Jiarui Xiong, Liang Wang, Jialun Lin, Lei Ni, Rongrong Zhang, Shuai Yang, Yajia Huang, Jun Chu, Fan Jin
Bacterial second messengers are crucial for transmitting environmental information to cellular responses. However, quantifying their information transmission capacity remains challenging. Here, we engineer an isolated cAMP signaling channel in Pseudomonas aeruginosa using targeted gene knockouts, optogenetics, and a fluorescent cAMP probe. This design allows precise optical control and real-time monitoring of cAMP dynamics. By integrating experimental data with information theory, we reveal an optimal frequency for light-mediated cAMP signaling that maximizes information transmission, reaching about 40 bits/h. This rate correlates strongly with cAMP degradation kinetics and employs a two-state encoding scheme. Our findings suggest a mechanism for fine-tuned regulation of multiple genes through temporal encoding of second messenger signals, providing new insights into bacterial adaptation strategies. This approach offers a framework for quantifying information processing in cellular signaling systems.
{"title":"Optimal Frequency in Second Messenger Signaling Quantifying cAMP Information Transmission in Bacteria","authors":"Jiarui Xiong, Liang Wang, Jialun Lin, Lei Ni, Rongrong Zhang, Shuai Yang, Yajia Huang, Jun Chu, Fan Jin","doi":"arxiv-2408.04988","DOIUrl":"https://doi.org/arxiv-2408.04988","url":null,"abstract":"Bacterial second messengers are crucial for transmitting environmental\u0000information to cellular responses. However, quantifying their information\u0000transmission capacity remains challenging. Here, we engineer an isolated cAMP\u0000signaling channel in Pseudomonas aeruginosa using targeted gene knockouts,\u0000optogenetics, and a fluorescent cAMP probe. This design allows precise optical\u0000control and real-time monitoring of cAMP dynamics. By integrating experimental\u0000data with information theory, we reveal an optimal frequency for light-mediated\u0000cAMP signaling that maximizes information transmission, reaching about 40\u0000bits/h. This rate correlates strongly with cAMP degradation kinetics and\u0000employs a two-state encoding scheme. Our findings suggest a mechanism for\u0000fine-tuned regulation of multiple genes through temporal encoding of second\u0000messenger signals, providing new insights into bacterial adaptation strategies.\u0000This approach offers a framework for quantifying information processing in\u0000cellular signaling systems.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marcos Wappner, Koichiro Uriu, Andrew C. Oates, Luis G. Morelli
Notch signaling is a ubiquitous and versatile intercellular signaling system that drives collective behaviors and pattern formation in biological tissues. During embryonic development, Notch is involved in generation of collective biochemical oscillations that form the vertebrate body segments, and its failure results in embryonic defects. Notch ligands of the Delta family are key components of this collective rhythm, but it is unclear how different Delta ligands with distinct properties contribute to relaying information among cells. Motivated by the zebrafish segmentation clock, in this work we propose a theory describing interactions between biochemical oscillators, where Notch receptor is bound by both oscillatory and nonoscillatory Delta ligands. Based on previous in vitro binding studies, we first consider Notch activation by Delta dimers. This hypothesis is consistent with experimental observations in conditions of perturbed Notch signaling. Then we test an alternative hypothesis where Delta monomers directly bind and activate Notch, and show that this second model can also describe the experimental observations. We show that these two hypotheses assign different roles for a non-oscillatory ligand, as a binding partner or as a baseline signal. Finally, we discuss experiments to distinguish between the two scenarios. Broadly, this work highlights how a multiplicity of ligands may be harnessed by a signaling system to generate versatile responses.
{"title":"Multiple Notch ligands in the synchronization of the segmentation clock","authors":"Marcos Wappner, Koichiro Uriu, Andrew C. Oates, Luis G. Morelli","doi":"arxiv-2408.04027","DOIUrl":"https://doi.org/arxiv-2408.04027","url":null,"abstract":"Notch signaling is a ubiquitous and versatile intercellular signaling system\u0000that drives collective behaviors and pattern formation in biological tissues.\u0000During embryonic development, Notch is involved in generation of collective\u0000biochemical oscillations that form the vertebrate body segments, and its\u0000failure results in embryonic defects. Notch ligands of the Delta family are key\u0000components of this collective rhythm, but it is unclear how different Delta\u0000ligands with distinct properties contribute to relaying information among\u0000cells. Motivated by the zebrafish segmentation clock, in this work we propose a\u0000theory describing interactions between biochemical oscillators, where Notch\u0000receptor is bound by both oscillatory and nonoscillatory Delta ligands. Based\u0000on previous in vitro binding studies, we first consider Notch activation by\u0000Delta dimers. This hypothesis is consistent with experimental observations in\u0000conditions of perturbed Notch signaling. Then we test an alternative hypothesis\u0000where Delta monomers directly bind and activate Notch, and show that this\u0000second model can also describe the experimental observations. We show that\u0000these two hypotheses assign different roles for a non-oscillatory ligand, as a\u0000binding partner or as a baseline signal. Finally, we discuss experiments to\u0000distinguish between the two scenarios. Broadly, this work highlights how a\u0000multiplicity of ligands may be harnessed by a signaling system to generate\u0000versatile responses.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141942697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shiling Liang, Paolo De Los Rios, Daniel Maria Busiello
Living systems are usually maintained out of equilibrium and exhibit complex dynamical behaviors. The external energy supply often comes from chemical fluxes that can keep some species concentrations constant. Furthermore, the properties of the underlying chemical reaction networks (CRNs) are also instrumental in establishing robust biological functioning. Hence, capturing the emergent complexity of living systems and the role of their non-equilibrium nature is fundamental to uncover constraints and properties of the CRNs underpinning their functions. In particular, while kinetics plays a key role in shaping detailed dynamical phenomena, the range of operations of any CRN must be fundamentally constrained by thermodynamics, as they necessarily operate with a given energy budget. Here, we derive universal thermodynamic upper and lower bounds for the accessible space of species concentrations in a generic CRN. The resulting region determines the "thermodynamic space" of the CRN, a concept we introduce in this work. Moreover, we obtain similar bounds also for the affinities, shedding light on how global thermodynamic properties can limit local non-equilibrium quantities. We illustrate our results in two paradigmatic examples, the Schl"ogl model for bistability and a minimal self-assembly process, demonstrating how the onset of complex behaviors is intimately tangled with the presence of non-equilibrium driving. In summary, our work unveils the exact form of the accessible space in which a CRN must work as a function of its energy budget, shedding light on the non-equilibrium origin of a variety of phenomena, from amplification to pattern formation. Ultimately, by providing a general tool for analyzing CRNs, the presented framework constitutes a stepping stone to deepen our ability to predict complex out-of-equilibrium behaviors and design artificial chemical reaction systems.
{"title":"Thermodynamic Space of Chemical Reaction Networks","authors":"Shiling Liang, Paolo De Los Rios, Daniel Maria Busiello","doi":"arxiv-2407.11498","DOIUrl":"https://doi.org/arxiv-2407.11498","url":null,"abstract":"Living systems are usually maintained out of equilibrium and exhibit complex\u0000dynamical behaviors. The external energy supply often comes from chemical\u0000fluxes that can keep some species concentrations constant. Furthermore, the\u0000properties of the underlying chemical reaction networks (CRNs) are also\u0000instrumental in establishing robust biological functioning. Hence, capturing\u0000the emergent complexity of living systems and the role of their non-equilibrium\u0000nature is fundamental to uncover constraints and properties of the CRNs\u0000underpinning their functions. In particular, while kinetics plays a key role in\u0000shaping detailed dynamical phenomena, the range of operations of any CRN must\u0000be fundamentally constrained by thermodynamics, as they necessarily operate\u0000with a given energy budget. Here, we derive universal thermodynamic upper and\u0000lower bounds for the accessible space of species concentrations in a generic\u0000CRN. The resulting region determines the \"thermodynamic space\" of the CRN, a\u0000concept we introduce in this work. Moreover, we obtain similar bounds also for\u0000the affinities, shedding light on how global thermodynamic properties can limit\u0000local non-equilibrium quantities. We illustrate our results in two paradigmatic\u0000examples, the Schl\"ogl model for bistability and a minimal self-assembly\u0000process, demonstrating how the onset of complex behaviors is intimately tangled\u0000with the presence of non-equilibrium driving. In summary, our work unveils the\u0000exact form of the accessible space in which a CRN must work as a function of\u0000its energy budget, shedding light on the non-equilibrium origin of a variety of\u0000phenomena, from amplification to pattern formation. Ultimately, by providing a\u0000general tool for analyzing CRNs, the presented framework constitutes a stepping\u0000stone to deepen our ability to predict complex out-of-equilibrium behaviors and\u0000design artificial chemical reaction systems.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Markov chains on the non-negative quadrant of dimension $d$ are often used to model the stochastic dynamics of the number of $d$ entities, such as $d$ chemical species in stochastic reaction networks. The infinite state space poses technical challenges, and the boundary of the quadrant can have a dramatic effect on the long term behavior of these Markov chains. For instance, the boundary can slow down the convergence speed of an ergodic Markov chain towards its stationary distribution due to the extinction or the lack of an entity. In this paper, we quantify this slow-down for a class of stochastic reaction networks and for more general Markov chains on the non-negative quadrant. We establish general criteria for such a Markov chain to exhibit a power-law lower bound for its mixing time. The lower bound is of order $|x|^theta$ for all initial state $x$ on a boundary face of the quadrant, where $theta$ is characterized by the local behavior of the Markov chain near the boundary of the quadrant. A better understanding of how these lower bounds arise leads to insights into how the structure of chemical reaction networks contributes to slow-mixing.
{"title":"Boundary-induced slow mixing for Markov chains and its application to stochastic reaction networks","authors":"Wai-TongLouis, Fan, Jinsu Kim, Chaojie Yuan","doi":"arxiv-2407.12166","DOIUrl":"https://doi.org/arxiv-2407.12166","url":null,"abstract":"Markov chains on the non-negative quadrant of dimension $d$ are often used to\u0000model the stochastic dynamics of the number of $d$ entities, such as $d$\u0000chemical species in stochastic reaction networks. The infinite state space\u0000poses technical challenges, and the boundary of the quadrant can have a\u0000dramatic effect on the long term behavior of these Markov chains. For instance,\u0000the boundary can slow down the convergence speed of an ergodic Markov chain\u0000towards its stationary distribution due to the extinction or the lack of an\u0000entity. In this paper, we quantify this slow-down for a class of stochastic\u0000reaction networks and for more general Markov chains on the non-negative\u0000quadrant. We establish general criteria for such a Markov chain to exhibit a\u0000power-law lower bound for its mixing time. The lower bound is of order\u0000$|x|^theta$ for all initial state $x$ on a boundary face of the quadrant,\u0000where $theta$ is characterized by the local behavior of the Markov chain near\u0000the boundary of the quadrant. A better understanding of how these lower bounds\u0000arise leads to insights into how the structure of chemical reaction networks\u0000contributes to slow-mixing.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nadine Ben Boina, Brigitte Mossé, Anaïs Baudot, Élisabeth Remy
Motivation: In systems biology, modelling strategies aim to decode how molecular components interact to generate dynamical behaviour. Boolean modelling is more and more used, but the description of the dynamics from two-levels components may be too limited to capture certain dynamical properties. %However, in Boolean models, the description of the dynamics may be too limited to capture certain dynamical properties. Multivalued logical models can overcome this limitation by allowing more than two levels for each component. However, multivaluing a Boolean model is challenging. Results: We present MRBM, a method for efficiently identifying the components of a Boolean model to be multivalued in order to capture specific fixed-point reachabilities in the asynchronous dynamics. To this goal, we defined a new updating scheme locating reachability properties in the most permissive dynamics. MRBM is supported by mathematical demonstrations and illustrated on a toy model and on two models of stem cell differentiation.
{"title":"Refining Boolean models with the partial most permissive scheme","authors":"Nadine Ben Boina, Brigitte Mossé, Anaïs Baudot, Élisabeth Remy","doi":"arxiv-2407.09954","DOIUrl":"https://doi.org/arxiv-2407.09954","url":null,"abstract":"Motivation: In systems biology, modelling strategies aim to decode how molecular\u0000components interact to generate dynamical behaviour. Boolean modelling is more\u0000and more used, but the description of the dynamics from two-levels components\u0000may be too limited to capture certain dynamical properties. %However, in\u0000Boolean models, the description of the dynamics may be too limited to capture\u0000certain dynamical properties. Multivalued logical models can overcome this\u0000limitation by allowing more than two levels for each component. However,\u0000multivaluing a Boolean model is challenging. Results: We present MRBM, a method for efficiently identifying the components\u0000of a Boolean model to be multivalued in order to capture specific fixed-point\u0000reachabilities in the asynchronous dynamics. To this goal, we defined a new\u0000updating scheme locating reachability properties in the most permissive\u0000dynamics. MRBM is supported by mathematical demonstrations and illustrated on a\u0000toy model and on two models of stem cell differentiation.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chun-Ka Wong, Ali Choo, Eugene C. C. Cheng, Wing-Chun San, Kelvin Chak-Kong Cheng, Yee-Man Lau, Minqing Lin, Fei Li, Wei-Hao Liang, Song-Yan Liao, Kwong-Man Ng, Ivan Fan-Ngai Hung, Hung-Fat Tse, Jason Wing-Hon Wong
Interrogation of biological pathways is an integral part of omics data analysis. Large language models (LLMs) enable the generation of custom pathways and gene sets tailored to specific scientific questions. These targeted sets are significantly smaller than traditional pathway enrichment analysis libraries, reducing multiple hypothesis testing and potentially enhancing statistical power. Lomics (Large Language Models for Omics Studies) v1.0 is a python-based bioinformatics toolkit that streamlines the generation of pathways and gene sets for transcriptomic analysis. It operates in three steps: 1) deriving relevant pathways based on the researcher's scientific question, 2) generating valid gene sets for each pathway, and 3) outputting the results as .GMX files. Lomics also provides explanations for pathway selections. Consistency and accuracy are ensured through iterative processes, JSON format validation, and HUGO Gene Nomenclature Committee (HGNC) gene symbol verification. Lomics serves as a foundation for integrating LLMs into omics research, potentially improving the specificity and efficiency of pathway analysis.
对生物通路的研究是 omics 数据分析不可或缺的一部分。大型语言模型（LLM）可以生成针对特定科学问题的定制通路和基因集。这些目标集比传统的通路富集分析库小很多，减少了多重假设检验，并可能提高统计能力。Lomics (Large Language Models for Omics Studies) v1.0 是一个基于 Python 的生物信息学工具包，可简化转录组分析中通路和基因集的生成。它分为三个步骤1）根据研究人员的科学问题生成相关通路；2）为每个通路生成有效的基因组；3）将结果输出为 GMX 文件。通过迭代过程、JSON 格式验证和 HUGO 基因命名委员会 (HGNC) 基因符号验证，确保了一致性和准确性。Lomics 是将 LLMs 整合到 omics 研究中的基础，有可能提高通路分析的特异性和效率。
{"title":"Lomics: Generation of Pathways and Gene Sets using Large Language Models for Transcriptomic Analysis","authors":"Chun-Ka Wong, Ali Choo, Eugene C. C. Cheng, Wing-Chun San, Kelvin Chak-Kong Cheng, Yee-Man Lau, Minqing Lin, Fei Li, Wei-Hao Liang, Song-Yan Liao, Kwong-Man Ng, Ivan Fan-Ngai Hung, Hung-Fat Tse, Jason Wing-Hon Wong","doi":"arxiv-2407.09089","DOIUrl":"https://doi.org/arxiv-2407.09089","url":null,"abstract":"Interrogation of biological pathways is an integral part of omics data\u0000analysis. Large language models (LLMs) enable the generation of custom pathways\u0000and gene sets tailored to specific scientific questions. These targeted sets\u0000are significantly smaller than traditional pathway enrichment analysis\u0000libraries, reducing multiple hypothesis testing and potentially enhancing\u0000statistical power. Lomics (Large Language Models for Omics Studies) v1.0 is a\u0000python-based bioinformatics toolkit that streamlines the generation of pathways\u0000and gene sets for transcriptomic analysis. It operates in three steps: 1)\u0000deriving relevant pathways based on the researcher's scientific question, 2)\u0000generating valid gene sets for each pathway, and 3) outputting the results as\u0000.GMX files. Lomics also provides explanations for pathway selections.\u0000Consistency and accuracy are ensured through iterative processes, JSON format\u0000validation, and HUGO Gene Nomenclature Committee (HGNC) gene symbol\u0000verification. Lomics serves as a foundation for integrating LLMs into omics\u0000research, potentially improving the specificity and efficiency of pathway\u0000analysis.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesco Avanzini, Timur Aslyamov, Étienne Fodor, Massimiliano Esposito
We develop a framework describing the dynamics and thermodynamics of open non-ideal reaction-diffusion systems, which embodies Flory-Huggins theories of mixtures and chemical reaction network theories. Our theory elucidates the mechanisms underpinning the emergence of self-organized dissipative structures in these systems. It evaluates the dissipation needed to sustain and control them, discriminating the contributions from each reaction and diffusion process with spatial resolution. It also reveals the role of the reaction network in powering and shaping these structures. We identify particular classes of networks in which diffusion processes always equilibrate within the structures, while dissipation occurs solely due to chemical reactions. The spatial configurations resulting from these processes can be derived by minimizing a kinetic potential, contrasting with the minimization of the thermodynamic free energy in passive systems. This framework opens the way to investigating the energetic cost of phenomena such as liquid-liquid phase separation, coacervation, and the formation of biomolecular condensates.
{"title":"Nonequilibrium Thermodynamics of Non-Ideal Reaction-Diffusion Systems: Implications for Active Self-Organization","authors":"Francesco Avanzini, Timur Aslyamov, Étienne Fodor, Massimiliano Esposito","doi":"arxiv-2407.09128","DOIUrl":"https://doi.org/arxiv-2407.09128","url":null,"abstract":"We develop a framework describing the dynamics and thermodynamics of open\u0000non-ideal reaction-diffusion systems, which embodies Flory-Huggins theories of\u0000mixtures and chemical reaction network theories. Our theory elucidates the\u0000mechanisms underpinning the emergence of self-organized dissipative structures\u0000in these systems. It evaluates the dissipation needed to sustain and control\u0000them, discriminating the contributions from each reaction and diffusion process\u0000with spatial resolution. It also reveals the role of the reaction network in\u0000powering and shaping these structures. We identify particular classes of\u0000networks in which diffusion processes always equilibrate within the structures,\u0000while dissipation occurs solely due to chemical reactions. The spatial\u0000configurations resulting from these processes can be derived by minimizing a\u0000kinetic potential, contrasting with the minimization of the thermodynamic free\u0000energy in passive systems. This framework opens the way to investigating the\u0000energetic cost of phenomena such as liquid-liquid phase separation,\u0000coacervation, and the formation of biomolecular condensates.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141722239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In pharmaceutical research, the strategy of drug repurposing accelerates the development of new therapies while reducing R&D costs. Network pharmacology lays the theoretical groundwork for identifying new drug indications, and deep graph models have become essential for their precision in mapping complex biological networks. Our study introduces an advanced graph model that utilizes graph convolutional networks and tensor decomposition to effectively predict signed chemical-gene interactions. This model demonstrates superior predictive performance, especially in handling the polar relations in biological networks. Our research opens new avenues for drug discovery and repurposing, especially in understanding the mechanism of actions of drugs.
{"title":"A deep graph model for the signed interaction prediction in biological network","authors":"Shuyi Jin, Mengji Zhang, Meijie Wang, Lun Yu","doi":"arxiv-2407.07357","DOIUrl":"https://doi.org/arxiv-2407.07357","url":null,"abstract":"In pharmaceutical research, the strategy of drug repurposing accelerates the\u0000development of new therapies while reducing R&D costs. Network pharmacology\u0000lays the theoretical groundwork for identifying new drug indications, and deep\u0000graph models have become essential for their precision in mapping complex\u0000biological networks. Our study introduces an advanced graph model that utilizes\u0000graph convolutional networks and tensor decomposition to effectively predict\u0000signed chemical-gene interactions. This model demonstrates superior predictive\u0000performance, especially in handling the polar relations in biological networks.\u0000Our research opens new avenues for drug discovery and repurposing, especially\u0000in understanding the mechanism of actions of drugs.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"376 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Exchange of molecules allows cells to exchange information. How robust is the information to changes in cell parameters? We use a mapping between the stochastic dynamics of two cells sharing a stimulatory molecule, and parameters akin to an extension of Landau's equilibrium phase transition theory. We show that different single-cell dynamics lead to the same dynamical response -- a flexibility that cells can use. The companion equilibrium Landau model behaves similarly, thereby describing the dynamics of information in a broad class of models with coupled order parameters.
{"title":"Flexibility in noisy cell-to-cell information dynamics","authors":"Ismail Qunbar, Michael Vennettilli, Amir Erez","doi":"arxiv-2407.06556","DOIUrl":"https://doi.org/arxiv-2407.06556","url":null,"abstract":"Exchange of molecules allows cells to exchange information. How robust is the\u0000information to changes in cell parameters? We use a mapping between the\u0000stochastic dynamics of two cells sharing a stimulatory molecule, and parameters\u0000akin to an extension of Landau's equilibrium phase transition theory. We show\u0000that different single-cell dynamics lead to the same dynamical response -- a\u0000flexibility that cells can use. The companion equilibrium Landau model behaves\u0000similarly, thereby describing the dynamics of information in a broad class of\u0000models with coupled order parameters.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How much information does a cell inherit from its ancestors beyond its genetic sequence? What are the epigenetic mechanisms that allow this? Despite the rise in available epigenetic data, how such information is inherited through the cell cycle is still not fully understood. Here, we develop and analyse a simple mathematical model for histone-based epigenetic information that describes how daughter cells can recapitulate the gene expression profiles of their parent. We consider the dynamics of histone modifications during the cell cycle deterministically but also incorporate the largest stochastic element: DNA replication, where histones are randomly distributed between the two daughter DNA strands. This hybrid stochastic-deterministic approach enables an analytic derivation of the switching rate, i.e., the frequency of loss-of-memory events due to replication. While retaining great simplicity, the model can recapitulate experimental switching rate data, establishing its biological importance as a framework to quantitatively study epigenetic inheritance.
除了遗传序列之外，细胞还能从祖先那里继承多少信息？有哪些表观遗传机制可以做到这一点？尽管现有的表观遗传学数据不断增加，但人们对这些信息是如何通过细胞周期继承下来的仍不完全清楚。在这里，我们建立并分析了一个基于组蛋白的表观遗传信息的简单数学模型，该模型描述了子细胞如何再现母细胞的基因表达谱。我们以确定性的方式考虑了组蛋白修饰在细胞周期中的动态变化，但也纳入了最大的随机因素：DNA 复制，组蛋白在两条子 DNA 链之间随机分布。这种随机-确定性混合方法可以分析推导出切换率，即复制导致的记忆丢失事件的频率。该模型非常简单，却能再现实验中的切换率数据，从而确立了其作为定量研究表观遗传学框架的生物学重要性。
{"title":"Theory of epigenetic switching due to stochastic histone mark loss during DNA replication","authors":"Ander Movilla Miangolarra, Martin Howard","doi":"arxiv-2407.06019","DOIUrl":"https://doi.org/arxiv-2407.06019","url":null,"abstract":"How much information does a cell inherit from its ancestors beyond its\u0000genetic sequence? What are the epigenetic mechanisms that allow this? Despite\u0000the rise in available epigenetic data, how such information is inherited\u0000through the cell cycle is still not fully understood. Here, we develop and\u0000analyse a simple mathematical model for histone-based epigenetic information\u0000that describes how daughter cells can recapitulate the gene expression profiles\u0000of their parent. We consider the dynamics of histone modifications during the\u0000cell cycle deterministically but also incorporate the largest stochastic\u0000element: DNA replication, where histones are randomly distributed between the\u0000two daughter DNA strands. This hybrid stochastic-deterministic approach enables\u0000an analytic derivation of the switching rate, i.e., the frequency of\u0000loss-of-memory events due to replication. While retaining great simplicity, the\u0000model can recapitulate experimental switching rate data, establishing its\u0000biological importance as a framework to quantitatively study epigenetic\u0000inheritance.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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