Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.
{"title":"Cell reprogramming design by transfer learning of functional transcriptional networks","authors":"Thomas P. Wytock, Adilson E. Motter","doi":"arxiv-2403.04837","DOIUrl":"https://doi.org/arxiv-2403.04837","url":null,"abstract":"Recent developments in synthetic biology, next-generation sequencing, and\u0000machine learning provide an unprecedented opportunity to rationally design new\u0000disease treatments based on measured responses to gene perturbations and drugs\u0000to reprogram cells. The main challenges to seizing this opportunity are the\u0000incomplete knowledge of the cellular network and the combinatorial explosion of\u0000possible interventions, both of which are insurmountable by experiments. To\u0000address these challenges, we develop a transfer learning approach to control\u0000cell behavior that is pre-trained on transcriptomic data associated with human\u0000cell fates, thereby generating a model of the network dynamics that can be\u0000transferred to specific reprogramming goals. The approach combines\u0000transcriptional responses to gene perturbations to minimize the difference\u0000between a given pair of initial and target transcriptional states. We\u0000demonstrate our approach's versatility by applying it to a microarray dataset\u0000comprising >9,000 microarrays across 54 cell types and 227 unique\u0000perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs\u0000across 36 cell types and 138 perturbations. Our approach reproduces known\u0000reprogramming protocols with an AUROC of 0.91 while innovating over existing\u0000methods by pre-training an adaptable model that can be tailored to specific\u0000reprogramming transitions. We show that the number of gene perturbations\u0000required to steer from one fate to another increases with decreasing\u0000developmental relatedness and that fewer genes are needed to progress along\u0000developmental paths than to regress. These findings establish a\u0000proof-of-concept for our approach to computationally design control strategies\u0000and provide insights into how gene regulatory networks govern phenotype.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140097557","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}
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 physical picture, yet the fitting parameters can provide information about biochemical reactions, such as the number of transcription factors (TFs) and the binding energy between regulatory elements. However, it remains unclear when and how much biochemical information can Hill function provide in addition to fitting. Here, started from the interactions between TFs and RNA polymerase during transcription regulation and both of their association-dissociation reactions at specific/nonspecific sites on DNA, the regulatory effect of TFs was deduced as fold change. We found that, for weak promoter, fold change can degrade into the regulatory factor (Freg) which is closely correlated with Hill function. By directly comparing and fitting with Hill function, the fitting parameters and corresponding biochemical reaction parameters in Freg were analyzed and discussed, where the single TF and multiple TFs that with cooperativity and basic logic effects were considered. We concluded the strength of promoter and interactions between TFs determine whether Hill function can reflect the corresponding biochemical information. Our findings highlight the role of Hill function in modeling/fitting for transcriptional regulation, which also benefits the preparation of synthetic regulatory elements.
{"title":"Hill Function-based Model of Transcriptional Response: Impact of Nonspecific Binding and RNAP Interactions","authors":"Wenjia Shi, Yao Ma, Peilin Hu, Mi Pang, Xiaona Huang, Yiting Dang, Yuxin Xie, Danni Wu","doi":"arxiv-2403.01702","DOIUrl":"https://doi.org/arxiv-2403.01702","url":null,"abstract":"Hill function is one of the widely used gene transcription regulation models.\u0000Its attribute of fitting may result in a lack of an underlying physical\u0000picture, yet the fitting parameters can provide information about biochemical\u0000reactions, such as the number of transcription factors (TFs) and the binding\u0000energy between regulatory elements. However, it remains unclear when and how\u0000much biochemical information can Hill function provide in addition to fitting.\u0000Here, started from the interactions between TFs and RNA polymerase during\u0000transcription regulation and both of their association-dissociation reactions\u0000at specific/nonspecific sites on DNA, the regulatory effect of TFs was deduced\u0000as fold change. We found that, for weak promoter, fold change can degrade into\u0000the regulatory factor (Freg) which is closely correlated with Hill function. By\u0000directly comparing and fitting with Hill function, the fitting parameters and\u0000corresponding biochemical reaction parameters in Freg were analyzed and\u0000discussed, where the single TF and multiple TFs that with cooperativity and\u0000basic logic effects were considered. We concluded the strength of promoter and\u0000interactions between TFs determine whether Hill function can reflect the\u0000corresponding biochemical information. Our findings highlight the role of Hill\u0000function in modeling/fitting for transcriptional regulation, which also\u0000benefits the preparation of synthetic regulatory elements.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034494","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}
Proper vertebrae formation relies on a tissue-wide oscillator called the segmentation clock. Individual cellular oscillators in the presomitic mesoderm are modulated by intercellular coupling and external signals, leading to the propagation of oscillatory waves of genetic expression eventually stabilizing into a static pattern of genetic expression. Here, we review 4 decades of biophysical models of this process, starting from the pioneering Clock and Wavefront model by Cooke and Zeeman, and the reaction-diffusion model by Meinhardt. We discuss how modern descriptions followed advances in molecular description and visualization of the process, reviewing phase models, delayed models, systems-level, and finally geometric models. We connect models to high-level aspects of embryonic development from embryonic scaling to wave propagation, up to reconstructed stem cell systems. We provide new analytical calculations and insights into classical and recent models, leading us to propose a geometric description of somitogenesis organized along two primary waves of differentiation.
{"title":"Waves, patterns and bifurcations: a tutorial review on the vertebrate segmentation clock","authors":"Paul François, Victoria Mochulska","doi":"arxiv-2403.00457","DOIUrl":"https://doi.org/arxiv-2403.00457","url":null,"abstract":"Proper vertebrae formation relies on a tissue-wide oscillator called the\u0000segmentation clock. Individual cellular oscillators in the presomitic mesoderm\u0000are modulated by intercellular coupling and external signals, leading to the\u0000propagation of oscillatory waves of genetic expression eventually stabilizing\u0000into a static pattern of genetic expression. Here, we review 4 decades of\u0000biophysical models of this process, starting from the pioneering Clock and\u0000Wavefront model by Cooke and Zeeman, and the reaction-diffusion model by\u0000Meinhardt. We discuss how modern descriptions followed advances in molecular\u0000description and visualization of the process, reviewing phase models, delayed\u0000models, systems-level, and finally geometric models. We connect models to\u0000high-level aspects of embryonic development from embryonic scaling to wave\u0000propagation, up to reconstructed stem cell systems. We provide new analytical\u0000calculations and insights into classical and recent models, leading us to\u0000propose a geometric description of somitogenesis organized along two primary\u0000waves of differentiation.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025801","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}
Previous work in Boolean dynamical networks has suggested that the number of components that must be controlled to select an existing attractor is typically set by the number of attractors admitted by the dynamics, with no dependence on the size of the network. Here we study the rare cases of networks that defy this expectation, with attractors that require controlling most nodes. We find empirically that unstable fixed points are the primary recurring characteristic of networks that prove more difficult to control. We describe an efficient way to identify unstable fixed points and show that, in both existing biological models and ensembles of random dynamics, we can better explain the variance of control kernel sizes by incorporating the prevalence of unstable fixed points. In the end, the fact that these exceptions are associated with dynamics that are unstable to small perturbations hints that they are likely an artifact of using deterministic models. These exceptions are likely to be biologically irrelevant, supporting the generality of easy controllability in biological networks.
{"title":"Difficult control is related to instability in biologically inspired Boolean networks","authors":"Bryan C. Daniels, Enrico Borriello","doi":"arxiv-2402.18757","DOIUrl":"https://doi.org/arxiv-2402.18757","url":null,"abstract":"Previous work in Boolean dynamical networks has suggested that the number of\u0000components that must be controlled to select an existing attractor is typically\u0000set by the number of attractors admitted by the dynamics, with no dependence on\u0000the size of the network. Here we study the rare cases of networks that defy\u0000this expectation, with attractors that require controlling most nodes. We find\u0000empirically that unstable fixed points are the primary recurring characteristic\u0000of networks that prove more difficult to control. We describe an efficient way\u0000to identify unstable fixed points and show that, in both existing biological\u0000models and ensembles of random dynamics, we can better explain the variance of\u0000control kernel sizes by incorporating the prevalence of unstable fixed points.\u0000In the end, the fact that these exceptions are associated with dynamics that\u0000are unstable to small perturbations hints that they are likely an artifact of\u0000using deterministic models. These exceptions are likely to be biologically\u0000irrelevant, supporting the generality of easy controllability in biological\u0000networks.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003441","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}
Molecular Communication (MC) architectures suffer from molecular build-up in the channel if they do not have appropriate reuptake mechanisms. The molecular build-up either leads to intersymbol interference (ISI) or reduces the transmission rate. To measure the molecular build-up, we derive analytic expressions for the incidence rate and absorption rate for one-dimensional MC channels where molecular dispersion obeys the Brownian Motion. We verify each of our key results with Monte Carlo simulations. Our results contribute to the development of more complicated models and analytic expressions to measure the molecular 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。
{"title":"Modelling 1D Partially Absorbing Boundaries for Brownian Molecular Communication Channels","authors":"Caglar Koca, Ozgur B. Akan","doi":"arxiv-2402.15888","DOIUrl":"https://doi.org/arxiv-2402.15888","url":null,"abstract":"Molecular Communication (MC) architectures suffer from molecular build-up in\u0000the channel if they do not have appropriate reuptake mechanisms. The molecular\u0000build-up either leads to intersymbol interference (ISI) or reduces the\u0000transmission rate. To measure the molecular build-up, we derive analytic\u0000expressions for the incidence rate and absorption rate for one-dimensional MC\u0000channels where molecular dispersion obeys the Brownian Motion. We verify each\u0000of our key results with Monte Carlo simulations. Our results contribute to the\u0000development of more complicated models and analytic expressions to measure the\u0000molecular build-up and the impact of ISI in MC.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139978231","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}
Chemical reaction network theory is a powerful framework to describe and analyze chemical systems. While much about the concentration profile in an equilibrium state can be determined in terms of the graph structure, the overall reaction's time evolution depends on the network's kinetic rate function. In this article, we consider the problem of the effective kinetics of a chemical reaction network regarded as a conversion system from the feeding species to products. We define the notion of effective kinetics as a partial solution of a system of non-autonomous ordinary differential equations determined from a chemical reaction network. Examples of actual calculations include the Michaelis-Menten mechanism, for which it is confirmed that our notion of effective kinetics yields the classical formula. Further, we introduce the notion of straight-line solutions of non-autonomous ordinary differential equations to formalize the situation where a well-defined reaction rate exists and consider its relation with the quasi-stationary state approximation used in microkinetics. Our considerations here give a unified framework to formulate the reaction rate of chemical reaction networks.
{"title":"Effective Kinetics of Chemical Reaction Networks","authors":"Tomoharu Suda","doi":"arxiv-2402.11762","DOIUrl":"https://doi.org/arxiv-2402.11762","url":null,"abstract":"Chemical reaction network theory is a powerful framework to describe and\u0000analyze chemical systems. While much about the concentration profile in an\u0000equilibrium state can be determined in terms of the graph structure, the\u0000overall reaction's time evolution depends on the network's kinetic rate\u0000function. In this article, we consider the problem of the effective kinetics of\u0000a chemical reaction network regarded as a conversion system from the feeding\u0000species to products. We define the notion of effective kinetics as a partial\u0000solution of a system of non-autonomous ordinary differential equations\u0000determined from a chemical reaction network. Examples of actual calculations\u0000include the Michaelis-Menten mechanism, for which it is confirmed that our\u0000notion of effective kinetics yields the classical formula. Further, we\u0000introduce the notion of straight-line solutions of non-autonomous ordinary\u0000differential equations to formalize the situation where a well-defined reaction\u0000rate exists and consider its relation with the quasi-stationary state\u0000approximation used in microkinetics. Our considerations here give a unified\u0000framework to formulate the reaction rate of chemical reaction networks.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139927771","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}
Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated in biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we show that approximability is strongly dependent on the dynamical robustness of a network, and that increased canalization in biological networks can almost completely explain their recently postulated high approximability.
{"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":null,"pages":null},"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}
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 (step CRNs), motivated by the step-wise addition of materials in standard lab procedures. Step CRNs have ordered reactants that transform into products via reaction rules over a series of steps. We study an important subset of weak reaction rules, emph{void} rules, in which chemical species may only be deleted but never changed. We demonstrate the capabilities of these simple limited systems to simulate threshold circuits and compute functions using various configurations of rule sizes and step constructions, and prove that without steps, void rules are incapable of these computations, which further motivates the step model. Additionally, we prove the coNP-completeness of verifying if a given step CRN computes a function, holding even for $O(1)$ step systems.
{"title":"Computing Threshold Circuits with Void Reactions in Step Chemical Reaction Networks","authors":"Rachel Anderson, Alberto Avila, Bin Fu, Timothy Gomez, Elise Grizzell, Aiden Massie, Gourab Mukhopadhyay, Adrian Salinas, Robert Schweller, Evan Tomai, Tim Wylie","doi":"arxiv-2402.08220","DOIUrl":"https://doi.org/arxiv-2402.08220","url":null,"abstract":"We introduce a new model of emph{step} Chemical Reaction Networks (step\u0000CRNs), motivated by the step-wise addition of materials in standard lab\u0000procedures. Step CRNs have ordered reactants that transform into products via\u0000reaction rules over a series of steps. We study an important subset of weak\u0000reaction rules, emph{void} rules, in which chemical species may only be\u0000deleted but never changed. We demonstrate the capabilities of these simple\u0000limited systems to simulate threshold circuits and compute functions using\u0000various configurations of rule sizes and step constructions, and prove that\u0000without steps, void rules are incapable of these computations, which further\u0000motivates the step model. Additionally, we prove the coNP-completeness of\u0000verifying if a given step CRN computes a function, holding even for $O(1)$ step\u0000systems.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139758405","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}
To successfully navigate chemical gradients, microorganisms need to predict how the ligand concentration changes in space. Due to their limited size, they do not take a spatial derivative over their body length but rather a temporal derivative, comparing the current signal with that in the recent past, over the so-called adaptation time. This strategy is pervasive in biology, but it remains unclear what determines the accuracy of such measurements. Using a generalized version of the previously established sampling framework, we investigate how resource limitations and the statistics of the input signal set the optimal design of a well-characterized network that measures temporal concentration changes: the Escherichia coli chemotaxis network. Our results show how an optimal adaptation time arises from the trade-off between the sampling error, caused by the stochastic nature of the network, and the dynamical error, caused by uninformative fluctuations in the input. A larger resource availability reduces the sampling error, which allows for a smaller adaptation time, thereby simultaneously decreasing the dynamical error. Similarly, we find that the optimal adaptation time scales inversely with the gradient steepness, because steeper gradients lift the signal above the noise and reduce the sampling error. These findings shed light on the principles that govern the optimal design of the E. coli chemotaxis network specifically, and any system measuring temporal changes more broadly.
{"title":"Predicting concentration changes via discrete sampling","authors":"Age J. Tjalma, Pieter Rein ten Wolde","doi":"arxiv-2402.05825","DOIUrl":"https://doi.org/arxiv-2402.05825","url":null,"abstract":"To successfully navigate chemical gradients, microorganisms need to predict\u0000how the ligand concentration changes in space. Due to their limited size, they\u0000do not take a spatial derivative over their body length but rather a temporal\u0000derivative, comparing the current signal with that in the recent past, over the\u0000so-called adaptation time. This strategy is pervasive in biology, but it\u0000remains unclear what determines the accuracy of such measurements. Using a\u0000generalized version of the previously established sampling framework, we\u0000investigate how resource limitations and the statistics of the input signal set\u0000the optimal design of a well-characterized network that measures temporal\u0000concentration changes: the Escherichia coli chemotaxis network. Our results\u0000show how an optimal adaptation time arises from the trade-off between the\u0000sampling error, caused by the stochastic nature of the network, and the\u0000dynamical error, caused by uninformative fluctuations in the input. A larger\u0000resource availability reduces the sampling error, which allows for a smaller\u0000adaptation time, thereby simultaneously decreasing the dynamical error.\u0000Similarly, we find that the optimal adaptation time scales inversely with the\u0000gradient steepness, because steeper gradients lift the signal above the noise\u0000and reduce the sampling error. These findings shed light on the principles that\u0000govern the optimal design of the E. coli chemotaxis network specifically, and\u0000any system measuring temporal changes more broadly.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139758443","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}
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 that involves the reversible modification of a protein (substrate) residue by another protein (kinase). Advances in liquid chromatography-mass spectrometry have enabled the rapid generation of massive protein phosphorylation datasets across multiple conditions by many research groups. Researchers are then tasked with inferring kinases responsible for changes in phosphorylation sites of each substrate. Despite the recent explosion of tools to infer kinase-substrate interactions (KSIs) from such datasets, these are not optimized for the interactive exploration of the resulting large and complex KSI networks together with significant phosphorylation sites and states. There are also no dedicated tools that streamline kinase inferences together with interactive visualizations of the resulting networks. There is thus an unmet need for a tool that facilitates uster-intuitive analysis, interactive exploration, visualization, and communication of datasets from phosphoproteomics experiments. Here, we present PhosNetVis, a freely available web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining multiple phosphoproteomics data analysis steps within one single tool. PhostNetVis significantly lowers the barriers for researchers in rapidly generating high-quality visualizations to translate their rich phosphoproteomics datasets into biological and clinical insights.
{"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":null,"pages":null},"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}
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