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- Lilja, Sandra, 1989-
(författare)
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Digital Twins : High Resolution Disease Models for Optimized Diagnosis and Treatment
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To study immune-mediated diseases, which can affect the expression of thousands of genes among many different cell types and organs, is a daunting challenge. However, for effective diagnosis and therapeutic treatment it is relevant to understand the regulatory functions of disease. In this thesis, we hypothesized that regulatory functions in complex diseases can be effectively prioritized based on so called digital twins, which are based on high-resolution single cell data in combination with network theories. More specifically, we tested if digital twins could be used on a patient-group level to prioritize cell types, genes, and/or organs based on their regulatory function in the disease progression. If this hypothesis is true, potential biomarkers and therapeutic targets can be identified for optimized diagnosis and treatment. The long-term goal is to construct digital twins for personalized medicine, to predict the optimal treatment strategies for the individual patients. Although, this is a very ambitious goal which could not be reached through this thesis, relevant steps towards it have been reached.First, we tested if high-resolution disease models based on single cell RNAsequencing (scRNA-seq) data could be used in combination with network theories, to predict and prevent disease. For this aim, we used a mouse model of antigeninduced arthritis (AIA). Based on the cell type specific genes in AIA joint, we identified a multi-cellular disease model (MCDM), including predicted cell-cell interactions. Analyzing this model, Granulocytes were identified as most central in AIA joint. The results from this centrality analysis correlated with GWAS enrichment among the cell type specific genes, as well as with the centrality analyses based on human RA, supporting our results relevance for human disease. A drug, bezafibrate, was further identified which mainly targeted shared disease modules over the central and GWAS enriched CD4+ T cells in nine of 13 analyzed human diseases. Bezafibrate treatment of our AIA mouse model resulted in a decrease in arthritis severity score as well as a decrease in T cell proliferation into the joint.Since blood is an easily available source of data, it is of interest to know it’s potential usefulness when constructing digital twins. To test if samples taken from blood are representative of the inflamed organ, we performed a meta-analysis of different samples from blood and joint of patients with rheumatoid arthritis, as well as from joint and blood Granulocytes of our AIA mouse model. Based on differentially expressed genes (DEGs) between sick and healthy samples from each dataset, we performed pathway analyses and predicted potential biomarkers and upstream regulators (URs). Comparing the lists of pathways, biomarkers, and URs between the datasets from different subsets of blood samples showed low or no similarities. However, the datasets of human bulk or mouse single cell data collected from synovial fluid or full joint showed high similarities. Furthermore, the top shared enriched pathways, predicted biomarkers, and URs from both human and mouse were to a higher degree connected to known functions of autoimmune diseases or rheumatoid arthritis, compared to the respective results from samples taken from blood. These findings indicate that inflammatory mechanisms in cells in blood and inflamed organs differ greatly, which may have important diagnostic and therapeutic implications.We next analyzed if digital twins could be used to identify the early regulatory mechanisms that are also present at the late time points. For this, we used an in vitro time series model of seasonal allergic rhinitis. Samples were taken before allergen stimulation, as well as at 12 hours, 1 day, 2 days, 3 days, 5 days, and 7 days after allergen stimulation, for scRNA-seq and MCDM construction. Multi-directional interactions including all cell types were found at all time points, even before allergen stimulation, which complicated the identification of one key regulatory cell type or gene. Instead, we found that the regulatory genes could be ranked based on their overall downstream effect over all the time points. Our top-ranked regulatory gene, PDGFB, targeted most of the cell types at all the time points, while a previously known early regulator and drug target in allergy, IL4, targeted only five cell type and time point combinations. Validation studies further showed that neutralization of PDGF-BB on allergen-stimulated PBMC from SAR patients were more effective compared to neutralization of IL-4.Finally, we tested if a digital twin including data from multiple organs could be used to understand the systemic interactional changes due to disease. For this aim, we used a systemic mouse model of arthritis, namely collagen induced arthritis (CIA). We first analyzed ten different organs, based on which we prioritized five organs with the highest number of DEGs between CIA and healthy mice, namely joint, lung, muscle, skin, and spleen. Although only joint showed signs of inflammation, many DEGs were identified in all five organs. Those changes were organized into a multi-organ multi-cellular disease model, which indicated an on/off switch of pro-/anti-inflammatory functions in joint and muscle respectively. Validation studies in human immune-mediated inflammatory diseases supported this on/off switch, where pro-inflammatory functions were mainly found in inflamed organs, while anti-inflammatory functions were found in non-inflamed organs.In conclusion, this thesis supports the potential of using high-resolution disease models for digital twin construction. Such digital twins could then be used to prioritize cell types and genes, for further prediction of diagnostic markers and therapeutic targets. Even though the identification of one key regulatory function was complicated due to multidirectional interactions, the genes could be ranked based on their relative downstream effect. For reproducible results, we found that digital twins should ideally be based on data from locally inflamed organs, while systemic models and models covering different disease stages could be useful to understand the disease progression.
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| 2. |
- Fontan, Angela, 1991-
(författare)
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Collective decision-making on networked systems in presence of antagonistic interactions
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Collective decision-making refers to a process in which the agents of a community exchange opinions with the objective of reaching a common decision. It is often assumed that a collective decision is reached through collaboration among the individuals. However in many contexts, concerning for instance collective human behavior, it is more realistic to assume that the agents can collaborate or compete with each other. In this case, different types of collective behavior can be observed. This thesis investigates collective decision-making problems in multiagent systems, both in the case of collaborative and of antagonistic interactions.The first problem studied in the thesis is a special instance of the consensus problem, denoted "interval consensus" in this work. It consists in letting the agents impose constraints on the possible common consensus value. It is shown that introducing saturated nonlinearities in the decision-making dynamics to describe how the agents express their opinions effectively allows the agents to influence the achievable consensus value and steer it to the intersection of all the intervals imposed by the agents. A second class of collective decision-making models discussed in the thesis is obtained by replacing the saturations with sigmoidal nonlinearities. This nonlinear interconnected model is first investigated in the collaborative case and then in the antagonistic case, represented as a signed graph of interactions. In both cases, it is shown that the behavior of the model can be described by means of bifurcation analysis, with the equilibria of the system encoding the possible decisions for the community. A scalar positive parameter, denoted "social effort", is added to the model to represent the strength of commitment between the agents, and plays the role of bifurcation parameter in the analysis. It is shown that if the social effort is small, then the community is in a deadlock situation (i.e., no decision is taken), while if the agents have the "right" amount of commitment two alternative consensus decision states for the community are achieved. However, by further increasing the social effort, the agents may fall in a situation of "overcommitment" where multiple (more than 2) decisions are possible. When antagonistic interactions between the agents are taken into account, they may lead to conflicts or social tensions during the decision-making process, which can be quantified by the notion of "frustration" of the signed network representing the community. The aim is to understand how the presence of antagonism (represented by the amount of frustration of the signed network) influences the collective decision-making process. It is shown that, while the qualitative behavior of the system does not change, the value of social effort required from the agents to break the deadlock (i.e., the value for which the bifurcation is crossed) increases with the frustration of the signed network: the higher the frustration, the higher the required social commitment.A natural context to apply these results is that of political decision-making. In particular it is shown in the thesis how the government formation process in parliamentary democracies can be modeled as a collective decision-making system, where the agents are the parliamentary members, the decision is the vote of confidence they cast to a candidate cabinet coalition, and the social effort parameter is a proxy for the duration of the government negotiation talks. A signed network captures the alliances/rivalries between the political parties in the parliament. The idea is that the frustration of the parliamentary networks should correlate well with the duration of the government negotiation, and it is supported by the analysis of the legislative elections in 29 European countries in the last 40 years. The final contribution of this thesis is an analysis of the structure of (signed) Laplacian matrices and of their pseudoinverses. It is shown that the pseudoinverse of a Laplacian is in general a signed Laplacian, and in particular that the set of eventually exponentially positive Laplacian matrices (i.e., matrices whose exponential is a matrix with negative entries which becomes and stays positive at a certain power) is closed under stability and matrix pseudoinversion.
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| 3. |
- Lindmark, Gustav, 1983-
(författare)
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Controllability of Complex Networks at Minimum Cost
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The control-theoretic notion of controllability captures the ability to guide a system toward a desired state with a suitable choice of inputs. Controllability of complex networks such as traffic networks, gene regulatory networks, power grids etc. can for instance enable efficient operation or entirely new applicative possibilities. However, when control theory is applied to complex networks like these, several challenges arise. This thesis considers some of them, in particular we investigate how a given network can be rendered controllable at a minimum cost by placement of control inputs or by growing the network with additional edges between its nodes. As cost function we take either the number of control inputs that are needed or the energy that they must exert.A control input is called unilateral if it can assume either positive or negative values, but not both. Motivated by the many applications where unilateral controls are common, we reformulate classical controllability results for this particular case into a more computationally-efficient form that enables a large scale analysis. Assuming that each control input targets only one node (called a driver node), we show that the unilateral controllability problem is to a high degree structural: from topological properties of the network we derive theoretical lower bounds for the minimal number of unilateral control inputs, bounds similar to those that have already been established for the minimal number of unconstrained control inputs (e.g. can assume both positive and negative values). With a constructive algorithm for unilateral control input placement we also show that the theoretical bounds can often be achieved.A network may be controllable in theory but not in practice if for instance unreasonable amounts of control energy are required to steer it in some direction. For the case with unconstrained control inputs, we show that the control energy depends on the time constants of the modes of the network, the longer they are, the less energy is required for control. We also present different strategies for the problem of placing driver nodes such that the control energy requirements are reduced (assuming that theoretical controllability is not an issue). For the most general class of networks we consider, directed networks with arbitrary eigenvalues (and thereby arbitrary time constants), we suggest strategies based on a novel characterization of network non-normality as imbalance in the distribution of energy over the network. Our formulation allows to quantify network non-normality at a node level as combination of two different centrality metrics. The first measure quantifies the influence that each node has on the rest of the network, while the second measure instead describes the ability to control a node indirectly from the other nodes. Selecting the nodes that maximize the network non-normality as driver nodes significantly reduces the energy needed for control.Growing a network, i.e. adding more edges to it, is a promising alternative to reduce the energy needed to control it. We approach this by deriving a sensitivity function that enables to quantify the impact of an edge modification with the H2 and H∞ norms, which in turn can be used to design edge additions that improve commonly used control energy metrics.
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| 4. |
- Zenere, Alberto, 1992-
(författare)
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Integration of epigenetic, transcriptomic and proteomic data
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- One of the scopes of Systems Biology is to propose mathematical models that best capture the dynamic behavior of intra-cellular processes. In this regard, the last two decades have brought up a shift in the field, with technological advances now allowing researchers to access a wide range of high-throughput technologies at an affordable cost. These techniques allow to simultaneously interrogate thousands of variables, such as genome-wide transcriptomics and proteomics. However, parallel to these technological advances, there is a growing need for mathematical models that are suited to integrate measurements obtained from different cellular processes.In this thesis we aim to model combinations of three commonly used high-throughput data: epigenetic (namely ATAC-seq and DNA methylation), transcriptomic (RNA-seq) and proteomic data (MASS-spectrometry). In the first work we analyze paired ATAC-seq and RNA-seq data to integrate measurements of (i) chromatin openness, (ii) transcription factors (TFs) availability and (iii) gene expression. To model these data, we use elementary causal motifs, a class of mathematical models which is suited to represent causal interactions between three nodes. Indeed, our analysis shows that the elementary causal motifs in the data are enriched for biologically relevant TF-gene interactions. Moreover, a significant overlap is observed between the causal motifs identified in datasets representing similar cell stimuli, suggesting that causal motifs represent a robust biological signal.This work is then extended to include another class of high-throughput data: MASS-spectrometry. More precisely, we propose a framework to model the flow of events that goes from chromatin remodeling to splice variants expression, and from splice variants to protein synthesis. As the underlying graph becomes more complex than the previous case, a more general mathematical framework is considered: Bayesian networks. Interestingly, this work shows that most putative associations between chromatin regions, splice variants and proteins that have been gathered by scientific community so far, are supported by the data. Moreover, similarly to the previous work, the causal interactions identified in the data highlight relevant biological features; more precisely, causal chains between chromatin regions, splice variants and proteins are enriched for splice variants that have a major role in protein synthesis.From a technical point of view, causal motifs are characterized by a property known as conditional independence, which can be used to identify causal interactions in the data. However, particularly when the data available is limited, it is challenging to assess conditional independencies in the data. It is therefore of interest to investigate the existence of properties that allow us to predict conditional independence. In particular, in our work we propose two properties: structural balance and inverse balance, which are closely connected to what is known in the literature as positive association and multivariate total positivity of order 2 (MTP2), respectively. Our analysis shows that both heuristics are useful in predicting conditional independence, both from a theoretical perspective and in experimental data.Lastly, a network-based approach is used to integrate DNA methylation and RNA-seq in a case-control study centered around multiple sclerosis, in order to identify common regulatory patterns in DNA methylation and gene expression during the course of pregnancy. The strategy is based on the rationale that proteins that are interconnected in the protein-protein network are more likely to be involved in similar cellular functions. Indeed, the analysis highlights that similar pathways are altered at epigenetic and transcriptomic level, leading to a set of genes that are likely involved in the modification of the disease symptoms that is observed during pregnancy.
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