Frontiers in systems biology最新文献

{"title":"Editorial: Systems biology, women in science 2021/22: translational systems biology and in silico trials","authors":"Jane A. Leopold, M. Ganapathiraju, N. Yanamala","doi":"10.3389/fsysb.2023.1293298","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1293298","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139334084","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}

{"title":"Editorial: Use of quantitative systems pharmacology pipelines to bridge in vitro and in vivo results in drug discovery","authors":"Federico Reali, Attila Csikász-Nagy, Gianluca Selvaggio","doi":"10.3389/fsysb.2023.1291610","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1291610","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139337538","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}

{"title":"Multilayered safety framework for living diagnostics in the colon","authors":"Sonia Mecacci, Lucía Torregrosa-Barragán, Enrique Asin-Garcia, Robert W. Smith","doi":"10.3389/fsysb.2023.1240040","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1240040","url":null,"abstract":"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.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061096","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}

{"title":"What’s next for computational systems biology?","authors":"Eberhard O. Voit, Ashti M. Shah, Daniel Olivença, Yoram Vodovotz","doi":"10.3389/fsysb.2023.1250228","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1250228","url":null,"abstract":"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.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135063687","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}

{"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}

{"title":"<i>In-silico</i> modelling of the mitogen-activated protein kinase (MAPK) pathway in colorectal cancer: mutations and targeted therapy.","authors":"Sara Sommariva, Silvia Berra, Giorgia Biddau, Giacomo Caviglia, Federico Benvenuto, Michele Piana","doi":"10.3389/fsysb.2023.1207898","DOIUrl":"10.3389/fsysb.2023.1207898","url":null,"abstract":"<p><p><b>Introduction:</b> 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. <b>Methods and Results:</b> 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 <i>in-silico</i> modeling and dosage of a multi-target therapeutic intervention that utilizes MAPK as its molecular target. <b>Discussion:</b> 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.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":"1207898"},"PeriodicalIF":2.3,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45941414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating synthetic multidimensional molecular time series data for machine learning: considerations.","authors":"Gary An, Chase Cockrell","doi":"10.3389/fsysb.2023.1188009","DOIUrl":"10.3389/fsysb.2023.1188009","url":null,"abstract":"<p><p>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 <i>ab initio</i> 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.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"1 1","pages":"1188009"},"PeriodicalIF":2.3,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43775027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contribution of farms to the microbiota in the swine value chain.","authors":"Pascal Laforge, Antony T Vincent, Caroline Duchaine, Perrine Feutry, Annick Dion-Fortier, Pier-Luc Plante, Éric Pouliot, Sylvain Fournaise, Linda Saucier","doi":"10.3389/fsysb.2023.1183868","DOIUrl":"10.3389/fsysb.2023.1183868","url":null,"abstract":"<p><p><b>Introduction:</b> A thorough understanding of the microbial ecology within the swine value chain is essential to develop new strategies to optimize the microbiological quality of pork products. To our knowledge, no study to date has followed the microbiota through the value chain from live farm animals to the cuts of meat obtained for market. The objective of this study is to evaluate how the microbiota of pigs and their environment influence the microbial composition of samples collected throughout the value chain, including the meat plant and meat cuts. <b>Method and results:</b> Results from 16S rDNA sequencing, short-chain fatty acid concentrations and metabolomic analysis of pig feces revealed that the microbiota from two farms with differing sanitary statuses were distinctive. The total aerobic mesophilic bacteria and <i>Enterobacteriaceae</i> counts from samples collected at the meat plant after the pre-operation cleaning and disinfection steps were at or around the detection limit and the pigs from the selected farms were the first to be slaughtered on each shipment days. The bacterial counts of individual samples collected at the meat plant did not vary significantly between the farms. Alpha diversity results indicate that as we move through the steps in the value chain, there is a clear reduction in the diversity of the microbiota. A beta diversity analysis revealed a more distinct microbiota at the farms compared to the meat plant which change and became more uniform as samples were taken towards the end of the value chain. The source tracker analysis showed that only 12.92% of the microbiota in shoulder samples originated from the farms and 81% of the bacteria detected on the dressed carcasses were of unknown origin. <b>Discussion:</b> Overall, the results suggest that with the current level of microbial control at farms, it is possible to obtain pork products with similar microbiological quality from different farms. However, broader studies are required to determine the impact of the sanitary status of the herd on the final products.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":"1183868"},"PeriodicalIF":2.3,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48725784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules.","authors":"Panteleimon D Mavroudis, Donato Teutonico, Alexandra Abos, Nikhil Pillai","doi":"10.3389/fsysb.2023.1180948","DOIUrl":"10.3389/fsysb.2023.1180948","url":null,"abstract":"<p><p>Prediction of a new molecule's exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using <i>in vivo</i>, or <i>in vitro</i> clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underscores the necessity of investigating the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims at earlier exposure prediction in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":"1180948"},"PeriodicalIF":2.3,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44324832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A practical guide for the generation of model-based virtual clinical trials.","authors":"Morgan Craig, Jana L Gevertz, Irina Kareva, Kathleen P Wilkie","doi":"10.3389/fsysb.2023.1174647","DOIUrl":"10.3389/fsysb.2023.1174647","url":null,"abstract":"<p><p>Mathematical modeling has made significant contributions to drug design, development, and optimization. Virtual clinical trials that integrate mathematical models to explore patient heterogeneity and its impact on a variety of therapeutic questions have recently risen in popularity. Here, we outline best practices for creating virtual patients from mathematical models to ultimately implement and execute a virtual clinical trial. In this practical guide, we discuss and provide examples of model design, parameter estimation, parameter sensitivity, model identifiability, and virtual patient cohort creation. Our goal is to help researchers adopt these approaches to further the use of virtual population-based analysis and virtual clinical trials.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":"1174647"},"PeriodicalIF":2.3,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43841623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}