{"title":"从生物物理、发育和系统生物学角度了解癌症。","authors":"Thomas W. Grunt","doi":"10.1016/j.biosystems.2024.105376","DOIUrl":null,"url":null,"abstract":"<div><div>Biophysical, developmental and systems-biology considerations enable deeper understanding why cancer is life threatening despite intensive research. Here we use two metaphors. Both conceive the cell genome and the encoded molecular system as an interacting gene regulatory network (GRN). According to Waddington's epigenetic (quasi-potential)-landscape, an instrumental tool in ontogenetics, individual interaction patterns ( = expression profiles) within this GRN represent possible cell states with different stabilities. Network interactions with low stability are represented on peaks. Unstable interactions strive towards regions with higher stability located at lower altitude in valleys termed attractors that correspond to stable cell phenotypes. Cancer cells are seen as GRNs adopting aberrant semi-stable attractor states (cancer attractor). In the second metaphor, Wright's phylogenetic fitness (adaptive) landscape, each genome ( = GRN) is assigned a specific position in the landscape according to its structure and reproductive fitness in the specific environment. High elevation signifies high fitness and low altitude low fitness. Selection ensures that mutant GRNs evolve and move from valleys to peaks. The genetic flexibility is highlighted in the fitness landscape, while non-genetic flexibility is captured in the quasi-potential landscape. These models resolve several inconsistencies that have puzzled cancer researchers, such as the fact that phenotypes generated by non-genetic mechanisms coexist in a single tumor with phenotypes caused by mutations and they mitigate conflicts between cancer theories that claim cancer is caused by mutation (somatic mutation theory) or by disruption of tissue architecture (tissue organization field theory). Nevertheless, spontaneous mutations play key roles in cancer. Remarkable, fundamental natural laws such as the second law of thermodynamics and quantum mechanics state that mutations are inevitable events. The good side of this is that without mutational variability in DNA, evolutionary development would not have occurred, but its bad side is that the occurrence of cancer is essentially inevitable. In summary, both landscapes together fully describe the behavior of cancer under normal and stressful conditions such as chemotherapy. Thus, the landscapes-attractor model fully describes cancer cell behavior and offers new perspectives for future treatment.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105376"},"PeriodicalIF":2.0000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding cancer from a biophysical, developmental and systems biology perspective using the landscapes-attractor model\",\"authors\":\"Thomas W. Grunt\",\"doi\":\"10.1016/j.biosystems.2024.105376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Biophysical, developmental and systems-biology considerations enable deeper understanding why cancer is life threatening despite intensive research. Here we use two metaphors. Both conceive the cell genome and the encoded molecular system as an interacting gene regulatory network (GRN). According to Waddington's epigenetic (quasi-potential)-landscape, an instrumental tool in ontogenetics, individual interaction patterns ( = expression profiles) within this GRN represent possible cell states with different stabilities. Network interactions with low stability are represented on peaks. Unstable interactions strive towards regions with higher stability located at lower altitude in valleys termed attractors that correspond to stable cell phenotypes. Cancer cells are seen as GRNs adopting aberrant semi-stable attractor states (cancer attractor). In the second metaphor, Wright's phylogenetic fitness (adaptive) landscape, each genome ( = GRN) is assigned a specific position in the landscape according to its structure and reproductive fitness in the specific environment. High elevation signifies high fitness and low altitude low fitness. Selection ensures that mutant GRNs evolve and move from valleys to peaks. The genetic flexibility is highlighted in the fitness landscape, while non-genetic flexibility is captured in the quasi-potential landscape. These models resolve several inconsistencies that have puzzled cancer researchers, such as the fact that phenotypes generated by non-genetic mechanisms coexist in a single tumor with phenotypes caused by mutations and they mitigate conflicts between cancer theories that claim cancer is caused by mutation (somatic mutation theory) or by disruption of tissue architecture (tissue organization field theory). Nevertheless, spontaneous mutations play key roles in cancer. Remarkable, fundamental natural laws such as the second law of thermodynamics and quantum mechanics state that mutations are inevitable events. The good side of this is that without mutational variability in DNA, evolutionary development would not have occurred, but its bad side is that the occurrence of cancer is essentially inevitable. In summary, both landscapes together fully describe the behavior of cancer under normal and stressful conditions such as chemotherapy. Thus, the landscapes-attractor model fully describes cancer cell behavior and offers new perspectives for future treatment.</div></div>\",\"PeriodicalId\":50730,\"journal\":{\"name\":\"Biosystems\",\"volume\":\"247 \",\"pages\":\"Article 105376\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0303264724002612\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0303264724002612","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
从生物物理、发展和系统生物学的角度来考虑问题,可以更深入地理解为什么尽管进行了深入的研究,癌症仍然威胁着人们的生命。在此,我们使用两个比喻。两者都将细胞基因组和编码分子系统视为相互作用的基因调控网络(GRN)。根据瓦丁顿(Waddington)的表观遗传学(准潜在)景观(本体遗传学的一种工具),这个基因调控网络中的单个相互作用模式(=表达档案)代表了具有不同稳定性的可能细胞状态。低稳定性的网络互动表现为峰值。不稳定的交互作用则向位于低谷的较高稳定性区域发展,这些区域被称为吸引子,对应于稳定的细胞表型。癌细胞被视为采用异常半稳定吸引子状态的 GRN(癌症吸引子)。在第二个比喻中,即赖特的系统发育适应性(适应性)景观中,每个基因组(=GRN)根据其结构和在特定环境中的繁殖适应性,被分配到景观中的特定位置。高海拔代表高适应性,低海拔代表低适应性。选择确保突变的 GRN 从低谷进化到高峰。遗传灵活性在适应性景观中得到强调,而非遗传灵活性则在准潜力景观中得到体现。这些模型解决了令癌症研究人员困惑的几个不一致之处,例如非遗传机制产生的表型与突变导致的表型共存于一个肿瘤中,它们还缓解了声称癌症是由突变(体细胞突变理论)或组织结构破坏(组织-组织-场理论)导致的癌症理论之间的冲突。然而,自发突变在癌症中起着关键作用。热力学第二定律和量子力学等显著的基本自然规律表明,突变是不可避免的事件。好的一面是,如果没有 DNA 变异,进化发展就不会发生;坏的一面是,癌症的发生基本上是不可避免的。总之,这两种景观共同充分描述了癌症在正常和化疗等压力条件下的行为。因此,"地貌-吸引子-模型 "完全描述了癌细胞的行为,并为未来的治疗提供了新的视角。
Understanding cancer from a biophysical, developmental and systems biology perspective using the landscapes-attractor model
Biophysical, developmental and systems-biology considerations enable deeper understanding why cancer is life threatening despite intensive research. Here we use two metaphors. Both conceive the cell genome and the encoded molecular system as an interacting gene regulatory network (GRN). According to Waddington's epigenetic (quasi-potential)-landscape, an instrumental tool in ontogenetics, individual interaction patterns ( = expression profiles) within this GRN represent possible cell states with different stabilities. Network interactions with low stability are represented on peaks. Unstable interactions strive towards regions with higher stability located at lower altitude in valleys termed attractors that correspond to stable cell phenotypes. Cancer cells are seen as GRNs adopting aberrant semi-stable attractor states (cancer attractor). In the second metaphor, Wright's phylogenetic fitness (adaptive) landscape, each genome ( = GRN) is assigned a specific position in the landscape according to its structure and reproductive fitness in the specific environment. High elevation signifies high fitness and low altitude low fitness. Selection ensures that mutant GRNs evolve and move from valleys to peaks. The genetic flexibility is highlighted in the fitness landscape, while non-genetic flexibility is captured in the quasi-potential landscape. These models resolve several inconsistencies that have puzzled cancer researchers, such as the fact that phenotypes generated by non-genetic mechanisms coexist in a single tumor with phenotypes caused by mutations and they mitigate conflicts between cancer theories that claim cancer is caused by mutation (somatic mutation theory) or by disruption of tissue architecture (tissue organization field theory). Nevertheless, spontaneous mutations play key roles in cancer. Remarkable, fundamental natural laws such as the second law of thermodynamics and quantum mechanics state that mutations are inevitable events. The good side of this is that without mutational variability in DNA, evolutionary development would not have occurred, but its bad side is that the occurrence of cancer is essentially inevitable. In summary, both landscapes together fully describe the behavior of cancer under normal and stressful conditions such as chemotherapy. Thus, the landscapes-attractor model fully describes cancer cell behavior and offers new perspectives for future treatment.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.