Search Results (6)

This work explores the application of a Category Theory model, advocating a paradigm for comprehending human experience and the communication process of a complex system from the perspective of a living Anticipatory System. Following the principles created by Robert Rosen for the anticipatory system and associated models—models that respect the principles of impredicativity, anticipation, and closure to efficient cause (CLEF)—we propose the Performance–Resilience–Sustainability (PRS) model. This new model introduces a new way to explain how anticipatory systems can elucidate the portions of variability observed in practice and research. Anticipatory system theory suggests that models such as PRS have significant potential to complement and explain dynamic phenomena observed in communication and experience development research, as well as in practical applications, underscoring the transformative potential for both fields. This class of models for complex systems may introduce a new dimension of emergent causality and its impact on current behavior, which was not previously considered. Full article

Biologists, philosophers, and mathematicians building upon Robert Rosen’s non-algorithmic theories of life using Relational Biology and Category Theory have continued to develop his theory and modeling approaches. There has been general agreement that the impredicative, self-referential, and complex nature of living systems negates an algorithmic approach. Rosen’s main goal was to answer, “What is Life?”. Many believe he provided the best but minimum answer using a cellular, metabolism–repair or (M, R)-system as a category-theoretic model. It has been challenging, however, to incorporate his theory to develop a fully non-algorithmic methodology that retains the essence of his thinking while creating more operational models of living systems that can be used to explore other facets of life and answer different questions. Living systems do more than the minimum in the real world beyond the confines of definition alone. For example, ecologists ask how living systems inherently mitigate existential risk from climate change and biodiversity loss through their complex self-organization. Loop Analysis, a signed graph technique, is discussed as a hybrid algorithmic/non-algorithmic methodology in Relational Biology. This methodology can be used at the ecosystem level with standard non-algorithmic field data as per McAllister’s description of the algorithmic incompressibility of empirical data of this type. An example is described showing how the North Atlantic Carbon Pump, an important planetary life support system, is situated in the plankton community and functions as a mutualistic ecosystem chimera. It captures carbon from the atmosphere as an extended (M, R)-system and processes it until it is sequestered in the marine sediments. This is an important process to alleviate climate change in magnitude equal to or larger than the sequestration of carbon on land with forests. It is suggested that the ecosystem level should replace the cellular and organismic levels as the main system unit in biology and evolution since all life exists and evolves with full functional potential in ecosystem networks and not laboratory test tubes. The plankton ecosystem is the largest after the total biosphere and consists of evolutionary links and relationships that have existed for eons of time. If there was ever a genuine robust, highly self-organized ecosystem, it would be planktonic. Severing the links in these thermodynamically open networks by focusing on lower levels of the biological hierarchy loses the critical organization of how life exists on this planet. There is no theory to regain this crucial ‘omitted’ ecological relational causality at the cell or organismal levels. At the end of the paper, some future directions are outlined. Full article

This paper examines the use of algorithms and non-algorithmic models in mathematics and science, especially in biology, during the past century by summarizing the gradual development of a conceptual rationale for non-algorithmic models in biology. First, beginning a century ago, mathematicians found it impossible to constrain mathematics in an algorithmic straitjacket via öö’s Incompleteness Theorems, so how would it be possible in biology? By the 1930s, biology was resolutely imitating classical physics, with biologists enforcing a reductionist agenda to expunge function, purpose, teleology, and vitalism from biology. Interestingly, physicists and mathematicians often understood better than biologists that mathematical representations of living systems required different approaches than those of dead matter. Nicolas Rashevsky, the Father of Mathematical Biology, and Robert Rosen, his student, pointed out that the complex systems of life cannot be reduced to machines or mechanisms as per the Newtonian paradigm. Robert Rosen concluded that living systems are not amenable to algorithmic models that are primarily syntactical. Life requires semantics for its description. Rashevsky and Rosen pioneered Relational Biology, initially using Graph Theory to model living systems. Later, Rosen created a metabolic–repair model (M, R)-system using Category Theory to encode the basic entailments of life itself. Although reductionism still dominates in current biology, several subsequent authors have built upon the Rashevsky–Rosen intellectual foundation and have explained, extended, and explored its ramifications. Algorithmic formulations have become increasingly inadequate for investigating and modeling living systems. Biology is shifting from a science of simple systems to complex ones. This transition will only be successful once mathematics fully depicts what it means to be alive. This paper is a call to mathematicians from biologists asking for help in doing this. Full article

When I am at conferences, talking about the scientific work of my father (theoretical biologist Robert Rosen, 1934–1998), I am often asked which aspects of his work I think are most important. My answer is Anticipatory Systems Theory. It’s about the entailment and characterization of both life and mind. It explains the fundamental nature of all life, showing how the human mind is an evolutionary concentration of the same peculiar behavior patterns manifested by all living organisms, regardless of species. How can we hope to fully understand ourselves or anything else in the biosphere of Earth without an accurate scientific comprehension of the entailment patterns underlying and generating all of it? The physics of orbital mechanics or atomic particles is insufficient for this. Therefore, I spend a lot of my time working to make the meaning of my father’s scientific discoveries accessible to as many human minds as possible. I think humanity is going to need this work in the future, and already needs it now. This paper will examine the basic premises of Anticipatory Systems Theory and describe, using examples familiar to all of us from daily life, how we can recognize Anticipation at work in ourselves and in local ecosystems all over the planet. I will conclude with some important ramifications of this theory, including how Anticipation necessarily plays into evolutionary processes. I will also point out the vulnerabilities of Anticipatory Systems (i.e., living organisms) to rapid change in environment, potentially leading to extinction cascades. Full article

The discovery and characterization of transposable element (TE) families are crucial tasks in the process of genome annotation. Careful curation of TE libraries for each organism is necessary as each has been exposed to a unique and often complex set of TE families. De novo methods have been developed; however, a fully automated and accurate approach to the development of complete libraries remains elusive. In this review, we cover established methods and recent developments in de novo TE analysis. We also present various methodologies used to assess these tools and discuss opportunities for further advancement of the field. Full article

Chronic low back pain (cLBP) is a common disorder with unsatisfactory treatment options. Acupuncture has emerged as a promising method for treating cLBP. However, the mechanism underlying acupuncture remains unclear. In this study, we investigated the modulation effects of acupuncture on resting state functional connectivity (rsFC) of the periaqueductal gray (PAG) and ventral tegmental area (VTA) in patients with cLBP. Seventy-nine cLBP patients were recruited and assigned to four weeks of real or sham acupuncture. Resting state functional magnetic resonance imaging data were collected before the first and after the last treatment. Fifty patients completed the study. We found remission of pain bothersomeness in all treatment groups after four weeks, with greater pain relief after real acupuncture compared to sham acupuncture. We also found that real acupuncture can increase VTA/PAG rsFC with the amygdala, and the increased rsFC was associated with decreased pain bothersomeness scores. Baseline PAG-amygdala rsFC could predict four-week treatment response. Our results suggest that acupuncture may simultaneously modulate the rsFC of key regions in the descending pain modulation (PAG) and reward systems (VTA), and the amygdala may be a key node linking the two systems to produce antinociceptive effects. Our findings highlight the potential of acupuncture for chronic low back pain management. Full article