Biosystems最新文献

The paper addresses a criticism raised by Kull (2020) against Code Biology. Kull's critique targets key ontological and epistemological assumptions within Code Biology and, if valid, could effectively subordinate it to Peircean Biosemiotics. After examining the core of Kull's argument, the paper counters a significant aspect of his claim: that cognition necessarily involves interpretation-driven decision-making, wherein an agent is always faced with at least two alternatives when acting upon the world. Drawing from radical cognitive science and general Code Biology, the paper argues that basic cognitive processes are devoid of mental content and, therefore, should not be described in mentalist terms such as 'decision-making' or 'interpretation.' In this connection, insights from phenomenological accounts of skill acquisition demonstrate that even sophisticated forms of human cognition can occur without explicit reasoning about alternatives. In such cases, the environment itself elicits skillful responses. Building on this, the paper introduces the concept of praxeological codes-codified relations characteristic of human socio-practical activity-which can be used to explain what Maturana identifies as the 'recursivity' of practical doings. In this connection, the paper provides an account of core aspects of human socio-practical behavior.

Major Depressive Disorder, Bipolar Disorder, and Schizophrenia, share significant genetic, epigenetic, and phenotypic overlap, manifesting as dimensional psychopathology and convergent neuroimaging findings. These shared features have led to various models exploring common underlying pathophysiological mechanisms, including excitatory-inhibitory imbalance, the triple network model, network analysis, and social disconnection. While these models offer valuable insights, a unifying framework remains elusive. Schismogenesis, a transdisciplinary construct, is proposed to reconcile divergent perspectives on mental health conditions. Characterized by positive feedback loops leading to functional dissociation due to insufficient inhibitory control, complementary schismogenesis results in rigid hyperactivation and hypoactivation within neural, cognitive, and social networks, compromising system flexibility. This pathological process underlies the core features of Major Depressive Disorder, Bipolar Disorder, and Schizophrenia, depending on its location within networks. The schismogenesis hypothesis suggests that when individuals are overwhelmed by excessive stress or tension, they may experience a breakdown or disconnection to prevent irreversible damage, reflecting evolutionary adaptations. Importantly, the potential reversibility of schismogenesis, particularly through interventions that facilitate system reintegration, suggests promising therapeutic avenues for further exploration.

Pleiotropy refers to a gene's ability to influence multiple phenotypes or traits. In the context of human genetic diseases, pleiotropy manifests as different pathological effects resulting from mutations in the same gene. This phenomenon plays a crucial role in understanding gene-gene interactions in system-level biological diseases. Previous studies have largely focused on pleiotropy within undirected molecular correlation networks, leaving a gap in examining pleiotropy induced by directed signaling networks, which can better explain dynamic gene-gene interactions. In this study, we utilized a synchronous Boolean network model to explore pleiotropic dynamics induced by various mutations in large-scale networks. We introduced an in-silico Pleiotropic Score (sPS) to quantify the impact of gene mutations and validated the model against observational pleiotropy data from the Human Phenotype Ontology (HPO). Our results indicate a significant correlation between sPS and network structural characteristics, including degree centrality and feedback loop involvement. The highest correlation was observed between closeness centrality and sPS (0.6), suggesting that genes more central in the network exhibit higher pleiotropic potential. Furthermore, genes involved in feedback loops demonstrated higher sPS values (p < 0.0001), supporting the role of feedback loops in amplifying pleiotropic behavior. Our model provides a novel approach for quantifying pleiotropy through directed network dynamics, complementing traditional observational methods.