limit evolutionary designs, suggesting that not all possibilities are realized.
- Fundamental constraints are inherent in the logic of living matter, influencing evolutionary outcomes.
- Examples of constraints include thermodynamics in living systems, linear molecular information, cellular composition, multicellularity, cognitive system computations, and ecosystem architecture.
- The study provides evidence for these constraints and proposes pathways for a defined theoretical framework.
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.
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.
