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Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization 微调癌细胞逻辑模型以预测药物协同作用:将人工整理与自动参数化相结合
Pub Date : 2023-11-20 DOI: 10.3389/fsysb.2023.1252961
Å. Flobak, John Zobolas, Miguel Vazquez, T. S. Steigedal, L. Thommesen, Asle Grislingås, B. Niederdorfer, Evelina Folkesson, Martin Kuiper
Treatment with combinations of drugs carries great promise for personalized therapy for a variety of diseases. We have previously shown that synergistic combinations of cancer signaling inhibitors can be identified based on a logical framework, by manual model definition. We now demonstrate how automated adjustments of model topology and logic equations both can greatly reduce the workload traditionally associated with logical model optimization. Our methodology allows the exploration of larger model ensembles that all obey a set of observations, while being less restrained for parts of the model where parameterization is not guided by biological data. We benchmark the synergy prediction performance of our logical models in a dataset of 153 targeted drug combinations. We show that well-performing manual models faithfully represent measured biomarker data and that their performance can be outmatched by automated parameterization using a genetic algorithm. Whereas the predictive performance of a curated model is strongly affected by simulated curation errors, data-guided deletion of a small subset of regulatory model edges can significantly improve prediction quality. With correct topology we find evidence of some tolerance to simulated errors in the biomarker calibration data, yet performance decreases with reduced data quality. Moreover, we show that predictive logical models are valuable for proposing mechanisms underpinning observed synergies. With our framework we predict the synergy of joint inhibition of PI3K and TAK1, and further substantiate this prediction with observations in cancer cell cultures and in xenograft experiments.
药物组合治疗为多种疾病的个性化治疗带来了巨大希望。我们之前已经证明,通过手动定义模型,可以根据逻辑框架确定癌症信号抑制剂的协同组合。现在,我们展示了自动调整模型拓扑结构和逻辑方程如何大大减少传统逻辑模型优化的工作量。我们的方法允许探索更大的模型集合,这些模型集合都服从一组观察结果,同时对模型中参数设置不受生物数据指导的部分限制较少。我们在一个包含 153 种靶向药物组合的数据集中对逻辑模型的协同预测性能进行了基准测试。我们的研究表明,性能良好的手动模型能忠实地反映测得的生物标记数据,而使用遗传算法进行自动参数化后,其性能可与之媲美。虽然模型的预测性能会受到仿真模型误差的严重影响,但在数据指导下删除一小部分调控模型边缘可以显著提高预测质量。在拓扑结构正确的情况下,我们发现生物标记校准数据对模拟错误有一定的容忍度,但随着数据质量的降低,预测性能也会下降。此外,我们还发现,预测性逻辑模型对于提出观察到的协同作用的基础机制很有价值。利用我们的框架,我们预测了联合抑制 PI3K 和 TAK1 的协同作用,并通过在癌细胞培养和异种移植实验中的观察进一步证实了这一预测。
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引用次数: 0
Editorial: Virtual patients and digital twins in the systems analysis of drug discovery and development 社论:药物发现和开发系统分析中的虚拟病人和数字双胞胎
Pub Date : 2023-09-29 DOI: 10.3389/fsysb.2023.1293076
Chen Zhao, Hua He, Huilin Ma
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引用次数: 0
Editorial: Systems biology, women in science 2021/22: translational systems biology and in silico trials 社论:系统生物学,科学界的女性 2021/22:转化系统生物学和硅学试验
Pub Date : 2023-09-29 DOI: 10.3389/fsysb.2023.1293298
Jane A. Leopold, M. Ganapathiraju, N. Yanamala
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引用次数: 0
Editorial: Use of quantitative systems pharmacology pipelines to bridge in vitro and in vivo results in drug discovery 社论:利用定量系统药理学管道在药物发现的体外和体内结果之间架起桥梁
Pub Date : 2023-09-22 DOI: 10.3389/fsysb.2023.1291610
Federico Reali, Attila Csikász-Nagy, Gianluca Selvaggio
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引用次数: 0
Multilayered safety framework for living diagnostics in the colon 结肠活体诊断的多层安全框架
Pub Date : 2023-09-22 DOI: 10.3389/fsysb.2023.1240040
Sonia Mecacci, Lucía Torregrosa-Barragán, Enrique Asin-Garcia, Robert W. Smith
Introduction: Colorectal cancer is the second most deadly cancer worldwide. Current screening methods have low detection rates and frequently provide false positive results, leading to missed diagnoses or unnecessary colonoscopies. To tackle this issue, the Wageningen UR iGEM team from 2022 developed “Colourectal”, a living diagnostic tool for colorectal cancer. Following a synthetic biology approach, the project used an engineered Escherichia coli Nissle 1917 strain capable of binding to tumour cells that detects two distinct cancer biomarkers, and secretes a coloured protein observable in stool. Due to the utilization of genetically modified bacteria in vivo , precautionary biosafety measures were included within a three level safe-by-design strategy. Results: The first genetic safeguard ensured confinement of the living diagnostic to the colon environment by implementing auxotrophy to mucin that is abundant in the colon lining. For this, a synthetic chimeric receptor was generated to ensure expression of essential genes in the presence of mucin. The second strategy limited the viability of the engineered bacteria to the human body, preventing proliferation in open environments. The use of a temperature sensitive kill switch induced bacterial cell death at temperatures below 37°C. The third biocontainment strategy was installed as an emergency kill switch to stop the Colourectal test at any point. By inducing a highly genotoxic response through CRISPR-Cas-mediated DNA degradation, cell death of E. coli Nissle is triggered. Discussion: While the use of engineered microorganisms in human applications is not yet a reality, the safety considerations of our multi-layered strategy provide a framework for the development of future living diagnostic tools.
导读:结直肠癌是全球第二大致命癌症。目前的筛查方法检出率低,经常出现假阳性结果,导致漏诊或不必要的结肠镜检查。为了解决这个问题,瓦赫宁根大学iGEM团队从2022年开始开发了“结肠直肠”,这是一种结肠直肠癌的活体诊断工具。根据合成生物学方法,该项目使用了一种工程大肠杆菌Nissle 1917菌株,该菌株能够与肿瘤细胞结合,检测两种不同的癌症生物标志物,并在粪便中分泌可观察到的彩色蛋白质。由于在体内使用转基因细菌,预防性生物安全措施包括在三级安全设计策略中。结果:第一个遗传保障通过对结肠内膜中丰富的粘蛋白实施营养不良,确保了对结肠环境的活体诊断。为此,合成的嵌合受体被生成,以确保必需基因在粘蛋白存在下的表达。第二种策略限制了工程细菌在人体中的生存能力,防止在开放环境中增殖。在低于37℃的温度下,使用温度敏感的灭活开关诱导细菌细胞死亡。第三种生物控制策略是作为紧急终止开关安装的,以便在任何时候停止结肠直肠试验。通过crispr - cas介导的DNA降解诱导高度基因毒性反应,触发大肠杆菌尼瑟尔细胞死亡。讨论:虽然工程微生物在人类中的应用尚未成为现实,但我们多层策略的安全性考虑为未来生活诊断工具的发展提供了一个框架。
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引用次数: 0
What’s next for computational systems biology? 计算系统生物学的下一步是什么?
Pub Date : 2023-09-19 DOI: 10.3389/fsysb.2023.1250228
Eberhard O. Voit, Ashti M. Shah, Daniel Olivença, Yoram Vodovotz
Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the next-generation of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis.
几十年前,计算系统生物学在很大程度上还不为人所知,现在它已成为生物学和医学研究的核心方法论。这种惊人的上升提出了一个问题,即社区下一步应该做什么。这篇文章概述了我们对计算系统生物学未来的个人愿景,建议需要解决思维方式和方法。我们通过关注当前和预期的研究目标、强大计算工具的开发、可能的突出应用、下一代科学家的教育以及向公众推广来呈现这一愿景。在我们看来,近年来出现了两类广泛的研究目标,并将指导未来的努力。第一个目标是越来越大和越来越复杂的计算模型,旨在解决新出现的与健康有关的挑战,例如真实的全细胞和器官模型、疾病模拟器和数字双胞胎、计算机临床试验以及治疗药物开发背景下的临床转化应用。这种大型模型还将引导我们找到解决农业和环境可持续性等紧迫问题的办法,包括充足的粮食供应和不断变化的栖息地中的生命。第二个目标是深刻理解自然界解决问题的系统设计和策略的本质。这种理解将有助于我们解释观察到的生物结构,并指导对合成生物系统的探索。在有效的方法方面,我们建议努力实现从原始生物医学数据到时空机制模型的自动化数据管道。这些将得到动态统计方法、机器学习、人工智能和动态模型设计的精简策略的支持,在建模现实的复杂性和抽象的简单性之间取得良好的平衡。最后,我们建议需要协调一致,在社区范围内强调有效的系统生物学教育,作为正式教学和实践指导的结合来实施。教育工作还应通过书籍、博客、社会媒体和互动网络机会向公众推广,最终目标是培训最先进的技术,同时重新找回失去的综合艺术。
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引用次数: 0
Systems biology platform for efficient development and translation of multitargeted therapeutics 系统生物学平台的有效开发和翻译的多靶向治疗
Pub Date : 2023-09-18 DOI: 10.3389/fsysb.2023.1229532
Karim Azer, Irina Leaf
Failure to achieve efficacy is among the top, if not the most common reason for clinical trial failures. While there may be many underlying contributors to these failures, selecting the right mechanistic hypothesis, the right dose, or the right patient population are the main culprits. Systems biology is an inter-disciplinary field at the intersection of biology and mathematics that has the growing potential to increase probability of success in clinical trials, delivering a data-driven matching of the right mechanism to the right patient, at the right dose. Moreover, as part of successful selection of targets for a therapeutic area, systems biology is a prime approach to development of combination therapies to combating complex diseases, where single targets have failed to achieve sufficient efficacy in the clinic. Systems biology approaches have become increasingly powerful with the progress in molecular and computational methods and represent a novel innovative tool to tackle the complex mechanisms of human disease biology, linking it to clinical phenotypes and optimizing multiple steps of drug discovery and development. With increasing ability of probing biology at a cellular and organ level with omics technologies, systems biology is here to stay and is positioned to be one of the key pillars of drug discovery and development, predicting and advancing the best therapies that can be combined together for an optimal pharmacological effect in the clinic. Here we describe a systems biology platform with a stepwise approach that starts with characterization of the key pathways contributing to the Mechanism of Disease (MOD) and is followed by identification, design, optimization, and translation into the clinic of the best therapies that are able to reverse disease-related pathological mechanisms through one or multiple Mechanisms of Action (MOA).
未能达到疗效即使不是临床试验失败最常见的原因,也是最重要的原因之一。虽然可能有许多潜在的因素导致这些失败,但选择正确的机制假设、正确的剂量或正确的患者群体是主要的罪魁祸首。系统生物学是生物学和数学交叉的跨学科领域,在提高临床试验成功概率方面具有越来越大的潜力,在正确的剂量下为正确的患者提供正确的机制匹配数据。此外,作为成功选择治疗领域靶点的一部分,系统生物学是开发联合疗法以对抗复杂疾病的主要方法,其中单一靶点在临床中未能达到足够的疗效。随着分子和计算方法的进步,系统生物学方法变得越来越强大,代表了一种新的创新工具,可以解决人类疾病生物学的复杂机制,将其与临床表型联系起来,并优化药物发现和开发的多个步骤。随着组学技术在细胞和器官水平上探测生物学的能力不断增强,系统生物学将继续存在,并被定位为药物发现和开发的关键支柱之一,预测和推进最佳疗法,这些疗法可以结合在一起,在临床中产生最佳的药理效果。在这里,我们描述了一个系统生物学平台,采用逐步的方法,从对疾病机制(MOD)的关键途径的表征开始,然后是识别、设计、优化和转化为临床的最佳疗法,这些疗法能够通过一种或多种作用机制(MOA)逆转疾病相关的病理机制。
{"title":"Systems biology platform for efficient development and translation of multitargeted therapeutics","authors":"Karim Azer, Irina Leaf","doi":"10.3389/fsysb.2023.1229532","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1229532","url":null,"abstract":"Failure to achieve efficacy is among the top, if not the most common reason for clinical trial failures. While there may be many underlying contributors to these failures, selecting the right mechanistic hypothesis, the right dose, or the right patient population are the main culprits. Systems biology is an inter-disciplinary field at the intersection of biology and mathematics that has the growing potential to increase probability of success in clinical trials, delivering a data-driven matching of the right mechanism to the right patient, at the right dose. Moreover, as part of successful selection of targets for a therapeutic area, systems biology is a prime approach to development of combination therapies to combating complex diseases, where single targets have failed to achieve sufficient efficacy in the clinic. Systems biology approaches have become increasingly powerful with the progress in molecular and computational methods and represent a novel innovative tool to tackle the complex mechanisms of human disease biology, linking it to clinical phenotypes and optimizing multiple steps of drug discovery and development. With increasing ability of probing biology at a cellular and organ level with omics technologies, systems biology is here to stay and is positioned to be one of the key pillars of drug discovery and development, predicting and advancing the best therapies that can be combined together for an optimal pharmacological effect in the clinic. Here we describe a systems biology platform with a stepwise approach that starts with characterization of the key pathways contributing to the Mechanism of Disease (MOD) and is followed by identification, design, optimization, and translation into the clinic of the best therapies that are able to reverse disease-related pathological mechanisms through one or multiple Mechanisms of Action (MOA).","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135207808","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
In-silico modelling of the mitogen-activated protein kinase (MAPK) pathway in colorectal cancer: mutations and targeted therapy 结直肠癌中丝裂原活化蛋白激酶(MAPK)通路的计算机模拟:突变和靶向治疗
Pub Date : 2023-08-23 DOI: 10.3389/fsysb.2023.1207898
Sara Sommariva, S. Berra, Giorgia Biddau, G. Caviglia, F. Benvenuto, Michele Piana
Introduction: Chemical reaction networks (CRNs) are powerful tools for describing the complex nature of cancer’s onset, progression, and therapy. The main reason for their effectiveness is in the fact that these networks can be rather naturally encoded as a dynamical system whose asymptotic solution mimics the proteins' concentration profile at equilibrium.Methods and Results: This paper relies on a complex CRN previously designed for modeling colorectal cells in their G1-S transition phase and presents a mathematical method to investigate global and local effects triggered on the network by partial and complete mutations occurring mainly in its mitogen-activated protein kinase (MAPK) pathway. Further, this same approach allowed the in-silico modeling and dosage of a multi-target therapeutic intervention that utilizes MAPK as its molecular target.Discussion: Overall the results shown in this paper demonstrate how the proposed approach can be exploited as a tool for the in-silico comparison and evaluation of different targeted therapies. Future effort will be devoted to refine the model so to incorporate more biologically sound partial mutations and drug combinations.
简介:化学反应网络(CRN)是描述癌症发病、进展和治疗的复杂性质的强大工具。它们有效的主要原因是,这些网络可以相当自然地编码为一个动态系统,其渐近解模拟了平衡时蛋白质的浓度分布。方法和结果:本文依赖于先前设计的用于模拟G1-S过渡期结直肠癌细胞的复杂CRN,并提出了一种数学方法来研究主要发生在其丝裂原活化蛋白激酶(MAPK)途径中的部分和完全突变对网络触发的全局和局部影响。此外,这种相同的方法允许利用MAPK作为其分子靶标的多靶点治疗干预的计算机建模和剂量。讨论:总的来说,本文中显示的结果表明,所提出的方法可以作为不同靶向治疗的计算机比较和评估工具。未来的工作将致力于完善该模型,以纳入更具生物学意义的部分突变和药物组合。
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引用次数: 0
A microbiota-epigenetic circuit controls systematic circadian programs in the gut epithelium 微生物群表观遗传回路控制肠道上皮的系统昼夜节律程序
Pub Date : 2023-08-08 DOI: 10.3389/fsysb.2023.1175306
Junjie Ma, Jianglin Zhang, Zheng Kuang
The intestinal microbiota is an important factor that regulates mammalian circadian rhythms and health. We previously reported that the microbiota synchronizes lipid uptake and metabolism in the intestinal epithelium through histone deacetylase 3 (HDAC3). However, the breadth and significance of microbiota-circadian crosstalk in the intestine are not well understood. Here, we show that the gut microbiota programs the rhythmic expression of a broad range of biological processes, and temporally orchestrates epithelial functions and physiology in accordance with the rhythmic gut environment. Protein synthesis, cell proliferation, and metabolic and immune activities are differentially expressed in the daytime and nighttime respectively, indicating a daily alternation of “working” and “recharging” themes in the gut. The rhythms of gene expression are dampened or altered in germ-free mice, suggesting that the microbiota helps to structure the timing of host gene expression. Further analysis showed that HDAC3 drives a vast majority of these microbiota-dependent circadian programs, likely through rhythmic deacetylation of histones. Motif enrichment analysis revealed that HDAC3 could differentially control distinct rhythmic pathways, most likely by recruiting different transcription factors. These findings provide a systematic view of how the commensal microbiota exploits an epigenetic factor to program just-in-time functions in the intestinal epithelium and maintain host homeostasis.
肠道微生物群是调节哺乳动物昼夜节律和健康的重要因素。我们之前报道过,微生物群通过组蛋白脱乙酰酶3(HDAC3)同步肠上皮中的脂质摄取和代谢。然而,肠道中微生物群昼夜节律串扰的广度和意义尚不清楚。在这里,我们发现肠道微生物群对一系列生物过程的节律性表达进行编程,并根据节律性肠道环境在时间上协调上皮功能和生理学。蛋白质合成、细胞增殖、代谢和免疫活性分别在白天和晚上有不同的表达,这表明肠道中“工作”和“充电”主题每天都在交替。无菌小鼠的基因表达节奏受到抑制或改变,这表明微生物群有助于构建宿主基因表达的时间。进一步的分析表明,HDAC3可能通过组蛋白的节律性脱乙酰化来驱动这些微生物群依赖性昼夜节律程序的绝大多数。Motif富集分析显示,HDAC3可以不同地控制不同的节律途径,很可能是通过募集不同的转录因子。这些发现为共生微生物群如何利用表观遗传因子来编程肠上皮中的及时功能并维持宿主稳态提供了一个系统的视角。
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引用次数: 0
Generating synthetic multidimensional molecular time series data for machine learning: considerations 生成用于机器学习的合成多维分子时间序列数据:注意事项
Pub Date : 2023-07-25 DOI: 10.3389/fsysb.2023.1188009
G. An, Chase Cockrell
The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time series data for AI tasks where it is necessary to know how the system works. This is most pronounced in the ability to generate synthetic multi-dimensional molecular time series data (subsequently referred to as synthetic mediator trajectories or SMTs); this is the type of data that underpins research into biomarkers and mediator signatures for forecasting various diseases and is an essential component of the drug development pipeline. We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the inability to use ab initio simulations due to the state of perpetual epistemic incompleteness in cellular/molecular biology. Alternatively, we present a rationale for using complex multi-scale mechanism-based simulation models, constructed and operated on to account for perpetual epistemic incompleteness and the need to provide maximal expansiveness in concordance with the Maximal Entropy Principle. These procedures provide for the generation of SMT that minimizes the known shortcomings associated with neural network AI systems, namely overfitting and lack of generalizability. The generation of synthetic data that accounts for the identified factors of multi-dimensional time series data is an essential capability for the development of mediator-biomarker based AI forecasting systems, and therapeutic control development and optimization.
合成数据的使用被认为是开发基于神经网络的人工智能(AI)系统的关键一步。虽然为其他领域的人工智能应用生成合成数据的方法在某些生物医学人工智能系统中发挥着作用,主要与图像处理有关,但在为人工智能任务生成时间序列数据方面存在关键差距,需要了解系统是如何工作的。这在生成合成多维分子时间序列数据(随后称为合成介质轨迹或SMT)的能力方面最为明显;这类数据是预测各种疾病的生物标志物和介体特征研究的基础,也是药物开发管道的重要组成部分。我们认为,生成这类合成数据的统计和以数据为中心的机器学习(ML)方法的不足是由于多种因素的结合:维度诅咒导致的永久数据稀疏性、中心极限定理在对这类数据的统计分布进行假设方面的不适用性,以及由于细胞/分子生物学中永久的认识不完全状态而无法使用从头算模拟。或者,我们提出了使用基于复杂多尺度机制的模拟模型的基本原理,这些模型是为了解释永久的认识不完全性和根据最大熵原理提供最大扩展性的需要而构建和操作的。这些程序提供了SMT的生成,最大限度地减少了与神经网络AI系统相关的已知缺点,即过拟合和缺乏可推广性。生成解释多维时间序列数据的已识别因素的合成数据是开发基于中介生物标志物的人工智能预测系统以及开发和优化治疗控制的重要能力。
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引用次数: 0
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Frontiers in systems biology
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