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Results for “Synthetic Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Polycomb Group protein EZH2-mediated transcriptional repression of microRNA-338/-421 drives SPINK1-positive prostate cancer

PurposeSerine Peptidase Inhibitor, Kazal type-1 (SPINK1) overexpression defines the second most recurrent and aggressive prostate cancer (PCa) subtype. However, the underlying molecular mechanism and pathobiology of SPINK1 in PCa remains largely unknown.\n\nExperimental DesignMicroRNA-prediction tools were employed to examine the SPINK1-3UTR for miRNAs binding. Luciferase reporter assays were performed to confirm the SPINK1-3UTR binding of shortlisted miR-338-5p/miR-421. Further, miR-338-5p/-421 overexpressing cancer cells (SPINK1-positive) were evaluated for oncogenic properties using cell-based functional assays and mice xenograft model. Global gene expression profiling was performed to unravel the biological pathways altered by miR-338-5p/-421. Immunohistochemistry and RNA in-situ hybridization was carried-out on PCa patients tissue microarray for SPINK1 and EZH2 expression respectively. Chromatin immunoprecipitation assay was performed to examine EZH2 occupancy on the miR-338-5p/-421 regulatory regions. Bisulfite sequencing and methylated DNA-immunoprecipitation was performed on PCa cell lines and patients specimens.\n\nResultsWe established a critical role of miRNA-338-5p/-421 in post-transcriptional regulation of SPINK1. Ectopic expression of miRNA-338-5p/-421 in SPINK1-positive PCa cells abrogate oncogenic properties including cell-cycle progression, stemness and drug resistance, and show reduced tumor burden and distant metastases in mice model. Importantly, we show SPINK1-positive PCa patients exhibit increased EZH2 expression, suggesting its role in miRNA-338-5p/-421 epigenetic silencing. Furthermore, presence of CpG dinucleotide DNA methylation marks on the regulatory regions of miR-338-5p/-421 in SPINK1-positive PCa cells and patients specimens confirms epigenetic silencing.\n\nConclusionOur findings revealed that miRNA-338-5p/-421 are epigenetically silenced in SPINK1-positive PCa, while restoring the expression of these miRNAs using epigenetic drugs or synthetic mimics could abrogate SPINK1-mediated oncogenesis.\n\nTRANSLATIONAL IMPACTWe establish a regulatory model involving the functional interplay between SPINK1, miRNA-338-5p/miRNA-421 and EZH2, thereby, revealing hitherto unknown mechanism of SPINK1 up-regulation in SPINK1-positive subtype. Our findings provide a strong rationale for the development of potential therapeutic strategies for SPINK1-positive malignancies. We demonstrate that restoring miRNA-338-5p/miRNA-421 expression using epigenetic drugs including DNMTs inhibitors in combination with HDACs or HKMTs inhibitors or miRNA synthetic mimics in SPINK1-positive prostate cancer abrogate SPINK1-mediated oncogenicity. The major findings of this study will not only advance the prostate cancer field, but will also be valuable for treatment and disease management of other SPINK1-positive malignancies.

cancer biology↗
bioRxiv2017

The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito

Gene drives have enormous potential for the control of insect populations of medical and agricultural relevance. By preferentially biasing their own inheritance gene drives can rapidly introduce genetic traits even if these confer a negative fitness on the population.\n\nWe have recently developed gene drives based on a CRISPR nuclease construct that is designed to disrupt key genes essential for female fertility in the malaria mosquito. The construct copies itself and the associated genetic disruption from one homologous chromosome to another during gamete formation, in a process called homing that ensures the majority of offspring inherit the drive. Such drives have the potential to cause long-lasting, sustainable population suppression though they are also expected to impose a large selection pressure for resistance in the mosquito. One of these population suppression gene drives showed rapid invasion of a caged population over 4 generations, establishing proof of principle for this technology. In order to assess the potential for the emergence of resistance to the gene drive in this population we allowed it to run for 25 generations and monitored the frequency of the gene drive over time. Following the initial increase of the gene drive we noticed a gradual decrease in its frequency that was accompanied emergence of small, nuclease-induced mutations at the target gene that are resistant to further cleavage and restore its functionality. Such mutations show rates of increase consistent with positive selection in the face of the gene drive. Our findings represent the first documented example of selection for resistance to a synthetic gene drive and lead to important design recommendations and considerations in order to mitigate for resistance for future gene drive applications.

evolutionary biology↗
bioRxiv2018

Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data

Cryo-electron microscopy (cryo-EM) is a powerful structural biology technique capable of determining atomic-resolution structures of biological macromolecules. Despite this ability, the low signal-to-noise ratio of cryo-EM data continues to remain a hurdle for assessing raw cryo-EM micrographs and subsequent image analysis. To help address this problem, we have performed proof-of-principle studies with generative adversarial networks, a form of artificial intelligence, to denoise individual particles. This approach effectively recovers global structural information for both synthetic and real cryo-EM data, facilitating per-particle assessment from noisy raw images. Our results suggest that generative adversarial networks may be able to provide an approach to denoise raw cryo-EM images to facilitate particle selection and raw particle interpretation for single particle and tomography cryo-EM data.

biochemistry↗
bioRxiv2017

Non-latching positive feedback enables robust bimodality by de-coupling expression noise from the mean

Fundamental to biological decision-making is the ability to generate bimodal expression patterns where two alternate expression states simultaneously exist. Here, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIVs fate decision between active replication and viral latency. We find that the HIV Tat protein manipulates the intrinsic toggling of HIVs promoter, the LTR, to generate bimodal ON-OFF expression, and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuits non-cooperative non-latching feedback architecture is optimized to slow the promoters toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that non-latching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean-expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIVs decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.

systems biology↗
bioRxiv2018

A total synthetic approach to CRISPR/Cas9 genome editing and homology directed repair

CRISPR/Cas9 has become a powerful tool for genome editing in zebrafish that permits the rapid generation of loss of function mutations and the knock-in of specific alleles using DNA templates and homology directed repair (HDR). We compared synthetic, chemically modified sgRNAs to in vitro transcribed sgRNAs and demonstrate the increased activity of synthetic sgRNAs in combination with recombinant Cas9 protein. We developed an in vivo genetic assay to measure HDR efficiency and we utilized this assay to optimize the design of synthetic DNA templates to promote HDR. Utilizing these principles, we successfully performed knock-in of fluorophores at multiple genomic loci and demonstrate transmission through the germline at high efficiency. We demonstrate that synthetic HDR templates can be used to knock-in bacterial nitroreductase (ntr) to facilitate lineage ablation of specific cell types. Collectively, our data demonstrate the utility of combining synthetic sgRNAs and dsDNA templates to perform homology directed repair and genome editing in vivo.

molecular biology↗
bioRxiv2017

Coal-Miner: A Coalescent-Based Method For GWA Studies Of Quantitative Traits With Complex Evolutionary Origins

Association mapping (AM) methods are used in genome-wide association (GWA) studies to test for statistically significant associations between genotypic and phenotypic data. The genotypic and phenotypic data share common evolutionary origins - namely, the evolutionary history of sampled organisms - introducing covariance which must be distinguished from the covariance due to biological function that is of primary interest in GWA studies. A variety of methods have been introduced to perform AM while accounting for sample relatedness. However, the state of the art predominantly utilizes the simplifying assumption that sample relatedness is effectively fixed across the genome. In contrast, population genetic theory and empirical studies have shown that sample relatedness can vary greatly across different loci within a genome; this phenomena - referred to as local genealogical variation - is commonly encountered in many genomic datasets. New AM methods are needed to better account for local variation in sample relatedness within genomes.\n\nWe address this gap by introducing Coal-Miner, a new statistical AM method. The Coal-Miner algorithm takes the form of a methodological pipeline. The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively causal markers. Subsequent stages of Coal-Miner perform test for association using a linear mixed model with multiple effects which account for sample relatedness locally within candidate loci and globally across the entire genome.\n\nUsing synthetic and empirical datasets, we compare the statistical power and type I error control of Coal-Miner against state-of-theart AM methods. The simulation conditions reflect a variety of genomic architectures for complex traits and incorporate a range of evolutionary scenarios, each with different evolutionary processes that can generate local genealogical variation. The empirical benchmarks include a large-scale dataset that appeared in a recent high-profile publication. Across the datasets in our study, we find that Coal-Miner consistently offers comparable or typically better statistical power and type I error control compared to the state-of-art methods.\n\nCCS CONCEPTSApplied computing [->] Computational genomics; Computational biology; Molecular sequence analysis; Molecular evolution; Computational genomics; Systems biology; Bioinformatics; Population genetics;\n\nACM Reference formatHussein A. Hejase, Natalie Vande Pol, Gregory M. Bonito, Patrick P. Edger, and Kevin J. Liu. 2017. Coal-Miner: a coalescent-based method for GWA studies of quantitative traits with complex evolutionary origins. In Proceedings of ACM BCB, Boston, MA, 2017 (BCB), 10 pages. DOI: 10.475/123 4

bioinformatics↗
bioRxiv2016

Synthetic miR-16-5p does not act as a reverse transcriptase co-factor to enhance detection of small RNA

Failure to detect low-abundance microRNAs (miRNAs), for example, in circulating plasma, may occur for a variety of reasons, including presence of enzyme inhibitors. Recently, we received the unusual but intriguing suggestion that miR-16-5p acts as a co-factor of reverse transcriptases, facilitating more efficient reverse transcription of miRNAs and thus enhanced detection of low-abundance miRNAs. We tested this hypothesis by incubating reverse transcriptase with several concentrations of synthetic miR-16-5p and then performing stem-loop RT-qPCR with serial dilutions of miRNA osa-MIR168a. Our results do not support a role for miR-16 as a co-factor of reverse transcriptase.

Molecular Biology↗
bioRxiv2018

WFD ecological status indicator shows poor correlation with flow parameters in a large Alpine catchment

Since the implementation of the Water Framework Directive, the ecological status of European running waters has been evaluated using a set of harmonised ecological indicators that should guide conservation and restoration actions. Among these, the restoration of the natural flow regime (ecological flows) is considered indispensable for the achievement of the good ecological status, and yet the sensitivity of the current biological indicators to hydrologic parameters remains understudied. The Italian Star_ICMi well represents other similar WFD indicators; it is a macroinvertebrate-based multimetric index officially adopted to assess the ecological status of running waters at the national level. Recent legislation has also included the Star_ICMi as one of the indicators used to assess and prescribe ecological flows in river reaches regulated by water abstraction. However, the relationship between river hydrology and the Star_ICMi index is so far virtually unknown. Using data from the Trentino - Alto Adige Alpine region, we first assessed the relationship between the Star_ICMi and synthetic descriptors of the physico-chemical (LIMeco) and morphological (MQI) status of respectively 280 and 184 river reaches. Then, we examined the relation between the Star_ICMi and a set of ecologically-relevant hydrologic parameters derived from discharge time-series measured at 21 hydrometric stations, representing both natural and regulated river reaches. Although the Star_ICMi showed significant and linear relationships with the physico-chemical character and, slightly, with the morphological quality of the reaches, its response to flow parameters appeared weak or non-existent when examined with linear models. Mixed quantile regressions allowed the identification of flow parameters that represented limiting factors for macroinvertebrate communities and the associated Star_ICMi scores. In particular, the index showed negative floors where lower values were observed in reaches with large temporal variation in flow magnitude as well as frequent low and high flow events. The modelled quantiles also tracked the transition of the index from acceptable to unacceptable conditions.\n\nThe results suggest that while the central tendency of the Star_ICMi index is not strongly influenced by river flow character, some key flow parameters represent limiting factors that allow the index to reach its lowest values, eventually pushing the site towards unacceptable ecological conditions. The identification of limiting flow parameters can aid the setting of hydrologic thresholds over which ecological impairment is likely to occur. Overall, however, results imply caution is needed in using biological indicator like the Star_ICMi for the quantitative assessment and design of ecological flows.

ecology↗
bioRxiv2017

Outlier detection for improved differential splicing quantification from RNA-Seq experiments withreplicates

A key component in many RNA-Seq based studies is the production of multiple replicates for varying experimental conditions. Such replicates allow to capture underlying biological variability and control for experimental ones. However, during data production researchers often lack clear definitions to what constitutes a \"bad\" replicate which should be discarded and if data from failed replicates is published downstream analysis by groups using this data can be hampered. Here we develop a probability model to weigh a given RNA-Seq experiment as a representative of an experimental condition when performing alternative splicing analysis. Using both synthetic and real life data we demonstrate that this model detects outlier samples which are consistently and significantly different compared to samples from the same condition. Using both synthetic and real life data we perform extensive evaluation of the algorithm in different scenarios involving perturbed samples, mislabeled samples, no-signal groups, and different levels of coverage, and show it compares favorably with current state of the art tools.\n\nAvailabilityProgram and code will be available at majiq.biociphers.org

bioinformatics↗
bioRxiv2017

THiCweed: fast, sensitive motif finding by clustering big data sets

We present THiCweed, a new approach to analyzing transcription factor binding data from high-throughput chromatin-immunoprecipitation-sequencing (ChIP-seq) experiments. THiCweed clusters bound regions based on sequence similarity using a divisive hierarchical clustering approach based on sequence similarity within sliding windows, while exploring both strands. ThiCweed is specially geared towards data containing mixtures of motifs, which present a challenge to traditional motif-finders. Our implementation is significantly faster than standard motif-finding programs, able to process 30,000 peaks in 1-2 hours, on a single CPU core of a desktop computer. On synthetic data containing mixtures of motifs it is as accurate or more accurate than all other tested programs.\n\nTHiCweed performs best with large \"window\" sizes ([&ge;] 50bp), much longer than typical binding sites (7-15 base pairs). On real data it successfully recovers literature motifs, but also uncovers complex sequence characteristics in flanking DNA, variant motifs, and secondary motifs even when they occur in < 5% of the input, all of which appear biologically relevant. We also find recurring sequence patterns across diverse ChIP-seq data sets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif-finding to give new insights into genomic TF binding complexity.

bioinformatics↗
bioRxiv2017

Synthetic DNA templates for the production of in situ hybridization probes

Generating RNA riboprobes for in situ hybridization generally requires the use of plasmids, which must be grown in bacteria, isolated, purified, and linearized prior to in vitro transcription. Here we report a simple method for generating DNA templates for the in vitro transcription of RNA probes from synthetic DNA (IDT gBlocks). Each synthetic DNA template contains sequences corresponding to the target mRNA flanked by bacteriophage promoters. Amplification of the template by a single round of PCR and subsequent in vitro transcription results in production of high quality RNA probes for in situ hybridization.

developmental biology↗
bioRxiv2017

Antibacterial nucleoside-analog inhibitor of bacterial RNA polymerase: pseudouridimycin

There is an urgent need for new antibacterial drugs effective against bacterial pathogens resistant to current drugs1-2. Nucleoside-analog inhibitors (NAIs) of viral nucleotide polymerases have had transformative impact in treatment of HIV3 and HCV4. NAIs of bacterial RNA polymerase (RNAP) potentially could have major impact on treatment of bacterial infection, particularly because functional constraints on substitution of RNAP nucleoside triphosphate (NTP) binding sites4-5 could limit resistance emergence4-5. Here we report the discovery, from microbial extract screening, of an NAI that inhibits bacterial RNAP and exhibits antibacterial activity against a broad spectrum of drug-sensitive and drug-resistant bacterial pathogens: pseudouridimycin (PUM). PUM is a novel microbial natural product consisting of a formamidinylated, N-hydroxylated Gly-Gln dipeptide conjugated to 6'-amino-pseudouridine. PUM potently and selectively inhibits bacterial RNAP in vitro, potently and selectively inhibits bacterial growth in culture, and potently clears infection in a mouse model of Streptococcus pyogenes peritonitis. PUM inhibits RNAP through a binding site on RNAP (the \"i+1\" NTP binding site) and mechanism (competition with UTP for occupancy of the \"i+1\" NTP binding site) that differ from those of the RNAP inhibitor and current antibacterial drug rifampin (Rif). PUM exhibits additive antibacterial activity when co-administered with Rif, exhibits no cross-resistance with Rif, and exhibits a spontaneous resistance rate an order-of-magnitude lower than that of Rif. The results provide the first example of a selective NAI of bacterial RNAP, provide an advanced lead compound for antibacterial drug development, and provide structural information and synthetic routes that enable lead optimization for antibacterial drug development.

molecular biology↗
bioRxiv2017

Therapeutic targeting of PGBD5-induced DNA repair dependency in pediatric solid tumors

Despite intense efforts, the cure rates of childhood and adult solid tumors are not satisfactory. Resistance to intensive chemotherapy is common, and targets for molecular therapies are largely undefined. We have now found that the majority of childhood solid tumors, including rhabdoid tumors, neuroblastoma, medulloblastoma and Ewing sarcoma, express an active DNA transposase PGBD5 that can promote site-specific genomic rearrangements in human cells. Using functional genetic approaches, we found that mouse and human cells deficient in non-homologous end joining (NHEJ) DNA repair cannot tolerate the expression of PGBD5. In a chemical screen of DNA damage signaling inhibitors, we identified AZD6738 as a specific sensitizer of PGBD5-dependent DNA damage and apoptosis. We found that expression of PGBD5, but not its nuclease activity-deficient mutant, was sufficient to induce hypersensitivity to AZD6738. Depletion of endogenous PGBD5 conferred resistance to AZD6738 in human tumor cells. PGBD5-expressing tumor cells accumulated unrepaired DNA damage in response to AZD6738 treatment, and underwent apoptosis in both dividing and G1 phase cells in the absence of immediate DNA replication stress. Accordingly, AZD6738 exhibited nanomolar potency against the majority of neuroblastoma, medulloblastoma, Ewing sarcoma and rhabdoid tumor cells tested, while sparing non-transformed human and mouse embryonic fibroblasts in vitro. Finally, treatment with AZD6738 induced apoptosis and regression of human neuroblastoma and medulloblastoma tumors engrafted in immunodeficient mice in vivo. This effect was potentiated by combined treatment with cisplatin, including significant anti-tumor activity against patient-derived primary neuroblastoma xenografts. These findings delineate a therapeutically actionable synthetic dependency induced in PGBD5-expressing solid tumors.

cancer biology↗
bioRxiv2017

Approximate inference of gene regulatory network models from RNA-Seq time series data

Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and information theoretic methods. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series.

systems biology↗
bioRxiv2018

Compensatory mechanisms render Tcf7l1a dispensable for eye formation despite its requirement in eye field specification

The vertebrate eye originates from the eyefield, a domain of cells specified by a small number of transcription factors. In this study, we show that Tcf7la is one such transcription factor that acts cell-autonomously to specify the eye field in zebrafish. Despite the much reduced eyefield in tcf7l1a mutants, these fish develop normal eyes revealing a striking ability of the eye to recover from a severe early phenotype. This robustness is not mediated through compensation by paralogous genes; instead, the smaller optic vesicle of tcf7l1a mutants shows delayed neurogenesis and continues to grow until it achieves approximately normal size. Although the developing eye is robust to the lack of Tcf7l1a function, it is sensitised to the effects of additional mutations. In support of this, a forward genetic screen identified mutations in hesx1, cct5 and gdf6a, which give synthetically enhanced eye specification or growth phenotypes when in combination with the tcf7l1a mutation.

developmental biology↗
bioRxiv2016

Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing

The number of microbial and metagenomic studies has increased drastically due to advance-ments in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis.\n\nTo address these challenges in human microbiome research, we introduce a novel probabilistic approach to explicitly model overdispersion and sampling zeros by considering the temporal correlation between nearby time points using Gaussian Processes. The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial, and non-parametric regression models on real and synthetic data. We demonstrate that the nonreplicative nature of human gut microbiota studies can be partially overcome by our method with proper experimental design of dense temporal sampling. We also show that different modeling ap-proaches have a strong impact on ecological interpretation of the data, such as stationarity, persistence, and environmental noise models.\n\nA Stan implementation of the proposed method is available under MIT license at https://github.com/tare/GPMicrobiome.

Ecology↗
bioRxiv2018

A Comprehensive Mathematical Model of Surface Electromyography and Force Generation

The purpose of this article is to provide a unified description of a comprehensive mathematical model of surface electromyographic (EMG) measurements and the corresponding force signal in skeletal muscles. The model comprises motor unit pool organization, recruitment and rate coding, intracellular action potential generation and the resulting EMG measurements, as well as the generated muscular force during voluntary isometric contractions. It consolidates and extends the results of several previous publications that proposed mathematical models for the individual model components. A parameterization of the electrical and mechanical components of the model is proposed that ensures a physiologically meaningful EMG-force relation in the simulated signals. Moreover, a novel nonlinear transformation of the excitation model input is proposed, which ensures that the model force output equals the desired target force. Finally, an alternative analytical formulation of the EMG model is proposed, which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink.\n\nNeuromuscular physiology is a vibrant research field that has recently seen exciting advances. Many previous publications have focused on thorough analyses of particular aspects of neuromuscular physiology, yet an integration of the various novel findings into a single, comprehensive model is missing. A consistent description of a complete physiological model as presented here, including thorough justification of model component choices, will facilitate the use of these advanced models in future research. Results of a numerical simulation highlight the models capability to reproduce many physiological effects observed in experimental measurements, and to produce realistic synthetic data that are useful for the validation of signal processing algorithms. The model is based on recent advances in the understanding of muscular physiology and hence also applicable for analyzing the influence of various physiological and measurement setup parameters on the measured force and EMG signals.

systems biology↗
bioRxiv2014

Model checking to assess T-helper cell plasticity

Computational modeling constitutes a crucial step towards the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g. stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity.\n\nIn this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions.\n\nAs a case study, we consider the cellular network regulating the differentiation of T-helper cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of T-helper cell differentiation. We then use symbolic model checking to analyze reachability properties between T-helper subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of T-helper cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific T-helper cell polarizing or reprograming events.

Systems Biology↗