A computational modelling of cellular and supra-cellular networks to unravel the control of EMT

"Over the last decade, Epithelial-to-Mesenchymal Transition (EMT) has gained the attention of cancer researchers due to its potential to promote cancer migration and metastasis. However, the complexity of EMT intertwined regulation and the involvement of multiple signals in the tumour microenvironment have been limiting the understanding of how this process can be controlled. Cell-cell adhesion and focal adhesion dynamics are two critical properties that change during EMT, which provide a simple way to characterize distinct modes of cancer migration. Therefore, the main focus of this thesis is to provide a framework to predict critical microenvironment and de-regulations in cancer that drive interconversion between adhesion phenotypes, accounting for main microenvironment signals and signalling pathways in EMT. Here, we address this issue through a systems approach using the logical modelling framework to generate new testable predictions for the field.(...)"Instituto Gulbenkian de Ciência (FCG-IGC

Towards a comprehensive modeling framework for studying glucose repression in yeast

The yeast Saccharomyces cerevisiae is an important model organism for human health and for industry applications as a cell factory. For both purposes, it has been an important organism for studying glucose repression. Glucose sensing and signaling is a complex biological system, where the SNF1 pathway is the main pathway responsible for glucose repression. However, it is highly interconnected with the cAMP-PKA, Snf3-Rgt2 and TOR pathways. To handle the complexity, mathematical modeling has successfully aided in elucidating the structure, mechanism, and dynamics of the pathway. In this thesis, I aim to elucidate what the effect of the interconnection of glucose repression with sensory and metabolic pathways in yeast is, specifically, how crosstalk influences the signaling cascade; what the main effects of nutrient signaling on the metabolism are and how those are affected by intrinsic stress, such as damage accumulation. Here, I have addressed these questions by developing new frameworks for mathematical modeling. A vector based method for Boolean representation of complex signaling events is presented. The method reduces the amount of necessary nodes and eases the interpretation of the Boolean states by separating different events that could alter the activity of a protein. This method was used to study how crosstalk influences the signaling cascade.To be able to represent a diverse biological network using methods suitable for respective pathways, we also developed two hybrid models. The first is demonstrating a framework to connect signaling pathways with metabolic networks, enabling the study of long-term signaling effects on the metabolism. The second hybrid model is demonstrating a framework to connect models of signaling and metabolism to growth and damage accumulation, enabling the study of how the long-term signaling effects on the metabolism influence the lifespan. This thesis represents a step towards comprehensive models of glucose repression. In addition, the methods and frameworks in this thesis can be applied and extended to other signaling pathways

Characterising the plasticity of cutaneous myeloid cells in graft-versus-host disease

The skin is an important barrier to the external environment. Within the skin, immune cells interact to maintain a healthy protective environment in the presence of daily challenges. This requires the induction of tolerance to innocuous insults, but activation of adaptive immunity upon infection with pathogens. Within healthy skin, myeloid cells such as monocytes, macrophages, dendritic cells and Langerhans cells (LC) cooperate to maintain this balance. However, we know little about how disease may impact on these dynamic cell populations and their control of skin immunity. Haematopoietic stem cell transplantation is a curative treatment for some blood cancers. However, the beneficial anti-tumour effect is often associated with the pathophysiology of graft-versus-host disease (GVHD). This thesis is focused on understanding how the skin myeloid compartment is altered after GVHD, and the consequences of these changes on skin immunity. We have used a murine model of sub-lethal GVHD to investigate the developmental and functional impact of skin pathology on different myeloid cell populations. We have characterised the immune environment of the skin during and after GVHD and identified lasting changes to the myeloid compartment. In particular, pathology resulted in the recruitment of blood monocytes with the plasticity to differentiate into different cell types in the skin environment. Alterations to dermal myeloid cells led to defects in cutaneous immunity, including a breakdown in peripheral tolerance and protective barrier immunity. We demonstrated a profound loss of regulatory T cell function in situ and identified monocyte-derived IL-6 as a potential mediator of loss of function. We have further utilised transcriptional analyses with a reductionist in vitro culture model to infer information about myeloid cell differentiation during GVHD. The approach has identified proliferation of myeloid precursors as a critical and unreported step in LC differentiation, and highlighted the importance of interleukin (IL)-34 in the generation of a persistent LC network following injury. Together, this work has demonstrated the plasticity of myeloid cells within the skin after disease and the potential long-term consequences for skin immunity in patients who have recovered from GVHD

A MAPK-Driven Feedback Loop Suppresses Rac Activity to Promote RhoA-Driven Cancer Cell Invasion

Data Availability: All relevant data are within the paper and its Supporting Information files.Supporting Information is available online at: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004909#sec024 .Cell migration in 3D microenvironments is fundamental to development, homeostasis and the pathobiology of diseases such as cancer. Rab-coupling protein (RCP) dependent co-trafficking of α5β1 and EGFR1 promotes cancer cell invasion into fibronectin (FN) containing extracellular matrix (ECM), by potentiating EGFR1 signalling at the front of invasive cells. This promotes a switch in RhoGTPase signalling to inhibit Rac1 and activate a RhoA-ROCK-Formin homology domain-containing 3 (FHOD3) pathway and generate filopodial actin-spike protrusions which drive invasion. To further understand the signalling network that drives RCP-driven invasive migration, we generated a Boolean logical model based on existing network pathways/models, where each node can be interrogated by computational simulation. The model predicted an unanticipated feedback loop, whereby Raf/MEK/ERK signalling maintains suppression of Rac1 by inhibiting the Rac-activating Sos1-Eps8-Abi1 complex, allowing RhoA activity to predominate in invasive protrusions. MEK inhibition was sufficient to promote lamellipodia formation and oppose filopodial actin-spike formation, and led to activation of Rac and inactivation of RhoA at the leading edge of cells moving in 3D matrix. Furthermore, MEK inhibition abrogated RCP/α5β1/EGFR1-driven invasive migration. However, upon knockdown of Eps8 (to suppress the Sos1-Abi1-Eps8 complex), MEK inhibition had no effect on RhoGTPase activity and did not oppose invasive migration, suggesting that MEK-ERK signalling suppresses the Rac-activating Sos1-Abi1-Eps8 complex to maintain RhoA activity and promote filopodial actin-spike formation and invasive migration. Our study highlights the predictive potential of mathematical modelling approaches, and demonstrates that a simple intervention (MEK-inhibition) could be of therapeutic benefit in preventing invasive migration and metastasis.JHRH is supported by a University of Manchester President’s Doctoral Scholarship and BBSRC-DTP (BB/J014478/1). This work was supported by the Wellcome Trust (090453/Z/09/Z and 090453/Z/09/A to PTC)

Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes

Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.Comment: Since the publication of this article and among the possible improvements mentioned in the Conclusion, two improvements have been done: extending the algorithm for multivalued logic and considering the basins of attraction of the pathological attractors for selecting the therapeutic bullet

Plasma rich in growth factors to treat Knee Osteoarthritis

188 p.En la tesis titulada Plasma Rich in Growth Factors to treat knee osteoarthritis se exponen 4 trabajosexperimentales basados en el uso del Plasma rico en factores de crecimiento. Los objetivos específicos delos trabajos realizados son los siguientes:1. Validar las inyecciones intraarticulares de PRP como un tratamiento seguro y eficaz para la artrosisde rodilla.2. Evaluar una nueva vía para tratar la artrosis de rodilla, utilizando como diana la membrana sinovial,la superficie del cartílago articular, el líquido sinovial y el hueso subcondral, y combinandoinfiltraciones intraarticulares e intraóseas de Plasma rico en factores de crecimiento (PRP).3. Explorar la adecuación del Líquido sinovial como fuente de células madre mesenquimales (CMMs)y sus respuestas a los mecanismos bilógicos implicados en los efectos de dos modalidades distintasde tratamiento de PRP en pacientes con artrosis: Inyecciones intraarticulares con la membranasinovial, la superfcicie del cartílago articular y el líquido sinovial como diana, o la combinación deinyecciones intraarticulares e intraóseas, alcanzando por último el hueso subcondral

Enhancing Boolean networks with fuzzy operators and edge tuning

Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale. An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic when modeling biological processes. Consequently, this work proposes a modeling approach derived from Boolean networks where fuzzy operators are used and where edges are tuned. Fuzzy operators allow variables to be continuous and then to be more finely valued than with discrete modeling approaches, such as Boolean networks, while remaining qualitative. Moreover, to consider that in a given biological network some interactions are slower and/or weaker relative to other ones, edge states are computed in order to modulate in speed and strength the signal they convey. The proposed formalism is illustrated through its implementation on a tiny sample of the epidermal growth factor receptor signaling pathway. The obtained simulations show that continuous results are produced, thus allowing finer analysis, and that modulating the signal conveyed by the edges allows their tuning according to knowledge about the modeled interactions, thus incorporating more knowledge. The proposed modeling approach is expected to bring enhancements in the ability of qualitative models to simulate the dynamics of biological networks while not requiring quantitative information