Pub Date : 2024-08-11DOI: 10.1101/2024.08.11.607483
Firnaaz Ahamed, James C. Stegen, Emily B. Graham, Timothy D. Scheibe, Hyun-Seob Song
Microbial priming, characterized by significant changes in organic matter (OM) decomposition rates due to minor external treatments with the addition of labile OM, exerts a significant impact on biogeochemical cycles in ecosystems. Priming can take many forms, including positive priming (increased OM decomposition rates), negative priming (decreased OM decomposition rates), and everything in between. Currently, we lack generalizable frameworks that can mechanistically explain these diverse patterns of priming, making it challenging to identify its governing factors. In this work, we theorized priming to result from a biogeochemical feedback loop regulated by microorganisms optimizing the balance between cost and benefit towards maximizing their growth rates, i.e., the cost of exoenzyme synthesis for decomposing complex OM and the benefits of energy acquisition from microbial growth on labile OM. Accordingly, we examined the impacts of microbial growth traits and interactions on priming employing a cybernetic approach, which specializes in predicting complex microbial growth patterns through a regulatory feedback loop. Using the cybernetic model, we simulated the occurrence of priming driven by microorganisms in the following four distinct settings: copiotrophic degraders independently, oligotrophic degraders independently, a consortium of copiotrophic degraders and oligotrophic non-degraders, and a consortium of oligotrophic degraders and copiotrophic non-degraders. Comprehensive Monte Carlo simulations using these four models revealed several critical aspects of priming, including: (1) positive priming is a dominant phenomenon in general, while negative priming can also occur sporadically under specific parameter settings, (2) positive priming is more frequently observed in microbial systems with copiotrophic degraders than with oligotrophic degraders, (3) the presence of copiotrophic non-degraders suppresses positive priming, while the presence of oligotrophic non-degraders promotes positive priming, and (4) the evolution of priming over time is also influenced by microbial growth traits and interactions. Most strikingly, all four models predicted a dramatic positive priming effect triggered by the addition of a small amount (i.e., less than 10%) of labile organic matter, with no notable changes observed beyond this point. Together with other findings summarized above, this might represent a key feature of microbial priming that might be commonly observed across microbial systems with diverse growth traits as supported by literature data. Overall, this work combining new theories and models significantly enhances our understanding of priming by providing model-generated and empirically-testable hypotheses on the mechanisms governing priming.
{"title":"Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming","authors":"Firnaaz Ahamed, James C. Stegen, Emily B. Graham, Timothy D. Scheibe, Hyun-Seob Song","doi":"10.1101/2024.08.11.607483","DOIUrl":"https://doi.org/10.1101/2024.08.11.607483","url":null,"abstract":"Microbial priming, characterized by significant changes in organic matter (OM) decomposition rates due to minor external treatments with the addition of labile OM, exerts a significant impact on biogeochemical cycles in ecosystems. Priming can take many forms, including positive priming (increased OM decomposition rates), negative priming (decreased OM decomposition rates), and everything in between. Currently, we lack generalizable frameworks that can mechanistically explain these diverse patterns of priming, making it challenging to identify its governing factors. In this work, we theorized priming to result from a biogeochemical feedback loop regulated by microorganisms optimizing the balance between cost and benefit towards maximizing their growth rates, i.e., the cost of exoenzyme synthesis for decomposing complex OM and the benefits of energy acquisition from microbial growth on labile OM. Accordingly, we examined the impacts of microbial growth traits and interactions on priming employing a cybernetic approach, which specializes in predicting complex microbial growth patterns through a regulatory feedback loop. Using the cybernetic model, we simulated the occurrence of priming driven by microorganisms in the following four distinct settings: copiotrophic degraders independently, oligotrophic degraders independently, a consortium of copiotrophic degraders and oligotrophic non-degraders, and a consortium of oligotrophic degraders and copiotrophic non-degraders. Comprehensive Monte Carlo simulations using these four models revealed several critical aspects of priming, including: (1) positive priming is a dominant phenomenon in general, while negative priming can also occur sporadically under specific parameter settings, (2) positive priming is more frequently observed in microbial systems with copiotrophic degraders than with oligotrophic degraders, (3) the presence of copiotrophic non-degraders suppresses positive priming, while the presence of oligotrophic non-degraders promotes positive priming, and (4) the evolution of priming over time is also influenced by microbial growth traits and interactions. Most strikingly, all four models predicted a dramatic positive priming effect triggered by the addition of a small amount (i.e., less than 10%) of labile organic matter, with no notable changes observed beyond this point. Together with other findings summarized above, this might represent a key feature of microbial priming that might be commonly observed across microbial systems with diverse growth traits as supported by literature data. Overall, this work combining new theories and models significantly enhances our understanding of priming by providing model-generated and empirically-testable hypotheses on the mechanisms governing priming.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945385","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}
Pub Date : 2024-08-10DOI: 10.1101/2024.08.09.607289
Neha Khetan, Binyamin Zuckerman, Giuliana P Calia, Xinyue Chen, Ximena Garcia Arceo, Leor S Weinberger
Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for ~90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule-amplifying noise without altering mean-expression levels-which could enable investigations of the physiological impacts of transcriptional noise.
{"title":"Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise","authors":"Neha Khetan, Binyamin Zuckerman, Giuliana P Calia, Xinyue Chen, Ximena Garcia Arceo, Leor S Weinberger","doi":"10.1101/2024.08.09.607289","DOIUrl":"https://doi.org/10.1101/2024.08.09.607289","url":null,"abstract":"Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for ~90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule-amplifying noise without altering mean-expression levels-which could enable investigations of the physiological impacts of transcriptional noise.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945431","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}
Pub Date : 2024-08-09DOI: 10.1101/2024.08.07.607025
Erik D Huckvale, Hunter N.B. Moseley
Metabolism is the network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic knowledgebases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) contain metabolites, reactions, and pathway annotations; however, such resources are incomplete due to current limits of metabolic knowledge. To fill in missing metabolite pathway annotations, past machine learning models showed some success at predicting KEGG Level 2 pathway category involvement of metabolites based on their chemical structure. Here, we present the first machine learning model to predict metabolite association to more granular KEGG Level 3 metabolic pathways. We used a feature and dataset engineering approach to generate over one million metabolite-pathway entries in the dataset used to train a single binary classifier. This approach produced a mean Matthews correlation coefficient (MCC) of 0.806 +/- 0.017 SD across 100 cross-validations iterations. The 172 Level 3 pathways were predicted with an overall MCC of 0.726. Moreover, metabolite association with the 12 Level 2 pathway categories were predicted with an overall MCC of 0.891, representing significant transfer learning from the Level 3 pathway entries. These are the best metabolite-pathway prediction results published so far in the field.
{"title":"Predicting the Pathway Involvement of Metabolites in Both Pathway Categories and Individual Pathways","authors":"Erik D Huckvale, Hunter N.B. Moseley","doi":"10.1101/2024.08.07.607025","DOIUrl":"https://doi.org/10.1101/2024.08.07.607025","url":null,"abstract":"Metabolism is the network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic knowledgebases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) contain metabolites, reactions, and pathway annotations; however, such resources are incomplete due to current limits of metabolic knowledge. To fill in missing metabolite pathway annotations, past machine learning models showed some success at predicting KEGG Level 2 pathway category involvement of metabolites based on their chemical structure. Here, we present the first machine learning model to predict metabolite association to more granular KEGG Level 3 metabolic pathways. We used a feature and dataset engineering approach to generate over one million metabolite-pathway entries in the dataset used to train a single binary classifier. This approach produced a mean Matthews correlation coefficient (MCC) of 0.806 +/- 0.017 SD across 100 cross-validations iterations. The 172 Level 3 pathways were predicted with an overall MCC of 0.726. Moreover, metabolite association with the 12 Level 2 pathway categories were predicted with an overall MCC of 0.891, representing significant transfer learning from the Level 3 pathway entries. These are the best metabolite-pathway prediction results published so far in the field.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945429","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}
Pub Date : 2024-08-09DOI: 10.1101/2024.08.08.607134
Mintu Nandi
Natural variations in gene expression, called noise, are fundamental to biological systems. The expression noise can be beneficial or detrimental to cellular functions. While the impact of noise on individual genes is well-established, our understanding of how noise behaves when multiple genes are co-expressed by shared regulatory elements within transcription networks remains elusive. This lack of understanding extends to how the architecture and regulatory features of these networks influence noise. To address this gap, we study the multi-output feed-forward loop motif. The motif is prevalent in bacteria and yeast and influences co-expression of multiple genes by shared transcription factors. Focusing on a two-output variant of the motif, the present study explores the interplay between its architecture, co-expression patterns of the two genes (including symmetric and asymmetric expressions), and the associated noise dynamics. We employ a stochastic modeling approach to investigate how the binding affinities of the transcription factors influence symmetric and asymmetric expression patterns and the resulting noise dynamics in the co-expressed genes. This knowledge could guide the development of strategies for manipulating gene expression patterns through targeted modulation of transcription factor binding affinities.
{"title":"Emergence of temporal noise hierarchy in co-regulated genes of multi-output feed-forward loop","authors":"Mintu Nandi","doi":"10.1101/2024.08.08.607134","DOIUrl":"https://doi.org/10.1101/2024.08.08.607134","url":null,"abstract":"Natural variations in gene expression, called noise, are fundamental to biological systems. The expression noise can be beneficial or detrimental to cellular functions. While the impact of noise on individual genes is well-established, our understanding of how noise behaves when multiple genes are co-expressed by shared regulatory elements within transcription networks remains elusive. This lack of understanding extends to how the architecture and regulatory features of these networks influence noise. To address this gap, we study the multi-output feed-forward loop motif. The motif is prevalent in bacteria and yeast and influences co-expression of multiple genes by shared transcription factors. Focusing on a two-output variant of the motif, the present study explores the interplay between its architecture, co-expression patterns of the two genes (including symmetric and asymmetric expressions), and the associated noise dynamics. We employ a stochastic modeling approach to investigate how the binding affinities of the transcription factors influence symmetric and asymmetric expression patterns and the resulting noise dynamics in the co-expressed genes. This knowledge could guide the development of strategies for manipulating gene expression patterns through targeted modulation of transcription factor binding affinities.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945386","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}
Pub Date : 2024-08-09DOI: 10.1101/2024.08.07.607075
Antoni Matyjaszkiewicz, James Sharpe
Successful computational modelling of complex biological phenomena will depend on the seamless sharing of models and hypotheses among researchers of all backgrounds - experimental and theoretical. LimbNET, a new online tool for modelling, simulating and visualising spatiotemporal patterning in limb development, aims to facilitate this process within the limb development community. LimbNET enables remote users to define and simulate arbitrary gene regulatory network (GRN) models of 2D spatiotemporal developmental patterning processes. Researchers can test and compare each others' hypotheses - GRNs and predicted spatiotemporal patterns - within a common framework. A database of previously created models empowers users to simulate, explore, and extend each others' work. Spatiotemporally-varying gene expression intensities, derived from image-based data, are mapped into a standardised computational description of limb growth, integrated within our modelling framework. This enables direct comparison not only between datasets but between data and simulation outputs, closing the feedback loop between experiments and simulation via parameter optimisation. All functionality is accessible through a web browser, requiring no special software, and opening the field of image-driven modelling to the full scientific community.
{"title":"LimbNET: collaborative platform for simulating spatial patterns of gene networks in limb development","authors":"Antoni Matyjaszkiewicz, James Sharpe","doi":"10.1101/2024.08.07.607075","DOIUrl":"https://doi.org/10.1101/2024.08.07.607075","url":null,"abstract":"Successful computational modelling of complex biological phenomena will depend on the seamless sharing of models and hypotheses among researchers of all backgrounds - experimental and theoretical. LimbNET, a new online tool for modelling, simulating and visualising spatiotemporal patterning in limb development, aims to facilitate this process within the limb development community. LimbNET enables remote users to define and simulate arbitrary gene regulatory network (GRN) models of 2D spatiotemporal developmental patterning processes. Researchers can test and compare each others' hypotheses - GRNs and predicted spatiotemporal patterns - within a common framework. A database of previously created models empowers users to simulate, explore, and extend each others' work. Spatiotemporally-varying gene expression intensities, derived from image-based data, are mapped into a standardised computational description of limb growth, integrated within our modelling framework. This enables direct comparison not only between datasets but between data and simulation outputs, closing the feedback loop between experiments and simulation via parameter optimisation. All functionality is accessible through a web browser, requiring no special software, and opening the field of image-driven modelling to the full scientific community.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"200 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945387","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}
Pub Date : 2024-08-09DOI: 10.1101/2024.08.07.606989
James S. Nagai, Michael T. Schaub, Ivan G.Costa
Motivation The combination of single-cell sequencing with ligand-receptor analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs stand out as a natural representation of cell-cell communication events. However, current computational methods cannot analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the non-linear nature of cell-cell communication and the high variability presented by the patient-specific single-cell RNAseq datasets.
{"title":"scACCorDiON: A clustering approach for explainable patient level cell cell communication graph analysis","authors":"James S. Nagai, Michael T. Schaub, Ivan G.Costa","doi":"10.1101/2024.08.07.606989","DOIUrl":"https://doi.org/10.1101/2024.08.07.606989","url":null,"abstract":"<strong>Motivation</strong> The combination of single-cell sequencing with ligand-receptor analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs stand out as a natural representation of cell-cell communication events. However, current computational methods cannot analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the non-linear nature of cell-cell communication and the high variability presented by the patient-specific single-cell RNAseq datasets.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945388","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}
Pub Date : 2024-08-07DOI: 10.1101/2024.08.04.606534
Lina Eckert, Maria Sol Vidal-Saez, Ziyuan Zhao, Jordi Garcia-Ojalvo, Rosa Martinez-Corral, Jeremy Gunawardena
The ability to learn is typically attributed to animals with brains. However, the apparently simplest form of learning, habituation, in which a steadily decreasing response is exhibited to a repeated stimulus, is found not only in animals but also in single-cell organisms and individual mammalian cells. Habituation has been codified from studies in both invertebrate and vertebrate animals, as having ten characteristic hallmarks, seven of which involve a single stimulus. Here, we show by mathematical modelling that simple molecular networks, based on plausible biochemistry with common motifs of negative feedback and incoherent feedforward, can robustly exhibit all single-stimulus hallmarks. The models reveal how the hallmarks arise from underlying properties of timescale separation and reversal behaviour of memory variables and they reconcile opposing views of frequency and intensity sensitivity expressed within the neuroscience and cognitive science traditions. Our results suggest that individual cells may exhibit habituation behaviour as rich as that in multi-cellular animals with central nervous systems and that the relative simplicity of the biomolecular level may enhance our understanding of the mechanisms of learning.
{"title":"Learning in single cells: biochemically-plausible models of habituation","authors":"Lina Eckert, Maria Sol Vidal-Saez, Ziyuan Zhao, Jordi Garcia-Ojalvo, Rosa Martinez-Corral, Jeremy Gunawardena","doi":"10.1101/2024.08.04.606534","DOIUrl":"https://doi.org/10.1101/2024.08.04.606534","url":null,"abstract":"The ability to learn is typically attributed to animals with brains. However, the apparently simplest form of learning, habituation, in which a steadily decreasing response is exhibited to a repeated stimulus, is found not only in animals but also in single-cell organisms and individual mammalian cells. Habituation has been codified from studies in both invertebrate and vertebrate animals, as having ten characteristic hallmarks, seven of which involve a single stimulus. Here, we show by mathematical modelling that simple molecular networks, based on plausible biochemistry with common motifs of negative feedback and incoherent feedforward, can robustly exhibit all single-stimulus hallmarks. The models reveal how the hallmarks arise from underlying properties of timescale separation and reversal behaviour of memory variables and they reconcile opposing views of frequency and intensity sensitivity expressed within the neuroscience and cognitive science traditions. Our results suggest that individual cells may exhibit habituation behaviour as rich as that in multi-cellular animals with central nervous systems and that the relative simplicity of the biomolecular level may enhance our understanding of the mechanisms of learning.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"201 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945526","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}
Pub Date : 2024-08-06DOI: 10.1101/2024.08.02.606432
Andrew Goetz, Frances Shanahan, Logan Brooks, Eva Lin, Rana Mroue, Darlene Dela Cruz, Thomas Hunsaker, Bartosz Czech, Purushottam Dixit, Udi Segal, Scott Martin, Scott A. Foster, Luca Gerosa
Purpose: This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range.
{"title":"Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma","authors":"Andrew Goetz, Frances Shanahan, Logan Brooks, Eva Lin, Rana Mroue, Darlene Dela Cruz, Thomas Hunsaker, Bartosz Czech, Purushottam Dixit, Udi Segal, Scott Martin, Scott A. Foster, Luca Gerosa","doi":"10.1101/2024.08.02.606432","DOIUrl":"https://doi.org/10.1101/2024.08.02.606432","url":null,"abstract":"Purpose: This study explores the potential of preclinical <em>in vitro</em> cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated <em>in vivo</em> translatability of <em>in vitro</em> dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969398","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}
Pub Date : 2024-08-06DOI: 10.1101/2024.08.02.605997
Aleksandra Eremina, Christian Schwall, Teresa Saez, Lennart Witting, Dietrich Kohlheyer, Bruno M.C. Martins, Philipp Thomas, James C.W. Locke
Circadian clocks enable organisms to anticipate daily cycles, while being robust to molecular and environmental noise. Here, we show how the cyanobacterial clock buffers genetic and environmental perturbations through its core phosphorylation loop. We first characterise single-cell clock dynamics in clock mutants using a microfluidics device that allows precise control of the microenvironment. We find known clock regulators are dispensable for clock robustness, whilst perturbations of the core clock reveal that the wild-type operates at a noise optimum that we can reproduce in a stochastic model of just the core phosphorylation loop. We then examine how the clock responds to noisy environments, including natural light conditions. The model accurately predicts how the clock filters out environmental noise, including fast light fluctuations, to keep time while remaining responsive to environmental shifts. Our findings illustrate how a simple clock network can exhibit complex noise filtering properties, advancing our understanding of how biological circuits can perform accurately in natural environments.
{"title":"Environmental and molecular noise buffering by the cyanobacterial clock in individual cells","authors":"Aleksandra Eremina, Christian Schwall, Teresa Saez, Lennart Witting, Dietrich Kohlheyer, Bruno M.C. Martins, Philipp Thomas, James C.W. Locke","doi":"10.1101/2024.08.02.605997","DOIUrl":"https://doi.org/10.1101/2024.08.02.605997","url":null,"abstract":"Circadian clocks enable organisms to anticipate daily cycles, while being robust to molecular and environmental noise. Here, we show how the cyanobacterial clock buffers genetic and environmental perturbations through its core phosphorylation loop. We first characterise single-cell clock dynamics in clock mutants using a microfluidics device that allows precise control of the microenvironment. We find known clock regulators are dispensable for clock robustness, whilst perturbations of the core clock reveal that the wild-type operates at a noise optimum that we can reproduce in a stochastic model of just the core phosphorylation loop. We then examine how the clock responds to noisy environments, including natural light conditions. The model accurately predicts how the clock filters out environmental noise, including fast light fluctuations, to keep time while remaining responsive to environmental shifts. Our findings illustrate how a simple clock network can exhibit complex noise filtering properties, advancing our understanding of how biological circuits can perform accurately in natural environments.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945363","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}
Waddington’s epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscape’s geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency.
{"title":"The intrinsic dimension of gene expression during cell differentiation","authors":"Marta Biondo, Niccolò Cirone, Filippo Valle, Silvia Lazzardi, Michele Caselle, Matteo Osella","doi":"10.1101/2024.08.02.606382","DOIUrl":"https://doi.org/10.1101/2024.08.02.606382","url":null,"abstract":"Waddington’s epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscape’s geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945361","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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