GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs

Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs) leveraging the expressivity of deep neural networks and the computing power of modern heterogeneous hardware. However, its training is still time-consuming, especially in the multi-query and real-time simulation settings, and its parameterization often overly excessive. In this paper, we propose the Generative Pre-Trained PINN (GPT-PINN) to mitigate both challenges in the setting of parametric PDEs. GPT-PINN represents a brand-new meta-learning paradigm for parametric systems. As a network of networks, its outer-/meta-network is hyper-reduced with only one hidden layer having significantly reduced number of neurons. Moreover, its activation function at each hidden neuron is a (full) PINN pre-trained at a judiciously selected system configuration. The meta-network adaptively ``learns'' the parametric dependence of the system and ``grows'' this hidden layer one neuron at a time. In the end, by encompassing a very small number of networks trained at this set of adaptively-selected parameter values, the meta-network is capable of generating surrogate solutions for the parametric system across the entire parameter domain accurately and efficiently

Computational prediction of functional similarity of CRMs

EThOS - Electronic Theses Online ServiceWarwick Systems Biology CentreHuman frontier science programGBUnited Kingdo

Effect of calcium alginate coating on shelf life of frozen lamb muscle

Considering the potential benefits of edible coatings and films for storage of food materials, effect of edible calcium alginate film on shelf life of frozen lamb muscles was studied in the present research. Microbial analyses including total microorganisms count and psychrophilic bacteria count and chemical analyses such as total volatile nitrogen (TVN) and moisture content determination were performed. Coated and uncoated samples had not statistically significant difference in total microbial count, total volatile nitrogen level and moisture content. However, there was statistically significant difference between the coated and uncoated samples in terms of psychrophilic bacteria count (p<0.05). Considering the role of psychrophilic bacteria in meat spoilage, results of the current research confirmed that calcium alginate films may be to some extent effective in shelf life extension of frozen lamb muscle.

Group A streptococcus induces CD1a-autoreactive T cells and promotes psoriatic inflammation

Group A Streptococcus (GAS) infection is associated with multiple clinical sequelae, including different subtypes of psoriasis. Such post-streptococcal disorders have been long known but are largely unexplained. CD1a is expressed at constitutively high levels by Langerhans cells and presents lipid antigens to T cells, but the potential relevance to GAS infection has not been studied. Here, we investigated whether GAS-responsive CD1a-restricted T cells contribute to the pathogenesis of psoriasis. Healthy individuals had high frequencies of circulating and cutaneous GAS-responsive CD4+ and CD8+ T cells with rapid effector functions, including the production of interleukin-22 (IL-22). Human skin and blood single-cell CITE-seq analyses of IL-22-producing T cells showed a type 17 signature with proliferative potential, whereas IFN-γ-producing T cells displayed cytotoxic T lymphocyte characteristics. Furthermore, individuals with psoriasis had significantly higher frequencies of circulating GAS-reactive T cells, enriched for markers of activation, cytolytic potential, and tissue association. In addition to responding to GAS, subsets of expanded GAS-reactive T cell clones/lines were found to be autoreactive, which included the recognition of the self-lipid antigen lysophosphatidylcholine. CD8+ T cell clones/lines produced cytolytic mediators and lysed infected CD1a-expressing cells. Furthermore, we established cutaneous models of GAS infection in a humanized CD1a transgenic mouse model and identified enhanced and prolonged local and systemic inflammation, with resolution through a psoriasis-like phenotype. Together, these findings link GAS infection to the CD1a pathway and show that GAS infection promotes the proliferation and activation of CD1a-autoreactive T cells, with relevance to post-streptococcal disease, including the pathogenesis and treatment of psoriasis

Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs

Broadly, computational approaches for ortholog assignment is a three steps process: (i) identify all putative homologs between the genomes, (ii) identify gene anchors and (iii) link anchors to identify best gene matches given their order and context. In this article, we engineer two methods to improve two important aspects of this pipeline [specifically steps (ii) and (iii)]. First, computing sequence similarity data [step (i)] is a computationally intensive task for large sequence sets, creating a bottleneck in the ortholog assignment pipeline. We have designed a fast and highly scalable sort-join method (afree) based on k-mer counts to rapidly compare all pairs of sequences in a large protein sequence set to identify putative homologs. Second, availability of complex genomes containing large gene families with prevalence of complex evolutionary events, such as duplications, has made the task of assigning orthologs and co-orthologs difficult. Here, we have developed an iterative graph matching strategy where at each iteration the best gene assignments are identified resulting in a set of orthologs and co-orthologs. We find that the afree algorithm is faster than existing methods and maintains high accuracy in identifying similar genes. The iterative graph matching strategy also showed high accuracy in identifying complex gene relationships. Standalone afree available from http://vbc.med.monash.edu.au/∼kmahmood/afree. EGM2, complete ortholog assignment pipeline (including afree and the iterative graph matching method) available from http://vbc.med.monash.edu.au/∼kmahmood/EGM2

Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape

We describe protein interaction quantitation (PIQ), a computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through the use of machine-learning techniques, PIQ identified binding sites for >700 TFs from one DNase I hypersensitivity analysis followed by sequencing (DNase-seq) experiment with accuracy comparable to that of chromatin immunoprecipitation followed by sequencing (ChIP-seq). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into prepancreatic and intestinal endoderm. We identified 120 and experimentally validated eight 'pioneer' TF families that dynamically open chromatin. Four pioneer TF families only opened chromatin in one direction from their motifs. Furthermore, we identified 'settler' TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and nondirectional pioneer activity shapes the chromatin landscape for population by settler TFs.National Institutes of Health (U.S.) (Common Fund 5UL1DE019581)National Institutes of Health (U.S.) (Common Fund RL1DE019021)National Institutes of Health (U.S.) (Common Fund 5TL1EB008540)National Institutes of Health (U.S.) (Grant 1U01HG007037)National Institutes of Health (U.S.) (Grant 5P01NS055923

Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework

Transcription Factors (TFs) bind to DNA and control activity of target genes. Here, we present ChIPanalyser, a user-friendly, versatile and powerful R/Bioconductor package predicting and modelling the binding of TFs to DNA. ChIPanalyser performs similarly to state-of-the-art tools, but is an explainable model and provides biological insights into binding mechanisms of TFs. We focused on investigating the binding mechanisms of three TFs that are known architectural proteins CTCF, BEAF-32 and su(Hw) in three Drosophila cell lines (BG3, Kc167 and S2). While CTCF preferentially binds only to a subset of high affinity sites located mainly in open chromatin, BEAF-32 binds to most of its high affinity binding sites available in open chromatin. In contrast, su(Hw) binds to both open chromatin and also partially closed chromatin. Most importantly, differences in TF binding profiles between cell lines for these TFs are mainly driven by differences in DNA accessibility and not by differences in TF concentrations between cell lines. Finally, we investigated binding of Hox TFs in Drosophila and found that Ubx binds only in open chromatin, while Abd-B and Dfd are capable to bind in both open and partially closed chromatin. Overall, our results show that TFs display different binding mechanisms and that our model is able to recapitulate their specific binding behaviour

Genome organization and chromatin analysis identify transcriptional downregulation of insulin-like growth factor signaling as a hallmark of aging in developing B cells.

BACKGROUND: Aging is characterized by loss of function of the adaptive immune system, but the underlying causes are poorly understood. To assess the molecular effects of aging on B cell development, we profiled gene expression and chromatin features genome-wide, including histone modifications and chromosome conformation, in bone marrow pro-B and pre-B cells from young and aged mice. RESULTS: Our analysis reveals that the expression levels of most genes are generally preserved in B cell precursors isolated from aged compared with young mice. Nonetheless, age-specific expression changes are observed at numerous genes, including microRNA encoding genes. Importantly, these changes are underpinned by multi-layered alterations in chromatin structure, including chromatin accessibility, histone modifications, long-range promoter interactions, and nuclear compartmentalization. Previous work has shown that differentiation is linked to changes in promoter-regulatory element interactions. We find that aging in B cell precursors is accompanied by rewiring of such interactions. We identify transcriptional downregulation of components of the insulin-like growth factor signaling pathway, in particular downregulation of Irs1 and upregulation of Let-7 microRNA expression, as a signature of the aged phenotype. These changes in expression are associated with specific alterations in H3K27me3 occupancy, suggesting that Polycomb-mediated repression plays a role in precursor B cell aging. CONCLUSIONS: Changes in chromatin and 3D genome organization play an important role in shaping the altered gene expression profile of aged precursor B cells. Components of the insulin-like growth factor signaling pathways are key targets of epigenetic regulation in aging in bone marrow B cell precursors

Computational prediction of functional similarity of CRMs

Transcriptional regulation of genes is fundamental to all living organisms. The spatial, temporal and condition-specific expression levels of genes are in part determined by inherited regulatory codes in non-coding regions of the DNA. A large set of methods have been proposed to detect conserved regions of regulatory DNA by means of sequence alignments. However, it has become clear that some regulatory regions do not show statistically significant alignments even in the presence of functional conservation. Therefore, detecting and characterising elusive regulatory codes remains a challenging problem. In this thesis we develop and validate a novel computational alignment free model for detection of functional similarity of regulatory sequences. We show that our model can detect functional links between pairs of sequences that do not align with a significant score. We apply the model to a) detect enhancers within the same genome that are likely to have similar functions and b) to detect functionally conserved enhancer regions in orthologous genomes. Our method finds regulatory codes that are common to groups of similar enhancers and consistent with previous biological knowledge. The inputs for our model are two sequences that we wish to compare in terms of their functional similarity as well as a set of transcription factor motifs. The mathematical framework of our model is built on two main components: In the first model component, each sequence is mapped to a vector of estimated occupancy levels for all motifs. These vectors are representing which motifs at what multiplicity and specificity are present in each sequence. In the second model component, a statistical approach is established where we first estimate a probability distribution of motif occupancy levels for sequences that function similar to the template sequence. We then compute a statistical similarity score to evaluate if the sequences are more similar to each other than to random background sequences. Two applications of this model are presented: First it is applied to a set of experimentally validated non-alignable enhancers from D. melanogaster. We show that: • Our model can detect statistical links between these enhancers, • Weak binding sites can make a strong contribution to sequence similarity, • Our model treats statistically significant presence and absence of motifs symmetrically. Similarity of sequences, therefore, can be based on a combination of the two. We show examples of motifs making contributions to sequence similarity through their absence. • Using our model, we can create a network of similarities among the fly enhancers. Groups of enhancers in this network show common regulatory codes. One of these regulatory codes is strongly supported by existing experimental data. In the second application of our model we predict functional subregions of a known D. melanogaster enhancer. To achieve this, we first show that the model can detect the orthology of this enhancer between 10 Drosophila species. We then demonstrate how this statistical link can be used to predict functional subregions within this enhancer