Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin
Developing an effective molecular generation framework even with a limited number of molecules is often important for its practical deployment, e.g., drug discovery, since acquiring task-related molecular data requires expensive and time-consuming experimental costs. To tackle this issue, we introduce Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method. HI-Mol is inspired by the importance of hierarchical information, e.g., both coarse- and fine-grained features, in understanding the molecule distribution. We propose to use multi-level embeddings to reflect such hierarchical features based on the adoption of the recent textual inversion technique in the visual domain, which achieves data-efficient image generation. Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution. We then generate molecules based on the interpolation of the multi-level token embeddings. Extensive experiments demonstrate the superiority of HI-Mol with notable data-efficiency. For instance, on QM9, HI-Mol outperforms the prior state-of-the-art method with 50x less training data. We also show the effectiveness of molecules generated by HI-Mol in low-shot molecular property prediction.
{"title":"Data-Efficient Molecular Generation with Hierarchical Textual Inversion","authors":"Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin","doi":"arxiv-2405.02845","DOIUrl":"https://doi.org/arxiv-2405.02845","url":null,"abstract":"Developing an effective molecular generation framework even with a limited\u0000number of molecules is often important for its practical deployment, e.g., drug\u0000discovery, since acquiring task-related molecular data requires expensive and\u0000time-consuming experimental costs. To tackle this issue, we introduce\u0000Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel\u0000data-efficient molecular generation method. HI-Mol is inspired by the\u0000importance of hierarchical information, e.g., both coarse- and fine-grained\u0000features, in understanding the molecule distribution. We propose to use\u0000multi-level embeddings to reflect such hierarchical features based on the\u0000adoption of the recent textual inversion technique in the visual domain, which\u0000achieves data-efficient image generation. Compared to the conventional textual\u0000inversion method in the image domain using a single-level token embedding, our\u0000multi-level token embeddings allow the model to effectively learn the\u0000underlying low-shot molecule distribution. We then generate molecules based on\u0000the interpolation of the multi-level token embeddings. Extensive experiments\u0000demonstrate the superiority of HI-Mol with notable data-efficiency. For\u0000instance, on QM9, HI-Mol outperforms the prior state-of-the-art method with 50x\u0000less training data. We also show the effectiveness of molecules generated by\u0000HI-Mol in low-shot molecular property prediction.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885702","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}
Dilawar Ahmad Mir, Zhengxin Ma, Jordan Horrocks, Aric N Rogers
The eukaryotic protein synthesis process entails intricate stages governed by diverse mechanisms to tightly regulate translation. Translational regulation during stress is pivotal for maintaining cellular homeostasis, ensuring the accurate expression of essential proteins crucial for survival. This selective translational control mechanism is integral to cellular adaptation and resilience under adverse conditions. This review manuscript explores various mechanisms involved in selective translational regulation, focusing on mRNA-specific and global regulatory processes. Key aspects of translational control include translation initiation, which is often a rate-limiting step, and involves the formation of the eIF4F complex and recruitment of mRNA to ribosomes. Regulation of translation initiation factors, such as eIF4E, eIF4E2, and eIF2, through phosphorylation and interactions with binding proteins, modulates translation efficiency under stress conditions. This review also highlights the control of translation initiation through factors like the eIF4F complex and the ternary complex and also underscores the importance of eIF2{alpha} phosphorylation in stress granule formation and cellular stress responses. Additionally, the impact of amino acid deprivation, mTOR signaling, and ribosome biogenesis on translation regulation and cellular adaptation to stress is also discussed. Understanding the intricate mechanisms of translational regulation during stress provides insights into cellular adaptation mechanisms and potential therapeutic targets for various diseases, offering valuable avenues for addressing conditions associated with dysregulated protein synthesis.
{"title":"Stress-induced Eukaryotic Translational Regulatory Mechanisms","authors":"Dilawar Ahmad Mir, Zhengxin Ma, Jordan Horrocks, Aric N Rogers","doi":"arxiv-2405.01664","DOIUrl":"https://doi.org/arxiv-2405.01664","url":null,"abstract":"The eukaryotic protein synthesis process entails intricate stages governed by\u0000diverse mechanisms to tightly regulate translation. Translational regulation\u0000during stress is pivotal for maintaining cellular homeostasis, ensuring the\u0000accurate expression of essential proteins crucial for survival. This selective\u0000translational control mechanism is integral to cellular adaptation and\u0000resilience under adverse conditions. This review manuscript explores various\u0000mechanisms involved in selective translational regulation, focusing on\u0000mRNA-specific and global regulatory processes. Key aspects of translational\u0000control include translation initiation, which is often a rate-limiting step,\u0000and involves the formation of the eIF4F complex and recruitment of mRNA to\u0000ribosomes. Regulation of translation initiation factors, such as eIF4E, eIF4E2,\u0000and eIF2, through phosphorylation and interactions with binding proteins,\u0000modulates translation efficiency under stress conditions. This review also\u0000highlights the control of translation initiation through factors like the eIF4F\u0000complex and the ternary complex and also underscores the importance of\u0000eIF2{alpha} phosphorylation in stress granule formation and cellular stress\u0000responses. Additionally, the impact of amino acid deprivation, mTOR signaling,\u0000and ribosome biogenesis on translation regulation and cellular adaptation to\u0000stress is also discussed. Understanding the intricate mechanisms of\u0000translational regulation during stress provides insights into cellular\u0000adaptation mechanisms and potential therapeutic targets for various diseases,\u0000offering valuable avenues for addressing conditions associated with\u0000dysregulated protein synthesis.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885469","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}
Alon Duvall, M. Ali Al-Radhawi, Dhruv D. Jatkar, Eduardo Sontag
This work studies relationships between monotonicity and contractivity, and applies the results to establish that many reaction networks are weakly contractive, and thus, under appropriate compactness conditions, globally convergent to equilibria. Verification of these properties is achieved through a novel algorithm that can be used to generate cones for monotone systems. The results given here allow a unified proof of global convergence for several classes of networks that had been previously studied in the literature.
{"title":"Interplay between Contractivity and Monotonicity for Reaction Networks","authors":"Alon Duvall, M. Ali Al-Radhawi, Dhruv D. Jatkar, Eduardo Sontag","doi":"arxiv-2404.18734","DOIUrl":"https://doi.org/arxiv-2404.18734","url":null,"abstract":"This work studies relationships between monotonicity and contractivity, and\u0000applies the results to establish that many reaction networks are weakly\u0000contractive, and thus, under appropriate compactness conditions, globally\u0000convergent to equilibria. Verification of these properties is achieved through\u0000a novel algorithm that can be used to generate cones for monotone systems. The\u0000results given here allow a unified proof of global convergence for several\u0000classes of networks that had been previously studied in the literature.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840463","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}
Roba Abukwaik, Elias Vera-Siguenza, Daniel Tennant, Fabian Spill
Cancer cells exhibit significant alterations in their metabolism, characterised by a reduction in oxidative phosphorylation (OXPHOS) and an increased reliance on glycolysis, even in the presence of oxygen. This metabolic shift, known as the Warburg effect, is pivotal in fuelling cancer's uncontrolled growth, invasion, and therapeutic resistance. While dysregulation of many genes contributes to this metabolic shift, the tumour suppressor gene p53 emerges as a master player. Yet, the molecular mechanisms remain elusive. This study introduces a comprehensive mathematical model, integrating essential p53 targets, offering insights into how p53 orchestrates its targets to redirect cancer metabolism towards an OXPHOS-dominant state. Simulation outcomes align closely with experimental data comparing glucose metabolism in colon cancer cells with wild-type and mutated p53. Additionally, our findings reveal the dynamic capability of elevated p53 activation to fully reverse the Warburg effect, highlighting the significance of its activity levels not just in triggering apoptosis (programmed cell death) post-chemotherapy but also in modifying the metabolic pathways implicated in treatment resistance. In scenarios of p53 mutations, our analysis suggests targeting glycolysis-instigating signalling pathways as an alternative strategy, whereas targeting solely synthesis of cytochrome c oxidase 2 (SCO2) does support mitochondrial respiration but may not effectively suppress the glycolysis pathway, potentially boosting the energy production and cancer cell viability.
{"title":"P53 Orchestrates Cancer Metabolism: Unveiling Strategies to Reverse the Warburg Effect","authors":"Roba Abukwaik, Elias Vera-Siguenza, Daniel Tennant, Fabian Spill","doi":"arxiv-2404.18613","DOIUrl":"https://doi.org/arxiv-2404.18613","url":null,"abstract":"Cancer cells exhibit significant alterations in their metabolism,\u0000characterised by a reduction in oxidative phosphorylation (OXPHOS) and an\u0000increased reliance on glycolysis, even in the presence of oxygen. This\u0000metabolic shift, known as the Warburg effect, is pivotal in fuelling cancer's\u0000uncontrolled growth, invasion, and therapeutic resistance. While dysregulation\u0000of many genes contributes to this metabolic shift, the tumour suppressor gene\u0000p53 emerges as a master player. Yet, the molecular mechanisms remain elusive.\u0000This study introduces a comprehensive mathematical model, integrating essential\u0000p53 targets, offering insights into how p53 orchestrates its targets to\u0000redirect cancer metabolism towards an OXPHOS-dominant state. Simulation\u0000outcomes align closely with experimental data comparing glucose metabolism in\u0000colon cancer cells with wild-type and mutated p53. Additionally, our findings\u0000reveal the dynamic capability of elevated p53 activation to fully reverse the\u0000Warburg effect, highlighting the significance of its activity levels not just\u0000in triggering apoptosis (programmed cell death) post-chemotherapy but also in\u0000modifying the metabolic pathways implicated in treatment resistance. In\u0000scenarios of p53 mutations, our analysis suggests targeting\u0000glycolysis-instigating signalling pathways as an alternative strategy, whereas\u0000targeting solely synthesis of cytochrome c oxidase 2 (SCO2) does support\u0000mitochondrial respiration but may not effectively suppress the glycolysis\u0000pathway, potentially boosting the energy production and cancer cell viability.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840549","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 this work, we study a class of hybrid dynamical systems called hybrid gene regulatory networks (HGRNs) which was proposed to model gene regulatory networks. In HGRNs, there exist well-behaved trajectories that reach a fixed point or converge to a limit cycle, as well as chaotic trajectories that behave non-periodic or indeterministic. In our work, we investigate these irregular behaviors of HGRNs and present theoretical results about the decidability of the reachability problem, the probability of indeterministic behavior of HGRNs, and chaos especially in 2-dimensional HGRNs.
{"title":"On Hybrid Gene Regulatory Networks","authors":"Adrian Wurm, Honglu Sun","doi":"arxiv-2404.16197","DOIUrl":"https://doi.org/arxiv-2404.16197","url":null,"abstract":"In this work, we study a class of hybrid dynamical systems called hybrid gene\u0000regulatory networks (HGRNs) which was proposed to model gene regulatory\u0000networks. In HGRNs, there exist well-behaved trajectories that reach a fixed\u0000point or converge to a limit cycle, as well as chaotic trajectories that behave\u0000non-periodic or indeterministic. In our work, we investigate these irregular\u0000behaviors of HGRNs and present theoretical results about the decidability of\u0000the reachability problem, the probability of indeterministic behavior of HGRNs,\u0000and chaos especially in 2-dimensional HGRNs.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140798779","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}
Keisuke Sugie, Dimitri Loutchko, Tetsuya J. Tobayashi
Chemical reaction networks (CRNs) exhibit complex dynamics governed by their underlying network structure. In this paper, we propose a novel approach to study the dynamics of CRNs by representing them on species graphs (S-graphs). By scaling concentrations by conservation laws, we obtain a graph representation of transitions compatible with the S-graph, which allows us to treat the dynamics in CRNs as transitions between chemicals. We also define thermodynamic-like quantities on the S-graph from the introduced transitions and investigate their properties, including the relationship between specieswise forces, activities, and conventional thermodynamic quantities. Remarkably, we demonstrate that this formulation can be developed for a class of irreversible CRNs, while for reversible CRNs, it is related to conventional thermodynamic quantities associated with reactions. The behavior of these specieswise quantities is numerically validated using an oscillating system (Brusselator). Our work provides a novel methodology for studying dynamics on S-graphs, paving the way for a deeper understanding of the intricate interplay between the structure and dynamics of chemical reaction networks.
化学反应网络（CRN）表现出复杂的动力学，受其基本网络结构的支配。在本文中，我们提出了一种研究 CRN 动力学的新方法，即在物种图（S-graph）上表示 CRN。通过守恒定律缩放浓度，我们获得了与 S-graph（物种图）兼容的转换图表示法，从而可以将 CRN 中的动力学视为化学物质之间的转换。我们还根据引入的转换定义了 S 图上的类热力学量，并研究了它们的性质，包括种力、活动和传统热力学量之间的关系。我们使用一个振荡系统（布鲁塞尔器）对这些按物种划分的量的行为进行了数值验证。我们的工作为研究 S 图上的动力学提供了一种新方法，为深入理解化学反应网络结构与动力学之间错综复杂的相互作用铺平了道路。
{"title":"Transitions and Thermodynamics on Species Graphs of Chemical Reaction Networks","authors":"Keisuke Sugie, Dimitri Loutchko, Tetsuya J. Tobayashi","doi":"arxiv-2404.14336","DOIUrl":"https://doi.org/arxiv-2404.14336","url":null,"abstract":"Chemical reaction networks (CRNs) exhibit complex dynamics governed by their\u0000underlying network structure. In this paper, we propose a novel approach to\u0000study the dynamics of CRNs by representing them on species graphs (S-graphs).\u0000By scaling concentrations by conservation laws, we obtain a graph\u0000representation of transitions compatible with the S-graph, which allows us to\u0000treat the dynamics in CRNs as transitions between chemicals. We also define\u0000thermodynamic-like quantities on the S-graph from the introduced transitions\u0000and investigate their properties, including the relationship between\u0000specieswise forces, activities, and conventional thermodynamic quantities.\u0000Remarkably, we demonstrate that this formulation can be developed for a class\u0000of irreversible CRNs, while for reversible CRNs, it is related to conventional\u0000thermodynamic quantities associated with reactions. The behavior of these\u0000specieswise quantities is numerically validated using an oscillating system\u0000(Brusselator). Our work provides a novel methodology for studying dynamics on\u0000S-graphs, paving the way for a deeper understanding of the intricate interplay\u0000between the structure and dynamics of chemical reaction networks.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140798784","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}
Stem cell regeneration is a vital biological process in self-renewing tissues, governing development and tissue homeostasis. Gene regulatory network dynamics are pivotal in controlling stem cell regeneration and cell type transitions. However, integrating the quantitative dynamics of gene regulatory networks at the single-cell level with stem cell regeneration at the population level poses significant challenges. This study presents a computational framework connecting gene regulatory network dynamics with stem cell regeneration through a data-driven formulation of the inheritance function. The inheritance function captures epigenetic state transitions during cell division in heterogeneous stem cell populations. Our scheme allows the derivation of the inheritance function based on a hybrid model of cross-cell-cycle gene regulation network dynamics. The proposed scheme enables us to derive the inheritance function based on the hybrid model of cross-cell-cycle gene regulation network dynamics. By explicitly incorporating gene regulatory network structure, it replicates cross-cell-cycling gene regulation dynamics through individual-cell-based modeling. The numerical scheme holds the potential for extension to diverse gene regulatory networks, facilitating a deeper understanding of the connection between gene regulation dynamics and stem cell regeneration.
{"title":"A computational scheme connecting gene regulatory network dynamics with heterogeneous stem cell regeneration","authors":"Yakun Li, Xiyin Liang, Jinzhi Lei","doi":"arxiv-2404.11761","DOIUrl":"https://doi.org/arxiv-2404.11761","url":null,"abstract":"Stem cell regeneration is a vital biological process in self-renewing\u0000tissues, governing development and tissue homeostasis. Gene regulatory network\u0000dynamics are pivotal in controlling stem cell regeneration and cell type\u0000transitions. However, integrating the quantitative dynamics of gene regulatory\u0000networks at the single-cell level with stem cell regeneration at the population\u0000level poses significant challenges. This study presents a computational\u0000framework connecting gene regulatory network dynamics with stem cell\u0000regeneration through a data-driven formulation of the inheritance function. The\u0000inheritance function captures epigenetic state transitions during cell division\u0000in heterogeneous stem cell populations. Our scheme allows the derivation of the\u0000inheritance function based on a hybrid model of cross-cell-cycle gene\u0000regulation network dynamics. The proposed scheme enables us to derive the\u0000inheritance function based on the hybrid model of cross-cell-cycle gene\u0000regulation network dynamics. By explicitly incorporating gene regulatory\u0000network structure, it replicates cross-cell-cycling gene regulation dynamics\u0000through individual-cell-based modeling. The numerical scheme holds the\u0000potential for extension to diverse gene regulatory networks, facilitating a\u0000deeper understanding of the connection between gene regulation dynamics and\u0000stem cell regeneration.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626215","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}
Cellular decision-making (CDM) is a dynamic phenomenon often controlled by regulatory networks defining interactions between genes and transcription factor proteins. Traditional studies have focussed on molecular switches such as positive feedback circuits that exhibit at most bistability. However, higher-order dynamics such as tristability is also prominent in many biological processes. It is thus imperative to identify a minimal circuit that can alone explain mono, bi, and tristable dynamics. In this work, we consider a two-component positive feedback network with an autoloop and explore these regimes of stability for different degrees of multimerization and the choice of Boolean logic functions. We report that this network can exhibit numerous dynamical scenarios such as bi-and tristability, hysteresis, and biphasic kinetics, explaining the possibilities of abrupt cell state transitions and the smooth state swap without a step-like switch. Specifically, while with monomeric regulation and competitive OR logic, the circuit exhibits mono-and bistability and biphasic dynamics, with non-competitive AND and OR logics only monostability can be achieved. To obtain bistability in the latter cases, we show that the autoloop must have (at least) dimeric regulation. In pursuit of higher-order stability, we show that tristability occurs with higher degrees of multimerization and with non-competitive OR logic only. Our results, backed by rigorous analytical calculations and numerical examples, thus explain the association between multistability, multimerization, and logic in this minimal circuit. Since this circuit underlies various biological processes, including epithelial-mesenchymal transition which often drives carcinoma metastasis, these results can thus offer crucial inputs to control cell state transition by manipulating multimerization and the logic of regulation in cells.
细胞决策（CDM）是一种动态现象，通常由定义基因和转录因子蛋白之间相互作用的调控网络控制。传统研究侧重于分子开关，如最多表现出双稳态的正反馈电路。然而，三稳态等高阶动力学在许多生物过程中也很突出。因此，当务之急是找出一种能单独解释单稳态、双稳态和三稳态动力学的最小电路。在这项研究中，我们考虑了具有自动环路的双分量正反馈网络，并探索了不同多聚化程度和布尔逻辑函数选择下的稳定状态。我们发现，这种网络可以表现出多种动力学情景，如双向和三向稳定性、滞后性和双镰刀动力学，从而解释了细胞状态突然转换和无阶跃开关的平滑状态交换的可能性。具体来说，在单调调节和竞争性 OR 逻辑下，电路表现出单双稳态和双相动力学，而在非竞争性 AND 和 OR 逻辑下，只能实现单稳态。要在后一种情况下获得双稳态性，我们需要证明自动环路必须（至少）具有二聚调节。为了追求更高阶的稳定性，我们证明了在更高的多聚化程度下以及仅在非竞争性 OR 逻辑下会出现三稳态。我们的研究结果得到了严密的分析计算和数字实例的支持，从而解释了这一最小电路中多态性、多聚化和逻辑之间的关联。由于这一电路是各种生物过程的基础，包括上皮-间质转化过程，而上皮-间质转化过程往往是癌细胞转移的驱动因素，因此这些结果可以为通过操纵细胞中的多聚化和调控逻辑来控制细胞状态转化提供重要的输入。
{"title":"Logic-dependent emergence of multistability, hysteresis, and biphasic dynamics in a minimal positive feedback network with an autoloop","authors":"Akriti Srivastava, Mubasher Rashid","doi":"arxiv-2404.05379","DOIUrl":"https://doi.org/arxiv-2404.05379","url":null,"abstract":"Cellular decision-making (CDM) is a dynamic phenomenon often controlled by\u0000regulatory networks defining interactions between genes and transcription\u0000factor proteins. Traditional studies have focussed on molecular switches such\u0000as positive feedback circuits that exhibit at most bistability. However,\u0000higher-order dynamics such as tristability is also prominent in many biological\u0000processes. It is thus imperative to identify a minimal circuit that can alone\u0000explain mono, bi, and tristable dynamics. In this work, we consider a\u0000two-component positive feedback network with an autoloop and explore these\u0000regimes of stability for different degrees of multimerization and the choice of\u0000Boolean logic functions. We report that this network can exhibit numerous\u0000dynamical scenarios such as bi-and tristability, hysteresis, and biphasic\u0000kinetics, explaining the possibilities of abrupt cell state transitions and the\u0000smooth state swap without a step-like switch. Specifically, while with\u0000monomeric regulation and competitive OR logic, the circuit exhibits mono-and\u0000bistability and biphasic dynamics, with non-competitive AND and OR logics only\u0000monostability can be achieved. To obtain bistability in the latter cases, we\u0000show that the autoloop must have (at least) dimeric regulation. In pursuit of\u0000higher-order stability, we show that tristability occurs with higher degrees of\u0000multimerization and with non-competitive OR logic only. Our results, backed by\u0000rigorous analytical calculations and numerical examples, thus explain the\u0000association between multistability, multimerization, and logic in this minimal\u0000circuit. Since this circuit underlies various biological processes, including\u0000epithelial-mesenchymal transition which often drives carcinoma metastasis,\u0000these results can thus offer crucial inputs to control cell state transition by\u0000manipulating multimerization and the logic of regulation in cells.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569138","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}
Recent technological advances allow us to view chemical mass-action systems as analog computers. In this context, the inputs to a computation are encoded as initial values of certain chemical species while the outputs are the limiting values of other chemical species. In this paper, we design chemical systems that carry out the elementary arithmetic computations of: identification, inversion, $m$th roots (for $m ge 2$), addition, multiplication, absolute difference, rectified subtraction over non-negative real numbers, and partial real inversion over real numbers. We prove that these ``elementary modules'' have a speed of computation that is independent of the inputs to the computation. Moreover, we prove that finite sequences of such elementary modules, running in parallel, can carry out composite arithmetic over real numbers, also at a rate that is independent of inputs. Furthermore, we show that the speed of a composite computation is precisely the speed of the slowest elementary step. Specifically, the scale of the composite computation, i.e. the number of elementary steps involved in the composite, does not affect the overall asymptotic speed -- a feature of the parallel computing nature of our algorithm. Our proofs require the careful mathematical analysis of certain non-autonomous systems, and we believe this analysis will be useful in different areas of applied mathematics, dynamical systems, and the theory of computation. We close with a discussion on future research directions, including numerous important open theoretical questions pertaining to the field of computation with reaction networks.
最近的技术进步使我们能够将化学物质作用系统视为模拟计算机。在这种情况下，计算的输入被编码为某些化学物质的初始值，而输出则是其他化学物质的极限值。在本文中，我们设计的化学系统可以进行以下基本算术计算：识别、反转、$m$次根（对于$m ge 2$）、加法、乘法、绝对差、非负实数的整式减法和实数的部分实数反转。我们证明，这些 "基本模块 "的计算速度与计算的输入无关。此外，我们还证明了并行运行的这些 "基本模块 "的有限序列可以对实数进行复合运算，而且运算速度与输入无关。此外，我们还证明了复合计算的速度正是最慢基本步的速度。具体来说，复合计算的规模，即参与复合计算的基本步数，不会影响整体渐近速度--这是我们算法的并行计算特性。我们的证明需要对某些非自治系统进行仔细的数学分析，我们相信这种分析将在应用数学、动力系统和计算理论等领域大有用武之地。最后，我们讨论了未来的研究方向，包括与反应网络计算领域相关的许多重要的开放性理论问题。
{"title":"Chemical mass-action systems as analog computers: implementing arithmetic computations at specified speed","authors":"David F. Anderson, Badal Joshi","doi":"arxiv-2404.04396","DOIUrl":"https://doi.org/arxiv-2404.04396","url":null,"abstract":"Recent technological advances allow us to view chemical mass-action systems\u0000as analog computers. In this context, the inputs to a computation are encoded\u0000as initial values of certain chemical species while the outputs are the\u0000limiting values of other chemical species. In this paper, we design chemical\u0000systems that carry out the elementary arithmetic computations of:\u0000identification, inversion, $m$th roots (for $m ge 2$), addition,\u0000multiplication, absolute difference, rectified subtraction over non-negative\u0000real numbers, and partial real inversion over real numbers. We prove that these\u0000``elementary modules'' have a speed of computation that is independent of the\u0000inputs to the computation. Moreover, we prove that finite sequences of such\u0000elementary modules, running in parallel, can carry out composite arithmetic\u0000over real numbers, also at a rate that is independent of inputs. Furthermore,\u0000we show that the speed of a composite computation is precisely the speed of the\u0000slowest elementary step. Specifically, the scale of the composite computation,\u0000i.e. the number of elementary steps involved in the composite, does not affect\u0000the overall asymptotic speed -- a feature of the parallel computing nature of\u0000our algorithm. Our proofs require the careful mathematical analysis of certain\u0000non-autonomous systems, and we believe this analysis will be useful in\u0000different areas of applied mathematics, dynamical systems, and the theory of\u0000computation. We close with a discussion on future research directions,\u0000including numerous important open theoretical questions pertaining to the field\u0000of computation with reaction networks.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569409","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}
Jakob L. Andersen, Akbar Davoodi, Rolf Fagerberg, Christoph Flamm, Walter Fontana, Juri Kolčák, Christophe V. F. P. Laurent, Daniel Merkle, Nikolai Nøjgaard
The explosion of data available in life sciences is fueling an increasing demand for expressive models and computational methods. Graph transformation is a model for dynamic systems with a large variety of applications. We introduce a novel method of the graph transformation model construction, combining generative and dynamical viewpoints to give a fully automated data-driven model inference method. The method takes the input dynamical properties, given as a "snapshot" of the dynamics encoded by explicit transitions, and constructs a compatible model. The obtained model is guaranteed to be minimal, thus framing the approach as model compression (from a set of transitions into a set of rules). The compression is permissive to a lossy case, where the constructed model is allowed to exhibit behavior outside of the input transitions, thus suggesting a completion of the input dynamics. The task of graph transformation model inference is naturally highly challenging due to the combinatorics involved. We tackle the exponential explosion by proposing a heuristically minimal translation of the task into a well-established problem, set cover, for which highly optimized solutions exist. We further showcase how our results relate to Kolmogorov complexity expressed in terms of graph transformation.
{"title":"Automated Inference of Graph Transformation Rules","authors":"Jakob L. Andersen, Akbar Davoodi, Rolf Fagerberg, Christoph Flamm, Walter Fontana, Juri Kolčák, Christophe V. F. P. Laurent, Daniel Merkle, Nikolai Nøjgaard","doi":"arxiv-2404.02692","DOIUrl":"https://doi.org/arxiv-2404.02692","url":null,"abstract":"The explosion of data available in life sciences is fueling an increasing\u0000demand for expressive models and computational methods. Graph transformation is\u0000a model for dynamic systems with a large variety of applications. We introduce\u0000a novel method of the graph transformation model construction, combining\u0000generative and dynamical viewpoints to give a fully automated data-driven model\u0000inference method. The method takes the input dynamical properties, given as a \"snapshot\" of the\u0000dynamics encoded by explicit transitions, and constructs a compatible model.\u0000The obtained model is guaranteed to be minimal, thus framing the approach as\u0000model compression (from a set of transitions into a set of rules). The\u0000compression is permissive to a lossy case, where the constructed model is\u0000allowed to exhibit behavior outside of the input transitions, thus suggesting a\u0000completion of the input dynamics. The task of graph transformation model inference is naturally highly\u0000challenging due to the combinatorics involved. We tackle the exponential\u0000explosion by proposing a heuristically minimal translation of the task into a\u0000well-established problem, set cover, for which highly optimized solutions\u0000exist. We further showcase how our results relate to Kolmogorov complexity\u0000expressed in terms of graph transformation.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140594500","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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