Stochastic Modeling and Inference of Large-scale Gene Regulatory Networks

Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GRN dynamics are usually investigated via stochastic models since molecular interactions are basically discrete and stochastic processes. However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. This thesis mainly focuses on developing methodologies for large-scale GRN analyses. It includes applications of a stochastic process theory called G-networks and a reverse engineering technique for large-scale GRNs. Additionally a series of bioinformatics techniques was applied in brain tumor data to detect disease candidate genes along with their large-scale GRNs. The proposed techniques such as stochastic modeling (bottom-up) and reverse engineering (top-down) could provide a systematic view of a complex system and an efficient guideline to identify candidate genes or pathways triggering a specific phenotype of a cell. As further work, the combinatorial use of the modeling and reverse engineering approaches would be helpful in obtaining a reliable mathematical model and even in developing a synthetic biological system

91st Annual Meeting of the Virginia Academy of Science: Proceedings

Proceedings of the 91st Annual Meeting of the Virginia Academy of Science, held at Virginia Polytechnic Institute and State University, May 22-24, 2013

Advances in quantitative microscopy

Microscopy allows us to peer into the complex deeply shrouded world that the cells of our body grow and thrive in. With the emergence of automated digital microscopes and software for anlysing and processing the large numbers of image that they produce; quantitative microscopy approaches are now allowing us to answer ever larger and more complex biological questions. In this thesis I explore two trends. Firstly, that of using quantitative microscopy for performing unbiased screens, the advances made here include developing strategies to handle imaging data captured from physiological models, and unsupervised analysis screening data to derive unbiased biological insights. Secondly, I develop software for analysing live cell imaging data, that can now be captured at greater rates than ever before and use this to help answer key questions covering the biology of how cells make the decision to arrest or proliferate in response to DNA damage. Together this thesis represents a view of the current state of the art in high-throughput quantitative microscopy and details where the field is heading as machine learning approaches become ever more sophisticated.Open Acces

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematics and physics backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges, and includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and classical systems typically studied in non-equilibrium statistical and quantum mechanics.Comment: 73 pages, 12 figure

Dynamical models of the mammalian target of rapamycin network in ageing

Phd ThesisThe mammalian Target of Rapamycin (mTOR)kinase is a central regulator of cellular growth and metabolism and plays an important role in ageing and age- related diseases. The increase of invitro data collected to extend our knowledge on its regulation, and consequently improve drug intervention,has highlighted the complexity of the mTOR network. This complexity is also aggravated by the intrinsic time-dependent nature of cellular regulatory network cross-talks and feedbacks. Systems biology constitutes a powerful tool for mathematically for- malising biological networks and investigating such dynamical properties. The present work discusses the development of three dynamical models of the mTOR network. The first aimed at the analysis of the current literature-based hypotheses of mTOR Complex2(mTORC2)regulation. For each hypothesis, the model predicted specific differential dynamics which were systematically tested by invitro experiments. Surprisingly, nocurrent hypothesis could explain the data and a new hypothesis of mTORC2 activation was proposed.The second model extended the previous one with an AMPK module. In this study AMPK was reported to be activated by insulin. Using a hypothesis ranking approach based on model goodness-of-fit, AMPK activity was insilico predicted and in vitro tested to be activated by the insulin receptor substrate(IRS).Finally,the last model linked mTOR with the oxidative stress response, mitochondrial reg- ulation, DNA damage and FoxO transcription factors. This work provided the characterisation of a dynamical mechanism to explain the state transition from normal to senescent cells and their reversibility of the senescentphenotype.European Council 6FP NoE LifeSpan, School of the Faculty of Medical Sciences, Newcastle Universit

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Cancer clocks in tumourigenesis : the p53 pathway and beyond

Circadian rhythms regulate a vast array of physiological and cellular processes, as well as the hormonal milieu, to keep our cells synchronised to the light-dark cycle. Epidemiologic studies have implicated circadian disruption in the development of breast and other cancers, and numerous clock genes are dysregulated in human tumours. Here we review the evidence that circadian rhythms, when altered at the molecular level, influence cancer growth. We also note some common pitfalls in circadian-cancer research and how they might be avoided to maximise comparable results and minimise misleading data. Studies of circadian gene mutant mice, and human cancer models in vitro and in vivo, demonstrate that clock genes can impact tumourigenesis. Clock genes influence important cancer related pathways, ranging from p53-mediated apoptosis to cell cycle progression. Confusingly, clock dysfunction can be both pro- or anti- tumourigenic in a model and cell type specific manner. Due to this duality, there is no canonical mechanism for clock interaction with tumourigenic pathways. To understand the role of the circadian clock in patients’ tumours requires analysis of the molecular clock status compared to healthy tissue. Novel mathematical approaches are under development, but this remains largely aspirational, and is hampered by a lack of temporal information in publicly available datasets. Current evidence broadly supports the notion that the circadian clock is important for cancer biology. More work is necessary to develop an overarching model of this connection. Future studies would do well to analyse the clock network in addition to alterations in single clock genes

Insulin-Like Growth Factors in Development, Cancers and Aging

This Special Issue of Cells on “Insulin-Like Growth Factors in Development, Cancers and Aging” provides a collection of modern articles dealing with the role of insulin-like growth factors (IGF1) in cancer biology, aging and development. Featured articles explore basic and clinical aspects of the IGF1 system, including post-genomic analyses as well as novel approaches to target the IGF1 receptor (IGF1R) in oncology. The present Special Issue highlights some of the most important topics in the broad area of IGF research, including the role of IGF1 in aging and longevity, attempts to target the IGF1 axis in oncology, the role of IGF-binding proteins, structural aspects of IGF-II, etc. We trust that this assembly of articles will be of great help to students, basic researchers and practitioners

Intégration de signaux au niveau de la chromatine et perturbations de la ribogénèse pour une suppression tumorale efficace

Environ 30% des cancers humains ont une mutation gain de fonction dans l’oncogène RAS, menant à une prolifération cellulaire accrue et une expansion clonale. Cependant, il est bien établi qu’une hyperactivation soutenue de cette voie mène au phénotype inverse, soit la sénescence cellulaire, définie par un arrêt stable de la prolifération. Ce destin cellulaire caractérise les lésions bénignes et la progression vers une tumeur maligne est associée à son contournement. Toutefois, les mécanismes moléculaires permettant aux cellules de distinguer entre une signalisation normale et oncogénique par RAS afin de les engager vers la sénescence plutôt que la prolifération demeurent inconnus. Ainsi, l’hypothèse à la base de ces travaux est que la décision d’engagement vers la sénescence implique une reprogrammation transcriptionnelle qui précède l’établissement des phénotypes caractéristiques de la sénescence, tel le phénotype sécrétoire (SASP) (Article 1). Nous avons ainsi identifié un point de restriction (SeRP) critique pour l’engagement des cellules vers la sénescence en réponse à l’oncogène HRASG12V. Ce SeRP intègre l'intensité et la durée du stress oncogénique, tout en gardant une mémoire des stress antérieurs, en modulant l’accessibilité à la chromatine via l’induction d’un réseau auto-régulé de facteurs de transcription comprenant notamment ETV4 et RUNX1 (Article 2). Notre modèle actuel nous porte à croire que cette augmentation d’accessibilité à la chromatine impliquerait principalement une décondensation de l’hétérochromatine périnucléolaire. Ceci mènerait à l’induction du SASP et aux défauts de ribogénèse observés dans la sénescence. Nous montrons d’ailleurs via la génération d’un modèle murin transgénique que l’induction de tels défauts de ribogénèse à l’échelle systémique mène à un phénotype de vieillissement prématuré suggérant une sénescence des cellules souches (Article 3). Les cellules souches ayant des niveaux particulièrement élevés de ribogénèse et étant très sensibles à des altérations de leur niche tels que l’inflammation chronique, nous pensons que, de manière fortuite, ce modèle reproduit en quelque sorte les conséquences du SeRP. En somme, l’ensemble des travaux présentés dans cette thèse permettent une meilleure compréhension des mécanismes moléculaires régulant l’engagement vers la sénescence. À termes, ces nouvelles notions permettraient de concevoir des stratégies thérapeutiques permettant de faire pencher la balance vers la sénescence dans un contexte de cancers mutés en RAS.Around 30% of human cancers have a gain-of-function mutation in the RAS oncogene, resulting in increased cell proliferation and clonal expansion. However, it is well established that a sustained hyperactivation of this same pathway leads instead to the opposite phenotype, namely cellular senescence, which is defined by a stable proliferation arrest. This cell fate characterizes benign lesions and progression to malignancy is associated with its bypass. However, the molecular mechanisms allowing cells to distinguish between normal and oncogenic RAS signaling in order to commit them to senescence rather than proliferation remain unknown. Thus, the hypothesis underlying the present work is that this decision to commit to senescence involves a transcriptional reprogramming that precedes the establishment of the senescence-characteristic phenotypes such as the secretory phenotype (Article 1). We have thus identified a restriction point (SeRP) critical for the commitment of cells towards senescence in response to HRASG12V oncogene. This SeRP integrates both the intensity and duration of oncogenic stress while keeping a memory of previous stresses. This integration is achieved by modulating chromatin accessibility via the induction of a self-regulated network of transcription factors including among others ETV4 and RUNX1 (Article 2). Our current model leads us to believe that this increase in chromatin accessibility during the SeRP would mainly involve decondensation of perinucleolar heterochromatin. This would lead to the induction of the pro-inflammatory secretome of senescent cells (SASP) and the ribogenesis defects observed in senescence. Besides, we show via the generation of a transgenic mouse model that the induction of such ribogenesis defects at the systemic scale leads to a premature aging phenotype suggesting stem cells senescence (Article 3). Stem cells having particularly high levels of ribogenesis and being very sensitive to alterations of their niche such as chronic inflammation, we believe that serendipitously, this model somehow reproduces the consequences of the SeRP. In short, all the work presented in this thesis allows for a better understanding of the molecular mechanisms regulating the commitment to senescence. Ultimately, these new notions would allow to design therapeutic strategies to tip the balance towards senescence in the context of RAS-mutated cancers