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Rxivist uses download data on preprints from bioRxiv to help you find the papers being discussed in your field. Currently indexing 101,433 bioRxiv papers from 428,488 authors.

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in category systems biology

2,558 results found. For more information, click each entry to expand.

2381: Trade-offs in Robustness to Perturbations of Bacterial Population Controllers
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Posted to bioRxiv 05 Jun 2020

Trade-offs in Robustness to Perturbations of Bacterial Population Controllers
125 downloads systems biology

Cameron McBride, Domitilla Del Vecchio

Synthetic biology applications have the potential to have lasting impact; however, there is considerable difficulty in scaling up engineered genetic circuits. One of the current hurdles is resource sharing, where different circuit components become implicitly coupled through the host cell's pool of resources, which may destroy circuit function. One potential solution around this problem is to distribute genetic circuit components across multiple cell strains and control the cell population size using a population controller. In these situations, perturbations in the availability of cellular resources, such as due to resource sharing, will affect the performance of the population controller. In this work, we model a genetic population controller implemented by a genetic circuit while considering perturbations in the availability of cellular resources. We analyze how these intracellular perturbations and extracellular disturbances to cell growth affect cell population size. We find that it is not possible to tune the population controller's gain such that the population density is robust to both extracellular disturbances and perturbations to the pool of available resources. ### Competing Interest Statement The authors have declared no competing interest.

2382: A Single Chromosome Strain of S. cerevisiae Exhibits Diminished Ethanol Metabolism and Tolerance.
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Posted to bioRxiv 23 Aug 2020

A Single Chromosome Strain of S. cerevisiae Exhibits Diminished Ethanol Metabolism and Tolerance.
125 downloads systems biology

Tyler W. Doughty, Rosemary Yu, Lucy Fang-I Chao, Zhongjun Qin, Verena Siewers, Jens Nielsen

This study characterized the growth, metabolism, and transcriptional profile of a S. cerevisiae strain with a single large chromosome that was constructed via successive chromosomal fusions. The single chromosome strain exhibited a longer lag phase, increased doubling time, and lower final biomass concentration compared with a wildtype strain when grown on YPD. These phenotypes were amplified when ethanol was added to the medium or used as the sole carbon source. RNAseq analysis showed diminished induction of genes involved in diauxic shift, ethanol metabolism, fatty-acid beta-oxidation, and methylglyoxal catabolism during growth on ethanol compared to the reference strain. Enzyme-constrained metabolic modeling predicted that decreased flux through these poorly induced enzymes results in diminished ATP formation and decreased biomass accumulation observed. Together, these observations suggest that switch-like control of carbon source dependent gene expression in S. cerevisiae requires genome arrangement into multiple chromosomes. ### Competing Interest Statement The authors have declared no competing interest.

2383: Transcription factor binding dynamics shape noise across biological processes
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Posted to bioRxiv 28 Jul 2020

Transcription factor binding dynamics shape noise across biological processes
123 downloads systems biology

Lavisha Parab, Sampriti Pal, Riddhiman Dhar

Cellular processes driven by coordinated actions of individual genes generate cellular phenotypes. Stochastic variations in these processes lead to phenotypic heterogeneity that often has important implications for antibiotic persistence, mutation penetrance, cancer growth and anti-cancer drug resistance. However, the architecture of noise in cellular processes has remained largely unexplored even though expression noise in individual genes have been widely studied. Here we quantify noise in biological processes in yeast and through an integrated quantitative model show that the number of regulating transcription factors and their binding dynamics are the primary drivers of noise. Specifically, binding dynamics arising from competition and cooperation among TFs for promoter binding can predict a large fraction of noise variation. Our work reveals a novel mechanism of noise regulation that arises out of the dynamic nature of gene regulation and is not dependent on specific transcription factor or specific promoter sequence. ### Competing Interest Statement The authors have declared no competing interest.

2384: Internetwork connectivity of molecular networks across species of life
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Posted to bioRxiv 03 Aug 2020

Internetwork connectivity of molecular networks across species of life
123 downloads systems biology

Tarun Mahajan, Roy D. Dar

Background: Molecular interactions have been studied as independent complex networks in systems biology. However, molecular networks dont exist independently of each other. In a network of networks approach (called multiplex), we uncover the design principles for the joint organization of transcriptional regulatory network (TRN) and protein-protein Interaction (PPI) network. Results: We find that TRN and PPI networks are non-randomly coupled in the TRN-PPI multiplex across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene interactions in TRN and protein-protein interactions in PPI also non-randomly overlap in the multiplex. These design principles are conserved across the five eukaryotic species. We show that the robustness of the TRN-PPI multiplex is dependent on these design principles. Further, functionally important genes and proteins, such as essential, disease-related and those involved in host-pathogen PPI networks, are preferentially situated in essential parts of the human multiplex with highly overlapping interactions. Conclusion: We unveil the multiplex architecture of TRN and PPI networks across different species. Multiplex architecture may thus define a general framework for studying molecular networks across the different species of life. This approach may uncover the building blocks of the hierarchical organization of molecular interactions. Keywords: Networks; Gene Regulatory Network; Protein-Protein Interaction; Multiplex; Network of Networks; Network Biology; Host-Pathogen Interaction; Disease-Gene Association ### Competing Interest Statement The authors have declared no competing interest.

2385: PRER: A Patient Representation with Pairwise Relative Expression of Proteins on Biological Networks
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Posted to bioRxiv 16 Jun 2020

PRER: A Patient Representation with Pairwise Relative Expression of Proteins on Biological Networks
122 downloads systems biology

Halil Ibrahim Kuru, Mustafa Buyukozkan, Oznur Tastan

Alterations in protein and gene expression levels are often used as features to predictive models such as clinical outcome prediction. A common strategy to combine signals on individual proteins is to integrate alterations with biological knowledge. In this work, we propose a novel patient representation where we integrate the expression levels of proteins with the biological networks. Patient representation with PRER (Pairwise Relative Expressions with Random walks) operates in the neighborhood of a protein and aims to capture the dysregulation patterns in protein abundance for proteins that are known to interact. This neighborhood of the source protein is derived using a biased random-walk strategy on the network. Specifically, PRER computes a feature vector for a patient by comparing the protein expression level of the source protein with other proteins' levels in its neighborhood. We test PRER's performance through a survival prediction task in 10 different cancers using random forest survival models. PRER representation yields a statistically significant predictive performance in 8 out of 10 cancer types when compared to a representation based on individual protein expression. We also identify the set of proteins that are important not because of alteration of its expression values but due to the alteration in their pairwise relative expression values. The set of identified relations provides a valuable collection of biomarkers with high prognostic value. PRER representation can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER ### Competing Interest Statement The authors have declared no competing interest.

2386: Physical modeling of a sliding clamp mechanism for the spreading of ParB at short genomic distance from bacterial centromere sites
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Posted to bioRxiv 24 Jul 2020

Physical modeling of a sliding clamp mechanism for the spreading of ParB at short genomic distance from bacterial centromere sites
122 downloads systems biology

Jean-Charles Walter, Jérôme Rech, Nils-Ole Walliser, Jérôme Dorignac, Frédéric Geniet, John Palmeri, Andrea Parmeggiani, Jean-Yves Bouet

Bacterial ParB partitioning proteins involved in chromosomes and low-copy-number plasmid segregation have recently been shown to belong to a new class of CTP-dependent molecular switches. Strikingly, CTP binding and hydrolysis was shown to induce a conformational change enabling ParB dimers to switch between an open and a closed conformation. This latter conformation clamps ParB dimers on DNA molecules, allowing their diffusion in one dimension along the DNA. It has been proposed that this novel sliding property may explain the spreading capability of ParB over more than 10-Kb from parS centromere sites where ParB is specifically loaded. Here, we modeled such a mechanism as a typical reaction-diffusion system and compared this 'Clamping & sliding' model to the ParB DNA binding pattern from high-resolution ChIP-sequencing data. We found that this mechanism cannot account for all the in vivo characteristics, especially the long range of ParB binding to DNA. In particular, it predicts a strong effect from the presence of a roadblock on the ParB binding pattern that is not observed in ChIP-seq. Moreover, the rapid assembly kinetics observed in vivo after the duplication of parS sites is not easily explained by this mechanism. We propose that 'Clamping & sliding' might explain the ParB spreading pattern at short distances from parS but that another mechanism must apply for ParB recruitment at larger genomic distances. ### Competing Interest Statement The authors have declared no competing interest.

2387: A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)
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Posted to bioRxiv 01 Jun 2020

A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)
122 downloads systems biology

Benjamin P. Kellman, Yujie Zhang, Emma Logomasini, Eric Meinhardt, Austin W. T. Chiang, James T. Sorrentino, Chenguang Liang, Bokan Bao, Yusen Zhou, Sachiko Akase, Isami Sogabe, Thukaa Kouka, Iain B.H. Wilson, Matthew P. Campbell, Sriram Neelamegham, Frederick J. Krambeck, Kiyoko F. Aoki-Kinoshita, Nathan E. Lewis

Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Reaction rule-extended Linear Code is an unambiguous representation for describing glycosylation reactions in both literature and code. ### Competing Interest Statement The authors have declared no competing interest.

2388: Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
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Posted to bioRxiv 09 Sep 2019

Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
122 downloads systems biology

Abhishek Varghese, C. Drovandi, Kerrie Mengersen, Antonietta Mira

The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to the agricultural sector. Current models of BBTV are largely deterministic, limited by an incomplete understanding of interactions in complex natural systems, and the appropriate identification of parameters. A stochastic network-based Susceptible-Infected model has been created which simulates the spread of BBTV across the subsections of a banana plantation, parameterising nodal recovery, neighbouring and distant infectivity across summer and winter. Findings from posterior results achieved through Markov Chain Monte Carlo approach to approximate Bayesian computation suggest seasonality in all parameters, which are influenced by correlated changes in inspection accuracy, temperatures and aphid activity. This paper demonstrates how the model may be used for monitoring and forecasting of various disease management strategies to support policy-level decision making.

2389: Treating the Network: Targeted inhibition of two specific microRNAs in the brainstem prevents the development of hypertension
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Posted to bioRxiv 12 Mar 2020

Treating the Network: Targeted inhibition of two specific microRNAs in the brainstem prevents the development of hypertension
122 downloads systems biology

Jonathan Gorky, Danielle DeCicco, Sirisha Achanta, James Schwaber, Rajanikanth Vadigepalli

We here test the concept that disease states may result not from a single cause but from small changes in a network that are collectively significant. We recently showed that development of hypertension (HTN) in the spontaneously hypertensive rat (SHR) model of human essential hypertension is accompanied by changes in microRNA expression levels in the brainstem tracking the development of HTN. This led to the hypothesis that preventing the change in microRNA levels could prevent the development of HTN. We propose that hypertension emerges from a network that has been pushed out of a normotensive equilibrium into a compensatory, pathological state. We show that small perturbations in the gene regulatory networks in the brainstem by selectively blocking two microRNAs highlighted in our previous results, miR-135a and miR-376a, is sufficient to prevent development of hypertension in the SHR model. This effect appears driven by only modest changes in the expression of rate-limiting genes, many of which are targets of these miRNAs, suggesting that the combination of genes that are targeted in the network is responsible for the effect. The demonstration that hypertension is an emergent property of an underlying regulatory network suggests that a new treatment paradigm altogether is needed.

2390: Reconstruction of Fur pan-regulon uncovers the complexity and diversity of transcriptional regulation in E. coli
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Posted to bioRxiv 22 May 2020

Reconstruction of Fur pan-regulon uncovers the complexity and diversity of transcriptional regulation in E. coli
122 downloads systems biology

Ye Gao, Ina Bang, Yara Seif, Gayoung Nam, Anand V Sastry, Ke Chen, Jonathan M. Monk, Kumari Sonal Choudhary, Sang Woo Seo, Eun-Yeol Lee, Donghyuk Kim, Bernhard O Palsson

Regulons for many transcription factors have been elucidated in model strains leading to an understanding of their role in producing physiological states. Comparative analysis of a regulon and its target genes between different strains of the same species is lacking. Ferric uptake regulator (Fur), involved in iron homeostasis, is one of the most conserved TFs, and is present in a wide range of bacteria. Using ChIP-exo experiments, we performed a comprehensive study of Fur binding sites in nine Escherichia coli strains with different lifestyles. 79 of the 431 target genes (18%) found belong to Fur core regulon, comprising genes involved in ion transport and metabolism, energy production and conversion, and amino acid metabolism and transport. 179 of the target genes (42%) comprise the accessory regulon, most of which were related to cell wall structure and biogenesis, and virulence factor pathways. The remaining target genes (173 or 40%) were in the unique regulon, with gene functions that were largely unknown. Furthermore, deletion of the fur gene led to distinct phenotypes in growth, motility, antibiotic resistance, and the change of siderophore production. These results provide a more complete understanding of how Fur regulates a set of target genes with surprising variation in closely related bacteria. ### Competing Interest Statement The authors have declared no competing interest.

2391: Discovering compounds mimicking calorie restriction using mammalian gene expression profiles
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Posted to bioRxiv 03 Jun 2020

Discovering compounds mimicking calorie restriction using mammalian gene expression profiles
121 downloads systems biology

Alexei Vazquez

Obesity is a risk factor for cardiovascular diseases, diabetes and cancer. In theory the obesity problem could be solve by the adherence to a calorie restricted diet, but that is not generally achieved in practice. An alternative is a pharmacological approach, using compounds that trigger the same metabolic changes associated with calorie restriction. Here I expand in the pharmacological direction by identifying compounds that induce liver gene signature profiles that mimic those induced by calorie restriction. Using gene expression profiles from mice and rat I identify corticosteroids, PPAR agonists and some antibacterial/antifungal as candidate compounds mimicking the response to calorie restriction in the liver gene signatures. Liver gene signature analysis can be used to identify compounds that mimic calorie restriction. ### Competing Interest Statement The authors have declared no competing interest.

2392: Computational study on ratio-sensing in yeast galactose utilization pathway
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Posted to bioRxiv 19 May 2020

Computational study on ratio-sensing in yeast galactose utilization pathway
119 downloads systems biology

Jiayin Hong, Bo Hua, Michael Springer, Chao Tang

Metabolic networks undergo gene expression regulation in response to external nutrient signals. In microbes, the synthesis of enzymes that are used to transport and catabolize less preferred carbon sources is repressed in the presence of a preferred carbon source. For most microbes, glucose is a preferred carbon source, and it has long been believed that as long as glucose is present in the environment, the expression of genes related to the metabolism of alternative carbon sources is shut down, due to catabolite repression. However, recent studies have shown that the induction of the galactose (GAL) metabolic network does not solely depend on the exhaustion of glucose. Instead, the GAL genes respond to the external concentration ratio of galactose to glucose, a phenomenon of unknown mechanism that we termed ratio-sensing. Using mathematical modeling, we found that ratio-sensing is a general phenomenon that can arise from competition between two carbon sources for shared transporters, between transcription factors for binding to communal regulatory sequences of the target genes, or a combination of the aforementioned two levels of competition. We analyzed how the parameters describing the competitive interaction influenced ratio-sensing behaviors in each scenario and found that the concatenation of both layers of signal integration can expand the dynamical range of ratio-sensing. Finally, we investigated the influence of circuit topology on ratio-sensing and found that incorporating negative auto-regulation and/or coherent feedforward loop motifs to the basic signal integration unit can tune the sensitivity of the response to the external nutrient signals. Our study not only deepened our understanding of how ratio-sensing is achieved in yeast GAL metabolic regulation, but also elucidated design principles for ratio-sensing signal processing that can be used in other biological settings, such as being introduced into circuit designs for synthetic biology applications.

2393: Five patterns of cell signaling pathways associated with cell behavior
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Posted to bioRxiv 04 Aug 2020

Five patterns of cell signaling pathways associated with cell behavior
119 downloads systems biology

Yuji Takeda, Kazuharu Kawano, Rui Ma, Shinichi Saitoh, Hironobu Asao

Cell signaling pathway is complex systems. Here, we present a concept for a new approach to analyze cell signaling pathway associated with cell behavior. In theoretically, cell behavior is recognized by energy and fluctuation. In this study, we measured phosphorylation level of signal transducers in a cell and fluctuation of the phosphorylation level in the cell population using flow cytometry. Flow cytometric data of mean fluorescence intensity (MFI) and coefficient variation (CV) were considered to the energy and the fluctuation, respectively. Topologically, the changes of MFI and CV were categorized into five patterns (we tentatively named as attractive, subsequent, passive, counter, and negative arbiter). In this study, we clarified the relationship between the cell behavior and the five patterns. Furthermore, combining the five patterns can define the signaling pathways, such as simple activated signal, oscillating signal, regulatory signal, robust signal, or homeostatic signal. These observations provide a proof of concept for general strategy to use the five patterns for connection between cell signaling pathway and cell behavior.

2394: Meta-azotomics of engineered wastewater treatment processes reveals differential contributions of established and novel models of N-cycling
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Posted to bioRxiv 25 Aug 2020

Meta-azotomics of engineered wastewater treatment processes reveals differential contributions of established and novel models of N-cycling
119 downloads systems biology

Mee-Rye Park, Medini K. Annavajhala, Kartik Chandran

The application of metagenomics and metatranscriptomics to field-scale engineered biological nitrogen removal (BNR) processes revealed a complex N-cycle network (the meta-azotome) therein in terms of microbial structure, potential and extant function. Autotrophic nitrification bore the imprint of well-documented Nitrosomonas and Nitrospira in most systems. However, in select BNR processes, complete ammonia oxidizing bacteria, comammox Nitrospira, unexpectedly contributed more substantially to ammonia oxidation than canonical ammonia oxidizing bacteria, based on metatranscriptomic profiling. Methylotrophic denitrification was distinctly active in methanol-fed reactors but not in glycerol-fed reactors. Interestingly, glycerol metabolism and N-reduction transcript signatures were uncoupled, possibly suggesting the role of other carbon sources in denitrification emanating from glycerol itself or from upstream process reactors. In sum, the meta-azotome of engineered BNR processes revealed both traditional and novel mechanisms of N-cycling. Similar interrogation approaches could potentially inform better design and optimization of wastewater treatment and engineered bioprocesses in general. ### Competing Interest Statement The authors have declared no competing interest.

2395: Single cell tracking based on Voronoi partition via stable matching
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Posted to bioRxiv 20 Aug 2020

Single cell tracking based on Voronoi partition via stable matching
119 downloads systems biology

Young Hwan Chang, Jeremy W. Linsley, Josh Lamstein, Jaslin Kalra, Irina Epstein, Mariya Barch, Kenneth Daily, Phil Snyder, Larsson Omberg, Laura Heiser, Steve Finkbeiner

Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as "stable matching problem" and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking. ### Competing Interest Statement The authors have declared no competing interest.

2396: Structure of autosynthetic models of balanced cell growth and numerical optimization of their growth rate
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Posted to bioRxiv 20 Sep 2020

Structure of autosynthetic models of balanced cell growth and numerical optimization of their growth rate
119 downloads systems biology

Deniz Sezer, Peter Schubert, Martin Lercher

Genome-scale reaction network models are available for many prokaryotic organisms. Yet, to predict the proteome and metabolome of the cell from them, additional information about (i) the nonlinear enzyme kinetics and (ii) the regulation of protein expression by metabolic signals is necessary. Knowledge about the latter could be sidestepped by assuming that expression regulation has evolved to achieve the protein composition that maximizes cellular growth rate. A general mathematical framework for optimizing the growth rate of models comprising an arbitrarily complex metabolic network and a relatively simple protein-synthesis network was recently formulated independently by two research groups [de Groot et al., PLoS Comput. Biol. 16 , e1007559 (2020); Dourado & Lercher, Nature Commun. 11 , 1226 (2020)]. Here, this formalism is further developed with particular focus on carrying out the optimization numerically. To this end, we identify the concentrations of the enzymes as the independent variables of the optimization problem and propose novel multiplicative updates for the iterative calculation of the dependent metabolite concentrations. The reduced gradient method, with analytical derivatives, is employed for the numerical optimization. Additionally, the roles of the dilution of the metabolite concentrations by growth and the commonly invoked constraint on the cell dry mass density are clarified. These developments should lay the basis for the practical optimization of large-scale kinetic models, thus formally connecting the physiological “macrostate” of the cell, characterized by its growth rate, to its “microstate”, described by the cell proteome and metabolome. Author summary An evolving population of non-interacting, unicellular organisms in a constant environment will maximize its growth rate. By expressing the growth rate as a mathematical function of the cellular composition, it becomes possible to formulate an optimization problem whose solution yields the cell proteome and metabolome at the maximal growth rate. The formulation and solution of such an optimization problem has the potential to elucidate fundamental optimality principles in living cells and to enable the engineering of complex biological systems. Building on previous work, here we address the task of solving this optimization problem numerically. In the process, we elucidate the mathematical role of some common simplifying approximations. This allows us to organize many of the existing formulations of the optimization problem into a hierarchy, whose lower levels are reached by invoking these approximations. ### Competing Interest Statement The authors have declared no competing interest.

2397: Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
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Posted to bioRxiv 15 Jun 2020

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
118 downloads systems biology

Andrei S. Rodin, Grigoriy Gogoshin, Lei Wang, Colt Egelston, Russel Rockne, Joseph Chao, Peter P. Lee

Cancer immunotherapy, specifically immune checkpoint blockade therapy, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients with certain cancer types achieve clinical responses. Consequently, elucidating immune system-related pre-treatment biomarkers that are predictive with respect to sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders and non-responders. Specifically, our group has been studying immune signaling networks as an accurate reflection of the global immune state. Flow cytometry data (FACS, Fluorescence-activated cell sorting) characterizing immune signaling in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune signaling networks in this setting. We developed a novel computational pipeline to perform secondary analyses of FACS data using systems biology / machine learning / information-theoretic techniques and concepts, primarily based on Bayesian network modeling. Application of this novel pipeline resulted in determination of immune markers, combinations / interactions thereof, and corresponding immune cell population types that are associated with clinical responses. Future studies are planned to generalize our analytical approach to different cancer types and corresponding datasets. ### Competing Interest Statement J.C. has received research funding (institutional) and consultant/advisory fees from Merck and serves on the speaker's bureau for Merck.

2398: Computational Model of G2-M DNA Damage Checkpoint Regulation in Normal and p53-null Cancer Cells
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Posted to bioRxiv 18 Jun 2020

Computational Model of G2-M DNA Damage Checkpoint Regulation in Normal and p53-null Cancer Cells
118 downloads systems biology

Yongwoon Jung, Pavel Kraikivski

Cancer and normal cells can respond differently to the same stressful conditions. Their dynamic responses under normal and stressful conditions are governed by complex molecular regulatory networks. We developed a computational model of G2-M DNA damage checkpoint regulation to study normal and cancer cell cycle progression under normal and stressful conditions. Our model is successful in explaining cancer cell cycle arrest in conditions when some cell cycle and DNA damage checkpoint regulators are inhibited, whereas the same conditions only delay entry into mitosis in normal cells. We use the model to explain known phenotypes of gene deletion mutants and predict phenotypes of yet uncharacterized mutants in normal and cancer cells. We also use sensitive analyses to identify the ranges of model parameter values that lead to the cell cycle arrest in cancer cells. Our results can be used to predict the effect of a potential treatment on cell cycle progression of normal and cancer cells. ### Competing Interest Statement The authors have declared no competing interest.

2399: Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile
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Posted to bioRxiv 15 Sep 2020

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile
118 downloads systems biology

Mario L Arrieta-Ortiz, Selva Rupa Christinal Immanuel, Serdar Turkarslan, Wei Ju Wu, Brintha P. Girinathan, Jay N. Worley, Nicholas DiBenedetto, Olga Soutourina, Johann Peltier, Bruno Dupuy, Lynn Bry, Nitin S. Baliga

Though Clostridioides difficile is among the most studied anaerobes, we know little about the systems level interplay of metabolism and regulation that underlies its ability to negotiate complex immune and commensal interactions while colonizing the human gut. We have compiled publicly available resources, generated through decades of work by the research community, into two models and a portal to support comprehensive systems analysis of C. difficile . First, by compiling a compendium of 148 transcriptomes from 11 studies we have generated an E nvironment and G ene R egulatory I nfluence N etwork (EGRIN) model that organizes 90% of all genes in the C. difficile genome into 297 high quality modules based on evidence for their conditional co-regulation by at least 120 transcription factors. EGRIN predictions, validated with independently-generated datasets, have recapitulated previously characterized C. difficile regulons of key transcriptional regulators, refined and extended membership of genes within regulons, and implicated new genes for sporulation, carbohydrate transport and metabolism. Findings further predict pathogen behaviors in in vivo colonization, and interactions with beneficial and detrimental commensals. Second, by advancing a constraints-based metabolic model, we have discovered that 15 amino acids, diverse carbohydrates, and 24 genes across glyoxylate, Wood-Ljungdahl, nucleotide, amino acid, and carbohydrate metabolism are essential to support growth of C. difficile within an intestinal environment. Models and supporting resources are accessible through an interactive web portal (<http://networks.systemsbiology.net/cdiff-portal/>) to support collaborative systems analyses of C. difficile . ### Competing Interest Statement The authors have declared no competing interest.

2400: The Synergy of Damage Repair and Retention Promotes Rejuvenation and Prolongs Healthy Lifespans in Cell Lineages
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Posted to bioRxiv 25 Mar 2020

The Synergy of Damage Repair and Retention Promotes Rejuvenation and Prolongs Healthy Lifespans in Cell Lineages
118 downloads systems biology

Barbara Schnitzer, Johannes Borgqvist, Marija Cvijovic

Damaged proteins are inherited asymmetrically during cell division in the yeast Saccharomyces cerevisiae, such that most damage is retained within the mother cell. The consequence is an ageing mother and a rejuvenated daughter cell with full replicative potential. Daughters of old and damaged mothers are however born with increasing levels of damage resulting in lowered replicative lifespans. Remarkably, these prematurely old daughters can give rise to rejuvenated cells with low damage levels and recovered lifespans, called second-degree rejuvenation. We aimed to investigate how damage repair and retention together can promote rejuvenation and at the same time ensure low damage levels in mother cells, reflected in longer health spans. We developed a dynamic model for damage accumulation over successive divisions in individual cells as part of a dynamically growing cell lineage. With detailed knowledge about single-cell dynamics and relationships between all cells in the lineage we can infer how individual damage repair and retention strategies affect the propagation of damage in the population. We show that active damage retention lowers damage levels in the population by reducing the variability across the lineage, and results in larger population sizes. Repairing damage efficiently in early life, as opposed to investing in repair when damage has already accumulated, counteracts accelerated ageing caused by damage retention. It prolongs the health span of individual cells which are moreover less prone to stress. In combination, damage retention and early investment in repair are beneficial for healthy ageing in yeast cell populations.

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