TY - GEN
AB - A comprehensive understanding of the clonal evolution of cancer is critical for understanding neoplasia. Genome-wide sequencing data enables evolutionary studies at unprecedented depth. However, classical phylogenetic methods often struggle with noisy sequencing data of impure DNA samples and fail to detect subclones that have different evolutionary trajectories. We have developed a tool, called Treeomics, that allows us to reconstruct the phylogeny of a cancer with commonly available sequencing technologies. Using Bayesian inference and Integer Linear Programming, robust phylogenies consistent with the biological processes underlying cancer evolution were obtained for pancreatic, ovarian, and prostate cancers. Furthermore, Treeomics correctly identified sequencing artifacts such as those resulting from low statistical power; nearly 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Importantly, we show that the evolutionary trees generated with Treeomics are mathematically optimal.
AU - Reiter, Johannes
AU - Makohon-Moore, Alvin
AU - Gerold, Jeffrey
AU - Bozic, Ivana
AU - Chatterjee, Krishnendu
AU - Iacobuzio-Donahue, Christine
AU - Vogelstein, Bert
AU - Nowak, Martin
ID - 5444
SN - 2664-1690
TI - Reconstructing robust phylogenies of metastatic cancers
ER -
TY - DATA
AB - This repository contains the experimental part of the CAV 2015 publication Counterexample Explanation by Learning Small Strategies in Markov Decision Processes.
We extended the probabilistic model checker PRISM to represent strategies of Markov Decision Processes as Decision Trees.
The archive contains a java executable version of the extended tool (prism_dectree.jar) together with a few examples of the PRISM benchmark library.
To execute the program, please have a look at the README.txt, which provides instructions and further information on the archive.
The archive contains scripts that (if run often enough) reproduces the data presented in the publication.
AU - Fellner, Andreas
ID - 5549
KW - Markov Decision Process
KW - Decision Tree
KW - Probabilistic Verification
KW - Counterexample Explanation
TI - Experimental part of CAV 2015 publication: Counterexample Explanation by Learning Small Strategies in Markov Decision Processes
ER -
TY - JOUR
AB - In plants, vacuolar H+-ATPase (V-ATPase) activity acidifies both the trans-Golgi network/early endosome (TGN/EE) and the vacuole. This dual V-ATPase function has impeded our understanding of how the pH homeostasis within the plant TGN/EE controls exo- and endocytosis. Here, we show that the weak V-ATPase mutant deetiolated3 (det3) displayed a pH increase in the TGN/EE, but not in the vacuole, strongly impairing secretion and recycling of the brassinosteroid receptor and the cellulose synthase complexes to the plasma membrane, in contrast to mutants lacking tonoplast-localized V-ATPase activity only. The brassinosteroid insensitivity and the cellulose deficiency defects in det3 were tightly correlated with reduced Golgi and TGN/EE motility. Thus, our results provide strong evidence that acidification of the TGN/EE, but not of the vacuole, is indispensable for functional secretion and recycling in plants.
AU - Yu, Luo
AU - Scholl, Stefan
AU - Doering, Anett
AU - Yi, Zhang
AU - Irani, Niloufer
AU - Di Rubbo, Simone
AU - Neumetzler, Lutz
AU - Krishnamoorthy, Praveen
AU - Van Houtte, Isabelle
AU - Mylle, Evelien
AU - Bischoff, Volker
AU - Vernhettes, Samantha
AU - Winne, Johan
AU - Friml, Jirí
AU - Stierhof, York
AU - Schumacher, Karin
AU - Persson, Staffan
AU - Russinova, Eugenia
ID - 1383
IS - 7
JF - Nature Plants
TI - V-ATPase activity in the TGN/EE is required for exocytosis and recycling in Arabidopsis
VL - 1
ER -
TY - THES
AB - This thesis is concerned with the computation and approximation of intrinsic volumes. Given a smooth body M and a certain digital approximation of it, we develop algorithms to approximate various intrinsic volumes of M using only measurements taken from its digital approximations. The crucial idea behind our novel algorithms is to link the recent theory of persistent homology to the theory of intrinsic volumes via the Crofton formula from integral geometry and, in particular, via Euler characteristic computations. Our main contributions are a multigrid convergent digital algorithm to compute the first intrinsic volume of a solid body in R^n as well as an appropriate integration pipeline to approximate integral-geometric integrals defined over the Grassmannian manifold.
AU - Pausinger, Florian
ID - 1399
TI - On the approximation of intrinsic volumes
ER -
TY - THES
AB - Cancer results from an uncontrolled growth of abnormal cells. Sequentially accumulated genetic and epigenetic alterations decrease cell death and increase cell replication. We used mathematical models to quantify the effect of driver gene mutations. The recently developed targeted therapies can lead to dramatic regressions. However, in solid cancers, clinical responses are often short-lived because resistant cancer cells evolve. We estimated that approximately 50 different mutations can confer resistance to a typical targeted therapeutic agent. We find that resistant cells are likely to be present in expanded subclones before the start of the treatment. The dominant strategy to prevent the evolution of resistance is combination therapy. Our analytical results suggest that in most patients, dual therapy, but not monotherapy, can result in long-term disease control. However, long-term control can only occur if there are no possible mutations in the genome that can cause cross-resistance to both drugs. Furthermore, we showed that simultaneous therapy with two drugs is much more likely to result in long-term disease control than sequential therapy with the same drugs. To improve our understanding of the underlying subclonal evolution we reconstruct the evolutionary history of a patient's cancer from next-generation sequencing data of spatially-distinct DNA samples. Using a quantitative measure of genetic relatedness, we found that pancreatic cancers and their metastases demonstrated a higher level of relatedness than that expected for any two cells randomly taken from a normal tissue. This minimal amount of genetic divergence among advanced lesions indicates that genetic heterogeneity, when quantitatively defined, is not a fundamental feature of the natural history of untreated pancreatic cancers. Our newly developed, phylogenomic tool Treeomics finds evidence for seeding patterns of metastases and can directly be used to discover rules governing the evolution of solid malignancies to transform cancer into a more predictable disease.
AU - Reiter, Johannes
ID - 1400
TI - The subclonal evolution of cancer
ER -
TY - CONF
AB - We consider the problem of statistical computations with persistence diagrams, a summary representation of topological features in data. These diagrams encode persistent homology, a widely used invariant in topological data analysis. While several avenues towards a statistical treatment of the diagrams have been explored recently, we follow an alternative route that is motivated by the success of methods based on the embedding of probability measures into reproducing kernel Hilbert spaces. In fact, a positive definite kernel on persistence diagrams has recently been proposed, connecting persistent homology to popular kernel-based learning techniques such as support vector machines. However, important properties of that kernel enabling a principled use in the context of probability measure embeddings remain to be explored. Our contribution is to close this gap by proving universality of a variant of the original kernel, and to demonstrate its effective use in twosample hypothesis testing on synthetic as well as real-world data.
AU - Kwitt, Roland
AU - Huber, Stefan
AU - Niethammer, Marc
AU - Lin, Weili
AU - Bauer, Ulrich
ID - 1424
TI - Statistical topological data analysis-A kernel perspective
VL - 28
ER -
TY - CONF
AB - In this work we aim at extending the theoretical foundations of lifelong learning. Previous work analyzing this scenario is based on the assumption that learning tasks are sampled i.i.d. from a task environment or limited to strongly constrained data distributions. Instead, we study two scenarios when lifelong learning is possible, even though the observed tasks do not form an i.i.d. sample: first, when they are sampled from the same environment, but possibly with dependencies, and second, when the task environment is allowed to change over time in a consistent way. In the first case we prove a PAC-Bayesian theorem that can be seen as a direct generalization of the analogous previous result for the i.i.d. case. For the second scenario we propose to learn an inductive bias in form of a transfer procedure. We present a generalization bound and show on a toy example how it can be used to identify a beneficial transfer algorithm.
AU - Pentina, Anastasia
AU - Lampert, Christoph
ID - 1425
TI - Lifelong learning with non-i.i.d. tasks
VL - 2015
ER -
TY - CONF
AB - Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse their runtime on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrence of new mutations is much longer than the time it takes for a new beneficial mutation to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a (1+1)-type process where the probability of accepting a new genotype (improvements or worsenings) depends on the change in fitness. We present an initial runtime analysis of SSWM, quantifying its performance for various parameters and investigating differences to the (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient.
AU - Paixao, Tiago
AU - Sudholt, Dirk
AU - Heredia, Jorge
AU - Trubenova, Barbora
ID - 1430
T2 - Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
TI - First steps towards a runtime comparison of natural and artificial evolution
ER -
TY - CONF
AB - Cryptographic access control offers selective access to encrypted data via a combination of key management and functionality-rich cryptographic schemes, such as attribute-based encryption. Using this approach, publicly available meta-data may inadvertently leak information on the access policy that is enforced by cryptography, which renders cryptographic access control unusable in settings where this information is highly sensitive. We begin to address this problem by presenting rigorous definitions for policy privacy in cryptographic access control. For concreteness we set our results in the model of Role-Based Access Control (RBAC), where we identify and formalize several different flavors of privacy, however, our framework should serve as inspiration for other models of access control. Based on our insights we propose a new system which significantly improves on the privacy properties of state-of-the-art constructions. Our design is based on a novel type of privacy-preserving attribute-based encryption, which we introduce and show how to instantiate. We present our results in the context of a cryptographic RBAC system by Ferrara et al. (CSF'13), which uses cryptography to control read access to files, while write access is still delegated to trusted monitors. We give an extension of the construction that permits cryptographic control over write access. Our construction assumes that key management uses out-of-band channels between the policy enforcer and the users but eliminates completely the need for monitoring read/write access to the data.
AU - Ferrara, Anna
AU - Fuchsbauer, Georg
AU - Liu, Bin
AU - Warinschi, Bogdan
ID - 1474
TI - Policy privacy in cryptographic access control
ER -
TY - CONF
AB - Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role not only in the development of mathematical and logical skills, but also in the emotional and social development. In this paper, we address the problem of generating targeted starting positions for such games. This can facilitate new approaches for bringing novice players to mastery, and also leads to discovery of interesting game variants. We present an approach that generates starting states of varying hardness levels for player 1 in a two-player board game, given rules of the board game, the desired number of steps required for player 1 to win, and the expertise levels of the two players. Our approach leverages symbolic methods and iterative simulation to efficiently search the extremely large state space. We present experimental results that include discovery of states of varying hardness levels for several simple grid-based board games. The presence of such states for standard game variants like 4×4 Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased.
AU - Ahmed, Umair
AU - Chatterjee, Krishnendu
AU - Gulwani, Sumit
ID - 1481
T2 - Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
TI - Automatic generation of alternative starting positions for simple traditional board games
VL - 2
ER -