Mathematical modelling plant signalling networks

During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting

Dynamic cross-talk analysis among TNF-R, TLR-4 and IL-1R signalings in TNFalpha-induced inflammatory responses

[[abstract]]Background Development in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways. Methods In this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFα-induced inflammation. Next, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R). Results The highly ranked cross-talks which are functionally relevant to the TNFα pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed. Conclusions A systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.[[fileno]]2030106010244[[department]]電機工程學

Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection

Integrative Identification of Arabidopsis Mitochondrial Proteome and Its Function Exploitation through Protein Interaction Network

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome

ClockOME: searching for oscillatory genes in early vertebrate development

Embryo development is a dynamic process regulated in space and time. Cells must integrate biochemical and mechanical signals to generate fully functional organisms, where oscillatory gene expression plays a key role. The embryo molecular clock (EMC) is the best known genetic oscillator active in embryo segmentation, involving genes from the Notch, FGF, and WNT pathways. However, the list of cyclic genes is still incomplete mostly due to the challenges involved with studying periodic systems. Recently, such studies have become more feasible with the development of pseudo-time ordering algorithms that search for candidate oscillatory genes using large transcriptomics datasets sampled without explicit time measurements. This study aims at finding candidate oscillatory genes - ClockOME - active in early chick embryo development. Two Gallus gallus microarray transcriptomics datasets from Presomitic mesoderm (PSM), and one dataset from limb segmentation were gathered from GEO and ArrayExpress. To normalize these data from different experiments, an RData package - FrozenChicken - was developed to apply a frozen Robust MultiArray (fRMA) normalization to the data. Next the datasets were processed with Oscope (a pseudo-time ordering algorithm) to search for candidate periodic genes clustered by similar oscillatory behaviour. The clusters of predicted oscillators were then subject to functional enrichment and interaction network analyses to highlight the biological functions associated with these genes. Oscope predicted three clusters of oscillators: two in PSM (106 and 32 genes), and one in Limb (162 genes). Overall, the genes are associated with regulatory, morphological, and developmental processes. Mesp2, a gene involved with the EMC, was found in this dataset, validating the approach, however, the majority of genes are novel oscillatory candidates, associated with chromatin and transcriptional regulation, as well as protein and oxygen metabolism. The list of candidate oscillators represents a valuable resource for guided experimental validation to discover additional members of the chick EMC. Six genes have been proposed for high-priority experimental validation: SRC, PTCH1, NOTCH2, YAP1, KDR, CTR9.O desenvolvimento embrionário é um processo dinâmico que envolve alterações moleculares no espaço e no tempo. As células embrionárias são constantemente expostas a estímulos bioquímicos e mecânicos, e respondem ao ambiente em que se encontram alterando o seu programa genético. Quando corretamente integradas, estas respostas celulares culminam com o desenvolvimento bem-sucedido de um organismo funcional. Assim, a embriogénese envolve processos moleculares estritamente regulados, sendo a expressão oscilatória de genes uma das formas possíveis para a regulação do comportamento das células ao longo do tempo. O relógio molecular embrionário é um conhecido oscilador genético, e está envolvido na segmentação do tecido paraxial embrionário. O conceito de relógio molecular foi inicialmente proposto em 1976 por Cooke e Zeeman, ao qual chamaram o modelo Clock and Wavefront (Relógio e Frente de Onda)1. Este modelo foi concebido para descrever teoricamente a formação rítmica de sómitos em ambos os lados da mesoderme paraxial (PSM) nos vertebrados, e baseia-se na existência de osciladores genéticos que regulam esse processo de segmentação da PSM ao longo do tempo. Para além do relógio, como diz o nome, o modelo inclui a existência de uma frente de onda, que determina espacialmente o comportamento das células presentes na mesoderme pré-somítica (PSM). Assim, os dois mecanismos guiam a diferenciação das células da PSM, que consequentemente sofrem transformações genéticas que precedem a formação dos sómitos. A base deste relógio molecular consiste na expressão periódica de genes que fazem parte das vias moleculares Notch, FGF e WNT. Contudo, a lista de genes envolvidos no relógio embrionário ainda não se encontra completa, facto este que se deve principalmente às dificuldades experimentais relacionadas com o estudo de sistemas periódicos quando não se conhece de antemão a periodicidade/ritmo da expressão dos genes envolvidos. Com o advento de novas técnicas de transcriptómica que permitem o estudo dos valores de expressão de todos os genes simultaneamente, nomeadamente usando Microarrays, ou mais recentemente através de métodos de sequenciação, como RNA-sequencing ou Single-Cell RNA-sequencing, surge a oportunidade de procurar alargar a lista de genes com expressão oscilatória. Porém, estes métodos implicam a extração do RNA das células amostradas resultando na morte celular. Assim, este processamento inviabiliza o estudo das mesmas células ao longo do tempo, originando dados moleculares estáticos, isto é, os níveis de expressão obtidos representam uma única amostra temporal. Para o estudo de processos periódicos, seria então necessário fazer uma série temporal amostrando diferentes indivíduos ao longo do tempo de desenvolvimento, aumentando grandemente o número de amostras biológicas necessárias para resolver o ciclo de oscilação para cada gene estudado. Assim, sem informação temporal medida explicitamente, a expressão oscilatória de genes pode apenas ser estudada usando modelos matemáticos apropriados, nomeadamente através da aplicação de algoritmos de ordenação pseudo-temporal. Estes métodos ordenam as amostras ao longo do tempo de uma oscilação de forma a obter o padrão do comportamento cíclico para todos os genes cuja expressão oscila concomitantemente. Torna-se assim possível, bioinformaticamente, inferir o potencial oscilatório de genes medidos por estas técnicas de transcriptómica, sem informação temporal explícita. Deste modo, o objetivo deste estudo é encontrar novos genes oscilatórios, a que coletivamente chamamos ClockOME, que estão ativos durante as primeiras etapas do desenvolvimento embrionário (somitogénese) da galinha, nos tecidos da mesoderme présomítica (PSM), e no membro superior (Limb); tecidos estes onde o relógio molecular foi descrito, atuando como regulador temporal das alterações genéticas subjacentes. Para tal, recolheu-se 3 conjuntos de dados (datasets) de transcriptómica obtidos por microarray de dois repositórios de dados públicos: GEO (da instituição americana NCBI) e ArrayExpress (da instituição europeia EMBL-EBI). Dois datasets continham dados de mesoderme paraxial (PSM) – tecido onde ocorre a somitogénese; e um dataset de dados de obtidos do membro superior do embrião de galinha. Com o objetivo de normalizar os três datasets de forma a torná-los comparáveis (uma vez que são oriundos de processos experimentais diferentes), foi desenvolvido um pacote de R denominado “FrozenChicken: Promoting the meta-analysis of chicken microarray data” (publicado em 2021) (https://doi.org/10.1101/2021.02.25.432894). Este pacote contém dados sumarizados de 472 datasets de microarrays de embriões de galinha, tornando possível a normalização por fRMA (frozen Robust MultiArray) de microarrays de Gallus gallus. Após normalização e controlo de qualidade dos valores de expressão genética, os dados da PSM e do membro foram processados com o Oscope (algoritmo de ordenação pseudo-temporal), com o propósito de prever genes oscilatórios. Este algoritmo avalia todas as combinações de pares de genes, agrupando aqueles que apresentem padrões de expressão semelhantes, ou seja, cujos valores de expressão ao longo das amostras seguem trajetórias semelhantes, indiciando um período de oscilação potencialmente semelhante. Os clusters de genes previstos pelo Oscope foram posteriormente submetidos a uma análise de enriquecimento funcional e a uma análise de interações funcionais, com o intuito de perceber o seu potencial papel biológico, e funções moleculares subjacentes. O Oscope reportou três listas de genes potencialmente oscilatórios: dois grupos foram encontrados a partir dos dados da PSM (com 106 e 32 genes cada) e o terceiro grupo de 162 genes foi encontrado nos dados do membro superior. No total, a lista de genes que denominamos ClockOME é composta por 296 genes potencialmente oscilatórios, envolvidos em diversos mecanismos regulatórios importantes para o desenvolvimento embrionário e para a morfogénese. A maioria dos genes presentes nesta lista não estão descritos na literatura como sendo oscilatórios (novel candidates), representando, portanto, uma mais-valia para a comunidade científica que estuda o relógio molecular embrionário. Estes genes parecem estar associados a funções como remodelação da cromatina, regulação da transcrição, metabolismo proteico e metabolismo do oxigénio, sendo, portanto, bons candidatos para futura validação experimental. Notavelmente, o Oscope identificou com sucesso o Mesp2, um gene oscilatório bem descrito na literatura, mostrando assim a validade e o potencial desta abordagem teórica. Em suma, este trabalho produziu uma lista de 296 genes potencialmente oscilatórios. Com base na sua novidade e na função molecular anotada, foi proposta uma lista de seis genes candidatos de particular relevância para validação experimental no futuro próximo, nomeadamente: SRC, PTCH1, NOTCH2, YAP1, KDR, CTR9. Assim, as listas resultantes do trabalho desta tese poderão agora guiar futuras experiências laboratoriais capazes de adicionar novos interactores moleculares ao atual modelo do relógio molecular embrionário

Statistical physics of information processing by cells

This thesis provides a physics account of the ability of cells to integrate environmental information to make complex decisions, a process commonly known as signaling. It strives to address the following questions: (i) How do cells relate the state of the environment (e.g. presence/absence of specific molecules) to a desired response such as gene expression? (ii) How can cells robustly transfer information? (iii) Is there a biophysical limit to a cells' ability to process information? (iv) Can we use the answers to the above questions to formulate biophysical principles that inform us about the evolution of signaling? Throughout, I borrow techniques from non-equilibrium statistical physics, statistical learning theory, information theory and information geometry to construct biophysical models capable of making quantitative experimental predictions. Finally, I address the connection of energy expenditure and biological efficiency by zeroing in on a process unique to eukaryotic cells-- nuclear transport. The thesis concludes with a discussion of our theory and its implications for synthetic biology

Exploration of large molecular datasets using global gene networks : computational methods and tools

Defining gene expression profiles and mapping complex interactions between molecular regulators and proteins is a key for understanding biological processes and the functional properties of cells, which is therefore, the focus on numerous experimental studies. Small-scale biochemical analyses deliver high-quality data, but lack coverage, whereas high throughput sequencing reveals thousands of interactions which can be error-prone and require proper computational methods to discover true relations. Furthermore, all these approaches usually focus on one type of interaction at a time. This makes experimental mapping of the genome-wide network a cost and time-intensive procedure. In the first part of the thesis, I present the developed network analysis tools for exploring large- scale datasets in the context of a global network of functional coupling. Paper I introduces NEArender, a method for performing pathway analysis and determines the relations between gene sets using a global network. Traditionally, pathway analysis did not consider network relations, thereby covering a minor part of the whole picture. Placing the gene sets in the context of a network provides additional information for pathway analysis, which reveals a more comprehensive picture. Paper II presents EviNet, a user-friendly web interface for using NEArender algorithm. The user can either input gene lists or manage and integrate highly complex experimental designs via the interactive Venn diagram-based interface. The web resource provides access to biological networks and pathways from multiple public or users’ own resources. The analysis typically takes seconds or minutes, and the results are presented in a graphic and tabular format. Paper III describes NEAmarker, a method to predict anti-cancer drug targets from enrichment scores calculated by NEArender, thus presenting a practical usage of network enrichment tool. The method can integrate data from multiple omics platforms to model drug sensitivity with enrichment variables. In parallel, alternative methods for pathway enrichment analysis were benchmarked in the paper. The second part of the thesis is focused on identifying spatial and temporal mechanisms that govern the formation of neural cell diversity in the developing brain. High-throughput platforms for RNA- and ChIP-sequencing were applied to provide data for studying the underlying biological hypothesis at the genome-wide scale. In Paper IV, I defined the role of the transcription factor Foxa2 during the specification and differentiation of floor plate cells of the ventral neural tube. By RNA-seq analyses of Foxa2-/- cells, a large set of candidate genes involved in floor plate differentiation were identified. Analysis of Foxa2 ChIP-seq dataset suggested that Foxa2 directly regulated more than 250 genes expressed by the floor plate and identified Rfx4 and Ascl1 as co-regulators of many floor plate genes. Experimental studies suggested a cooperative activator function for Foxa2 and Rfx4 and a suppressive role for Ascl1 in spatially constraining floor plate induction. Paper V addresses how time is measured during sequential specification of neurons from multipotent progenitor cells during the development of ventral hindbrain. An underlying timer circuitry which leads to the sequential generation of motor neurons and serotonergic neurons has been identified by integrating experimental and computational data modeling