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Cell reprogramming design by transfer learning of functional transcriptional networks 通过转移学习功能转录网络进行细胞重编程设计
Pub Date : 2024-03-07 DOI: arxiv-2403.04837
Thomas P. Wytock, Adilson E. Motter
Recent developments in synthetic biology, next-generation sequencing, andmachine learning provide an unprecedented opportunity to rationally design newdisease treatments based on measured responses to gene perturbations and drugsto reprogram cells. The main challenges to seizing this opportunity are theincomplete knowledge of the cellular network and the combinatorial explosion ofpossible interventions, both of which are insurmountable by experiments. Toaddress these challenges, we develop a transfer learning approach to controlcell behavior that is pre-trained on transcriptomic data associated with humancell fates, thereby generating a model of the network dynamics that can betransferred to specific reprogramming goals. The approach combinestranscriptional responses to gene perturbations to minimize the differencebetween a given pair of initial and target transcriptional states. Wedemonstrate our approach's versatility by applying it to a microarray datasetcomprising >9,000 microarrays across 54 cell types and 227 uniqueperturbations, and an RNASeq dataset consisting of >10,000 sequencing runsacross 36 cell types and 138 perturbations. Our approach reproduces knownreprogramming protocols with an AUROC of 0.91 while innovating over existingmethods by pre-training an adaptable model that can be tailored to specificreprogramming transitions. We show that the number of gene perturbationsrequired to steer from one fate to another increases with decreasingdevelopmental relatedness and that fewer genes are needed to progress alongdevelopmental paths than to regress. These findings establish aproof-of-concept for our approach to computationally design control strategiesand provide insights into how gene regulatory networks govern phenotype.
合成生物学、下一代测序和机器学习的最新发展为我们提供了一个前所未有的机会,可以根据对基因扰动和重编程细胞药物的测量反应,合理地设计新的疾病治疗方法。抓住这一机遇所面临的主要挑战是细胞网络知识的不完整和可能干预措施的组合爆炸,而这两点都是实验无法克服的。为了应对这些挑战,我们开发了一种转移学习方法来控制细胞行为,这种方法在与人体细胞命运相关的转录组数据上进行预训练,从而生成一个可转移到特定重编程目标的网络动力学模型。这种方法结合了转录对基因扰动的反应,以最小化给定的一对初始转录状态和目标转录状态之间的差异。我们将这一方法应用于一个包括 54 种细胞类型和 227 种独特扰动的超过 9000 个微阵列数据集,以及一个包括 36 种细胞类型和 138 种扰动的超过 10000 次测序的 RNASeq 数据集,从而展示了它的多功能性。我们的方法重现了已知的编程协议,AUROC 为 0.91,同时通过预训练一个可适应特定编程转换的适应性模型,对现有方法进行了创新。我们的研究表明,从一种命运转向另一种命运所需的基因扰动数量随着发育相关性的降低而增加,沿着发育路径前进所需的基因数量少于退步所需的基因数量。这些发现为我们计算设计控制策略的方法建立了概念验证,并为了解基因调控网络如何调控表型提供了启示。
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引用次数: 0
Hill Function-based Model of Transcriptional Response: Impact of Nonspecific Binding and RNAP Interactions 基于希尔功能的转录反应模型:非特异性结合和 RNAP 相互作用的影响
Pub Date : 2024-03-04 DOI: arxiv-2403.01702
Wenjia Shi, Yao Ma, Peilin Hu, Mi Pang, Xiaona Huang, Yiting Dang, Yuxin Xie, Danni Wu
Hill function is one of the widely used gene transcription regulation models.Its attribute of fitting may result in a lack of an underlying physicalpicture, yet the fitting parameters can provide information about biochemicalreactions, such as the number of transcription factors (TFs) and the bindingenergy between regulatory elements. However, it remains unclear when and howmuch biochemical information can Hill function provide in addition to fitting.Here, started from the interactions between TFs and RNA polymerase duringtranscription regulation and both of their association-dissociation reactionsat specific/nonspecific sites on DNA, the regulatory effect of TFs was deducedas fold change. We found that, for weak promoter, fold change can degrade intothe regulatory factor (Freg) which is closely correlated with Hill function. Bydirectly comparing and fitting with Hill function, the fitting parameters andcorresponding biochemical reaction parameters in Freg were analyzed anddiscussed, where the single TF and multiple TFs that with cooperativity andbasic logic effects were considered. We concluded the strength of promoter andinteractions between TFs determine whether Hill function can reflect thecorresponding biochemical information. Our findings highlight the role of Hillfunction in modeling/fitting for transcriptional regulation, which alsobenefits the preparation of synthetic regulatory elements.
希尔函数是被广泛使用的基因转录调控模型之一。它的拟合属性可能导致缺乏基本的物理图像,但拟合参数可以提供生化反应信息,如转录因子(TFs)的数量和调控元件之间的结合能。在此,我们从转录调控过程中 TFs 与 RNA 聚合酶之间的相互作用以及它们在 DNA 上特异/非特异位点的结合-解离反应入手,推导出 TFs 的调控效应折叠变化。我们发现,对于弱启动子,折叠变化可降解为与希尔函数密切相关的调控因子(Freg)。通过与希尔函数的直接比较和拟合,分析和讨论了 Freg 中的拟合参数和相应的生化反应参数,其中考虑了单个 TF 和具有合作性和基本逻辑效应的多个 TF。我们的结论是,启动子的强度和 TF 之间的相互作用决定了希尔函数能否反映相应的生化信息。我们的研究结果突出了希尔函数在转录调控建模/拟合中的作用,这也有利于合成调控元件的制备。
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引用次数: 0
Waves, patterns and bifurcations: a tutorial review on the vertebrate segmentation clock 波浪、模式和分叉:脊椎动物分节钟教程回顾
Pub Date : 2024-03-01 DOI: arxiv-2403.00457
Paul François, Victoria Mochulska
Proper vertebrae formation relies on a tissue-wide oscillator called thesegmentation clock. Individual cellular oscillators in the presomitic mesodermare modulated by intercellular coupling and external signals, leading to thepropagation of oscillatory waves of genetic expression eventually stabilizinginto a static pattern of genetic expression. Here, we review 4 decades ofbiophysical models of this process, starting from the pioneering Clock andWavefront model by Cooke and Zeeman, and the reaction-diffusion model byMeinhardt. We discuss how modern descriptions followed advances in moleculardescription and visualization of the process, reviewing phase models, delayedmodels, systems-level, and finally geometric models. We connect models tohigh-level aspects of embryonic development from embryonic scaling to wavepropagation, up to reconstructed stem cell systems. We provide new analyticalcalculations and insights into classical and recent models, leading us topropose a geometric description of somitogenesis organized along two primarywaves of differentiation.
椎体的正常形成依赖于一种被称为 "分节钟 "的全组织振荡器。瘤前中胚层中的单个细胞振荡器受细胞间耦合和外部信号的调节,导致基因表达振荡波的传播,最终稳定为基因表达的静态模式。在此,我们回顾了 40 年来有关这一过程的生物物理模型,包括库克和齐曼首创的时钟和波前模型,以及明哈特的反应-扩散模型。我们讨论了现代描述如何追随分子描述和过程可视化的进步,回顾了相模型、延迟模型、系统级模型以及几何模型。我们将模型与胚胎发育的高层次方面联系起来,从胚胎缩放到波传播,直至重建干细胞系统。我们对经典模型和最新模型进行了新的分析计算并提出了新的见解,从而提出了按照两个主要分化波来组织体细胞发生的几何描述。
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引用次数: 0
Difficult control is related to instability in biologically inspired Boolean networks 难以控制与生物启发布尔网络的不稳定性有关
Pub Date : 2024-02-28 DOI: arxiv-2402.18757
Bryan C. Daniels, Enrico Borriello
Previous work in Boolean dynamical networks has suggested that the number ofcomponents that must be controlled to select an existing attractor is typicallyset by the number of attractors admitted by the dynamics, with no dependence onthe size of the network. Here we study the rare cases of networks that defythis expectation, with attractors that require controlling most nodes. We findempirically that unstable fixed points are the primary recurring characteristicof networks that prove more difficult to control. We describe an efficient wayto identify unstable fixed points and show that, in both existing biologicalmodels and ensembles of random dynamics, we can better explain the variance ofcontrol kernel sizes by incorporating the prevalence of unstable fixed points.In the end, the fact that these exceptions are associated with dynamics thatare unstable to small perturbations hints that they are likely an artifact ofusing deterministic models. These exceptions are likely to be biologicallyirrelevant, supporting the generality of easy controllability in biologicalnetworks.
以往在布尔动力学网络方面的研究表明,选择现有吸引子所需控制的部件数量通常取决于动力学所接纳的吸引子数量,而与网络的大小无关。在这里,我们研究了网络中违背这一预期的罕见情况,即吸引子需要控制大多数节点。我们通过经验发现,不稳定的固定点是证明更难控制的网络的主要重复特征。我们描述了一种识别不稳定定点的有效方法,并表明在现有的生物模型和随机动力学集合中,我们可以通过纳入不稳定定点的普遍性来更好地解释控制核大小的方差。这些例外情况很可能与生物相关,支持了生物网络中易控性的普遍性。
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引用次数: 0
Modelling 1D Partially Absorbing Boundaries for Brownian Molecular Communication Channels 布朗分子通讯通道的一维部分吸收边界建模
Pub Date : 2024-02-24 DOI: arxiv-2402.15888
Caglar Koca, Ozgur B. Akan
Molecular Communication (MC) architectures suffer from molecular build-up inthe channel if they do not have appropriate reuptake mechanisms. The molecularbuild-up either leads to intersymbol interference (ISI) or reduces thetransmission rate. To measure the molecular build-up, we derive analyticexpressions for the incidence rate and absorption rate for one-dimensional MCchannels where molecular dispersion obeys the Brownian Motion. We verify eachof our key results with Monte Carlo simulations. Our results contribute to thedevelopment of more complicated models and analytic expressions to measure themolecular build-up and the impact of ISI in MC.
分子通信(MC)架构如果没有适当的再吸收机制,就会受到信道内分子堆积的影响。分子集结要么导致符号间干扰(ISI),要么降低传输速率。为了测量分子堆积,我们推导出了分子弥散服从布朗运动的一维 MC 信道的入射率和吸收率的分析表达式。我们用蒙特卡罗模拟验证了每一个关键结果。我们的结果有助于开发更复杂的模型和解析表达式,以测量 MC 中的分子集聚和 ISI 的影响。
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引用次数: 0
Effective Kinetics of Chemical Reaction Networks 化学反应网络的有效动力学
Pub Date : 2024-02-19 DOI: arxiv-2402.11762
Tomoharu Suda
Chemical reaction network theory is a powerful framework to describe andanalyze chemical systems. While much about the concentration profile in anequilibrium state can be determined in terms of the graph structure, theoverall reaction's time evolution depends on the network's kinetic ratefunction. In this article, we consider the problem of the effective kinetics ofa chemical reaction network regarded as a conversion system from the feedingspecies to products. We define the notion of effective kinetics as a partialsolution of a system of non-autonomous ordinary differential equationsdetermined from a chemical reaction network. Examples of actual calculationsinclude the Michaelis-Menten mechanism, for which it is confirmed that ournotion of effective kinetics yields the classical formula. Further, weintroduce the notion of straight-line solutions of non-autonomous ordinarydifferential equations to formalize the situation where a well-defined reactionrate exists and consider its relation with the quasi-stationary stateapproximation used in microkinetics. Our considerations here give a unifiedframework to formulate the reaction rate of chemical reaction networks.
化学反应网络理论是描述和分析化学系统的强大框架。平衡状态下的浓度分布可以根据图结构确定,而整个反应的时间演化则取决于网络的动力学速率函数。在本文中,我们将化学反应网络视为一个从原料到产物的转化系统,考虑其有效动力学问题。我们将有效动力学的概念定义为由化学反应网络确定的非自治常微分方程系统的部分解。实际计算的例子包括 Michaelis-Menten 机理,对该机理的研究证实,我们的有效动力学运动可以得到经典公式。此外,我们还引入了非自治常微分方程直线解的概念,将存在定义明确的反应速率的情况正规化,并考虑了它与微动力学中使用的准稳态近似的关系。我们在这里的考虑为化学反应网络的反应速率提供了一个统一的表述框架。
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引用次数: 0
Canalization reduces the nonlinearity of regulation in biological networks 渠化降低了生物网络调控的非线性
Pub Date : 2024-02-15 DOI: arxiv-2402.09703
Claus Kadelka, David Murrugarra
Biological networks such as gene regulatory networks possess desirableproperties. They are more robust and controllable than random networks. Thismotivates the search for structural and dynamical features that evolution hasincorporated in biological networks. A recent meta-analysis of published,expert-curated Boolean biological network models has revealed several suchfeatures, often referred to as design principles. Among others, the biologicalnetworks are enriched for certain recurring network motifs, the dynamic updaterules are more redundant, more biased and more canalizing than expected, andthe dynamics of biological networks are better approximable by linear andlower-order approximations than those of comparable random networks. Since mostof these features are interrelated, it is paramount to disentangle cause andeffect, that is, to understand which features evolution actively selects for,and thus truly constitute evolutionary design principles. Here, we show thatapproximability is strongly dependent on the dynamical robustness of a network,and that increased canalization in biological networks can almost completelyexplain their recently postulated high approximability.
基因调控网络等生物网络具有理想的特性。与随机网络相比,它们更具鲁棒性和可控性。这就促使人们寻找生物网络在进化过程中融入的结构和动态特征。最近对已发表的、由专家编辑的布尔生物网络模型进行的荟萃分析发现了几个这样的特征,这些特征通常被称为设计原则。其中包括:生物网络富含某些重复出现的网络图案;动态更新模块比预期的更冗余、更偏向和更渠化;与同类随机网络相比,生物网络的动态可通过线性和低阶近似更好地近似。由于这些特征大多相互关联,因此最重要的是厘清因果关系,即了解哪些特征是进化主动选择的,从而真正构成进化设计原则。在这里,我们证明了近似性与网络的动态鲁棒性密切相关,而生物网络中运河化程度的提高几乎可以完全解释最近推测的高近似性。
{"title":"Canalization reduces the nonlinearity of regulation in biological networks","authors":"Claus Kadelka, David Murrugarra","doi":"arxiv-2402.09703","DOIUrl":"https://doi.org/arxiv-2402.09703","url":null,"abstract":"Biological networks such as gene regulatory networks possess desirable\u0000properties. They are more robust and controllable than random networks. This\u0000motivates the search for structural and dynamical features that evolution has\u0000incorporated in biological networks. A recent meta-analysis of published,\u0000expert-curated Boolean biological network models has revealed several such\u0000features, often referred to as design principles. Among others, the biological\u0000networks are enriched for certain recurring network motifs, the dynamic update\u0000rules are more redundant, more biased and more canalizing than expected, and\u0000the dynamics of biological networks are better approximable by linear and\u0000lower-order approximations than those of comparable random networks. Since most\u0000of these features are interrelated, it is paramount to disentangle cause and\u0000effect, that is, to understand which features evolution actively selects for,\u0000and thus truly constitute evolutionary design principles. Here, we show that\u0000approximability is strongly dependent on the dynamical robustness of a network,\u0000and that increased canalization in biological networks can almost completely\u0000explain their recently postulated high approximability.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"102 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761786","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}
引用次数: 0
Computing Threshold Circuits with Void Reactions in Step Chemical Reaction Networks 利用阶跃化学反应网络中的虚空反应计算阈值电路
Pub Date : 2024-02-13 DOI: arxiv-2402.08220
Rachel Anderson, Alberto Avila, Bin Fu, Timothy Gomez, Elise Grizzell, Aiden Massie, Gourab Mukhopadhyay, Adrian Salinas, Robert Schweller, Evan Tomai, Tim Wylie
We introduce a new model of emph{step} Chemical Reaction Networks (stepCRNs), motivated by the step-wise addition of materials in standard labprocedures. Step CRNs have ordered reactants that transform into products viareaction rules over a series of steps. We study an important subset of weakreaction rules, emph{void} rules, in which chemical species may only bedeleted but never changed. We demonstrate the capabilities of these simplelimited systems to simulate threshold circuits and compute functions usingvarious configurations of rule sizes and step constructions, and prove thatwithout steps, void rules are incapable of these computations, which furthermotivates the step model. Additionally, we prove the coNP-completeness ofverifying if a given step CRN computes a function, holding even for $O(1)$ stepsystems.
我们介绍了一种新的(emph{step})化学反应网络(stepCRNs)模型,其灵感来自于标准实验过程中材料的分步添加。步骤化学反应网络具有有序的反应物,这些反应物通过一系列步骤的反应规则转化为产物。我们研究了弱反应规则的一个重要子集--"emph{void}规则",在这个规则中,化学物种只能被删除,但永远不会发生变化。我们证明了这些简单有限系统模拟阈值电路的能力,以及利用规则大小和步骤构造的各种配置计算函数的能力,并证明了如果没有步骤,空洞规则就无法进行这些计算,这进一步推动了步骤模型的建立。此外,我们还证明了验证给定步骤 CRN 是否计算函数的 coNP 完备性,即使对于 $O(1)$ 步骤系统也是如此。
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引用次数: 0
Predicting concentration changes via discrete sampling 通过离散采样预测浓度变化
Pub Date : 2024-02-08 DOI: arxiv-2402.05825
Age J. Tjalma, Pieter Rein ten Wolde
To successfully navigate chemical gradients, microorganisms need to predicthow the ligand concentration changes in space. Due to their limited size, theydo not take a spatial derivative over their body length but rather a temporalderivative, comparing the current signal with that in the recent past, over theso-called adaptation time. This strategy is pervasive in biology, but itremains unclear what determines the accuracy of such measurements. Using ageneralized version of the previously established sampling framework, weinvestigate how resource limitations and the statistics of the input signal setthe optimal design of a well-characterized network that measures temporalconcentration changes: the Escherichia coli chemotaxis network. Our resultsshow how an optimal adaptation time arises from the trade-off between thesampling error, caused by the stochastic nature of the network, and thedynamical error, caused by uninformative fluctuations in the input. A largerresource availability reduces the sampling error, which allows for a smalleradaptation time, thereby simultaneously decreasing the dynamical error.Similarly, we find that the optimal adaptation time scales inversely with thegradient steepness, because steeper gradients lift the signal above the noiseand reduce the sampling error. These findings shed light on the principles thatgovern the optimal design of the E. coli chemotaxis network specifically, andany system measuring temporal changes more broadly.
要成功驾驭化学梯度,微生物需要预测配体浓度在空间中的变化。由于微生物的体型有限,它们不会对其体长进行空间导数计算,而是进行时间导数计算,将当前信号与近期信号进行比较,即所谓的适应时间。这种策略在生物学中非常普遍,但目前还不清楚是什么决定了这种测量的准确性。利用以前建立的采样框架的广义版本,我们研究了资源限制和输入信号的统计量如何决定了一个特性良好的测量时间浓度变化的网络(大肠杆菌趋化网络)的最佳设计。我们的研究结果表明,最佳适应时间是如何从网络随机性引起的采样误差和输入中无信息波动引起的动态误差之间的权衡中产生的。同样,我们发现最佳适应时间与梯度陡度成反比,因为较陡的梯度能将信号提升到噪声之上并减少采样误差。这些发现揭示了大肠杆菌趋化网络以及任何测量时间变化的系统的优化设计原则。
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引用次数: 0
PhosNetVis: a web-based tool for kinase enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data PhosNetVis:一种基于网络的激酶富集分析和磷酸化蛋白质组学数据交互式二维/三维网络可视化工具
Pub Date : 2024-02-07 DOI: arxiv-2402.05016
Osho Rawal, Berk Turhan, Irene Font Peradejordi, Shreya Chandrasekar, Selim Kalayci, Jeffrey Johnson, Mehdi Bouhaddou, Zeynep H. Gumus
Protein phosphorylation is a vital process in cellular signaling thatinvolves the reversible modification of a protein (substrate) residue byanother protein (kinase). Advances in liquid chromatography-mass spectrometryhave enabled the rapid generation of massive protein phosphorylation datasetsacross multiple conditions by many research groups. Researchers are then taskedwith inferring kinases responsible for changes in phosphorylation sites of eachsubstrate. Despite the recent explosion of tools to infer kinase-substrateinteractions (KSIs) from such datasets, these are not optimized for theinteractive exploration of the resulting large and complex KSI networkstogether with significant phosphorylation sites and states. There are also nodedicated tools that streamline kinase inferences together with interactivevisualizations of the resulting networks. There is thus an unmet need for atool that facilitates uster-intuitive analysis, interactive exploration,visualization, and communication of datasets from phosphoproteomicsexperiments. Here, we present PhosNetVis, a freely available web-based tool forresearchers of all computational skill levels to easily infer, generate andinteractively explore KSI networks in 2D or 3D by streamlining multiplephosphoproteomics data analysis steps within one single tool. PhostNetVissignificantly lowers the barriers for researchers in rapidly generatinghigh-quality visualizations to translate their rich phosphoproteomics datasetsinto biological and clinical insights.
蛋白质磷酸化是细胞信号传导的一个重要过程,它涉及另一种蛋白质(激酶)对蛋白质(底物)残基的可逆修饰。液相色谱-质谱联用技术的进步使许多研究小组能够在多种条件下快速生成大量蛋白质磷酸化数据集。随后,研究人员的任务就是推断负责每个底物磷酸化位点变化的激酶。尽管最近从此类数据集中推断激酶-底物相互作用(KSI)的工具层出不穷,但这些工具并没有针对由此产生的庞大而复杂的 KSI 网络以及重要的磷酸化位点和状态进行交互式探索进行优化。此外,还有一些专用工具可以简化激酶推断,并对由此产生的网络进行交互式可视化。因此,我们需要一种工具来促进直观分析、交互式探索、可视化以及磷酸化蛋白质组实验数据集的交流。在这里,我们介绍了 PhosNetVis,这是一种免费提供的基于网络的工具,通过在一个工具中简化多个磷蛋白组学数据分析步骤,让各种计算技能水平的研究人员都能轻松地推断、生成和交互式地探索二维或三维的 KSI 网络。PhostNetViss 大大降低了研究人员快速生成高质量可视化数据的门槛,从而将丰富的磷蛋白组学数据集转化为生物学和临床见解。
{"title":"PhosNetVis: a web-based tool for kinase enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data","authors":"Osho Rawal, Berk Turhan, Irene Font Peradejordi, Shreya Chandrasekar, Selim Kalayci, Jeffrey Johnson, Mehdi Bouhaddou, Zeynep H. Gumus","doi":"arxiv-2402.05016","DOIUrl":"https://doi.org/arxiv-2402.05016","url":null,"abstract":"Protein phosphorylation is a vital process in cellular signaling that\u0000involves the reversible modification of a protein (substrate) residue by\u0000another protein (kinase). Advances in liquid chromatography-mass spectrometry\u0000have enabled the rapid generation of massive protein phosphorylation datasets\u0000across multiple conditions by many research groups. Researchers are then tasked\u0000with inferring kinases responsible for changes in phosphorylation sites of each\u0000substrate. Despite the recent explosion of tools to infer kinase-substrate\u0000interactions (KSIs) from such datasets, these are not optimized for the\u0000interactive exploration of the resulting large and complex KSI networks\u0000together with significant phosphorylation sites and states. There are also no\u0000dedicated tools that streamline kinase inferences together with interactive\u0000visualizations of the resulting networks. There is thus an unmet need for a\u0000tool that facilitates uster-intuitive analysis, interactive exploration,\u0000visualization, and communication of datasets from phosphoproteomics\u0000experiments. Here, we present PhosNetVis, a freely available web-based tool for\u0000researchers of all computational skill levels to easily infer, generate and\u0000interactively explore KSI networks in 2D or 3D by streamlining multiple\u0000phosphoproteomics data analysis steps within one single tool. PhostNetVis\u0000significantly lowers the barriers for researchers in rapidly generating\u0000high-quality visualizations to translate their rich phosphoproteomics datasets\u0000into biological and clinical insights.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139758489","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}
引用次数: 0
期刊
arXiv - QuanBio - Molecular Networks
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