Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs

Learning causal graphs under interventions and applications to single-cell biological data analysis

Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February, 2021Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021Cataloged from the official PDF version of thesis.Includes bibliographical references (pages 49-51).This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. The identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes, has previously been studied. This thesis first extends these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them, by defining and characterizing the interventional Markov equivalence class that can be identified from general interventions. Subsequently, this thesis proposes the first provably consistent algorithm for learning DAGs in this setting. Finally, this algorithm as well as related work is applied to analyze biological datasets.by Karren Dai Yang.S.M.S.M.S.M. Massachusetts Institute of Technology, Department of Biological EngineeringS.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Scalable unbalanced optimal transport using generative adversarial networks

Generative adversarial networks (GANs) are an expressive class of neural generative models with tremendous success in modeling high-dimensional continuous measures. In this paper, we present a scalable method for unbalanced optimal transport (OT) based on the generative-adversarial framework. We formulate unbalanced OT as a problem of simultaneously learning a transport map and a scaling factor that push a source measure to a target measure in a cost-optimal manner. We provide theoretical justification for this formulation, showing that it is closely related to an existing static formulation by Liero et al. (2018). We then propose an algorithm for solving this problem based on stochastic alternating gradient updates, similar in practice to GANs, and perform numerical experiments demonstrating how this methodology can be applied to population modeling

Displacement of population from the Sudetenland after 1938

Pro pochopení role sudetských Němců v dějinných událostech roku 1938 začíná práce již rokem 1918 a vznikem Československa. Národnostní krize 30. let se vyostřila zejména v období hospodářské krize, která postihla celý svět. Práce se zabývá důvody úspěchu strany SdP, vlivu na obyvatelstvo a úlohu při odstoupení území. Přijetí mnichovské dohody pak znamenalo porušení mezinárodního práva a zásah do integrity Československého státu. Dále se práce zaměřuje na rozsah území, která určila mnichovská dohoda k odstoupení a způsob vytyčení tzv. pátého pásma. Hlavním těžištěm práce je vysídlení obyvatel. Jsou zde vymezeny skupiny obyvatel, které z pohraničí odcházely a důvody, jež je vedly k opuštění svých domovů. S problematikou odstoupených území úzce souvisí i otázka státního občanství a možnosti opce, která je v práci rovněž rozebírána. Na hlavním městě říšské župy Liberci je demonstrována nálada obyvatelstva a jejich reakce na změny poměrů v odstoupeném pohraničí. Na odtrženém území zůstala i početná menšina Čechů, proto je poukázáno na práva, která jim byla přiznána a jejich realizaci. Dalekosáhlé důsledky měla mnichovská dohoda i pro okleštěné území Československa, tzv. druhou republiku, která pod vlivem mnichovského diktátu transformovala formu vlády v autoritativní demokracii. Předkládaná práce se zaměřuje na ústavněprávní změny a vybočení normativně právních aktů z ústavního rámce. Dále jsou zde uvedeny hlavní zákonné úpravy, které řešily přistěhovalectví a nezaměstnanost, jež se staly palčivým problémem nového státu.Katedra právních dějinObhájenoThe Interwar period (1919?1939) is characterized by the fall of multi-national states in Europe, the rise of national states but also increasing tension between ethnic groups in these newly installed sovereign bodies. In Czechoslovakia, this process eventually resulted into large scale migration caused by the detachment of the boundary regions. As the majority of population in border regions (Sudeten) had been of German or Austrian descent, their claims to the right of self-determination were heard by Britain. They had sent a delegation to report on the state of affairs in the Czechoslovak national state. The situation had become a major international topic, closely related to the interest of keeping Europe at peace. The consequences the Munich Agreement had on the Czechoslovak state and population were dramatic ? they had meant a large scale migrations between the Sudenten and the so called second republic and imposed a choice of citizenship. In order to map the circumstances that had been shaping the course of region?s history, the author explores the social change and relevant legal documents to suggest parallels to the historical development. This work focuses on historical events that lead to the Munich Agreement and the legal implications on the expulsion of Czechoslovak populace from the boundary regions. Mentioned are minority rights as well as the division into 5 zones that the agreement imposed on the Czechoslovak state and the role of SdP in the events that had lead to the annexation by The Third Reich in 1938

Permutation-based Causal Inference Algorithms with Interventions

© 2017 Neural information processing systems foundation. All rights reserved. Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data. In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption. These algorithms are interventional adaptations of the Greedy SP algorithm and are the first algorithms using both observational and interventional data with consistency guarantees. Moreover, these algorithms have the advantage that they are nonparametric, which makes them useful also for analyzing non-Gaussian data. In this paper, we present these two algorithms and their consistency guarantees, and we analyze their performance on simulated data, protein signaling data, and single-cell gene expression data