@phdthesis{1125,
abstract = {Natural environments are never constant but subject to spatial and temporal change on
all scales, increasingly so due to human activity. Hence, it is crucial to understand the
impact of environmental variation on evolutionary processes. In this thesis, I present
three topics that share the common theme of environmental variation, yet illustrate its
effect from different perspectives.
First, I show how a temporally fluctuating environment gives rise to second-order
selection on a modifier for stress-induced mutagenesis. Without fluctuations, when
populations are adapted to their environment, mutation rates are minimized. I argue
that a stress-induced mutator mechanism may only be maintained if the population is
repeatedly subjected to diverse environmental challenges, and I outline implications of
the presented results to antibiotic treatment strategies.
Second, I discuss my work on the evolution of dispersal. Besides reproducing
known results about the effect of heterogeneous habitats on dispersal, it identifies
spatial changes in dispersal type frequencies as a source for selection for increased
propensities to disperse. This concept contains effects of relatedness that are known
to promote dispersal, and I explain how it identifies other forces selecting for dispersal
and puts them on a common scale.
Third, I analyse genetic variances of phenotypic traits under multivariate stabilizing
selection. For the case of constant environments, I generalize known formulae of
equilibrium variances to multiple traits and discuss how the genetic variance of a focal
trait is influenced by selection on background traits. I conclude by presenting ideas and
preliminary work aiming at including environmental fluctuations in the form of moving
trait optima into the model.},
author = {Novak, Sebastian},
pages = {124},
publisher = {IST Austria},
title = {{Evolutionary proccesses in variable emvironments}},
year = {2016},
}
@phdthesis{1396,
abstract = {CA3 pyramidal neurons are thought to pay a key role in memory storage and pattern completion by activity-dependent synaptic plasticity between CA3-CA3 recurrent excitatory synapses. To examine the induction rules of synaptic plasticity at CA3-CA3 synapses, we performed whole-cell patch-clamp recordings in acute hippocampal slices from rats (postnatal 21-24 days) at room temperature. Compound excitatory postsynaptic potentials (ESPSs) were recorded by tract stimulation in stratum oriens in the presence of 10 µM gabazine. High-frequency stimulation (HFS) induced N-methyl-D-aspartate (NMDA) receptor-dependent long-term potentiation (LTP). Although LTP by HFS did not requier postsynaptic spikes, it was blocked by Na+-channel blockers suggesting that local active processes (e.g.) dendritic spikes) may contribute to LTP induction without requirement of a somatic action potential (AP). We next examined the properties of spike timing-dependent plasticity (STDP) at CA3-CA3 synapses. Unexpectedly, low-frequency pairing of EPSPs and backpropagated action potentialy (bAPs) induced LTP, independent of temporal order. The STDP curve was symmetric and broad, with a half-width of ~150 ms. Consistent with these specific STDP induction properties, post-presynaptic sequences led to a supralinear summation of spine [Ca2+] transients. Furthermore, in autoassociative network models, storage and recall was substantially more robust with symmetric than with asymmetric STDP rules. In conclusion, we found associative forms of LTP at CA3-CA3 recurrent collateral synapses with distinct induction rules. LTP induced by HFS may be associated with dendritic spikes. In contrast, low frequency pairing of pre- and postsynaptic activity induced LTP only if EPSP-AP were temporally very close. Together, these induction mechanisms of synaptiic plasticity may contribute to memory storage in the CA3-CA3 microcircuit at different ranges of activity.},
author = {Mishra, Rajiv Kumar},
pages = {83},
publisher = {IST Austria},
title = {{Synaptic plasticity rules at CA3-CA3 recurrent synapses in hippocampus}},
year = {2016},
}
@phdthesis{1121,
abstract = {Horizontal gene transfer (HGT), the lateral acquisition of genes across existing species
boundaries, is a major evolutionary force shaping microbial genomes that facilitates
adaptation to new environments as well as resistance to antimicrobial drugs. As such,
understanding the mechanisms and constraints that determine the outcomes of HGT
events is crucial to understand the dynamics of HGT and to design better strategies to
overcome the challenges that originate from it.
Following the insertion and expression of a newly transferred gene, the success of an
HGT event will depend on the fitness effect it has on the recipient (host) cell. Therefore,
predicting the impact of HGT on the genetic composition of a population critically
depends on the distribution of fitness effects (DFE) of horizontally transferred genes.
However, to date, we have little knowledge of the DFE of newly transferred genes, and
hence little is known about the shape and scale of this distribution.
It is particularly important to better understand the selective barriers that determine
the fitness effects of newly transferred genes. In spite of substantial bioinformatics
efforts to identify horizontally transferred genes and selective barriers, a systematic
experimental approach to elucidate the roles of different selective barriers in defining
the fate of a transfer event has largely been absent. Similarly, although the fact that
environment might alter the fitness effect of a horizontally transferred gene may seem
obvious, little attention has been given to it in a systematic experimental manner.
In this study, we developed a systematic experimental approach that consists of
transferring 44 arbitrarily selected Salmonella typhimurium orthologous genes into an
Escherichia coli host, and estimating the fitness effects of these transferred genes at a
constant expression level by performing competition assays against the wild type.
In chapter 2, we performed one-to-one competition assays between a mutant strain
carrying a transferred gene and the wild type strain. By using flow cytometry we
estimated selection coefficients for the transferred genes with a precision level of 10-3,and obtained the DFE of horizontally transferred genes. We then investigated if these
fitness effects could be predicted by any of the intrinsic properties of the genes, namely,
functional category, degree of complexity (protein-protein interactions), GC content,
codon usage and length. Our analyses revealed that the functional category and length
of the genes act as potential selective barriers. Finally, using the same procedure with
the endogenous E. coli orthologs of these 44 genes, we demonstrated that gene dosage is
the most prominent selective barrier to HGT.
In chapter 3, using the same set of genes we investigated the role of environment on the
success of HGT events. Under six different environments with different levels of stress
we performed more complex competition assays, where we mixed all 44 mutant strains
carrying transferred genes with the wild type strain. To estimate the fitness effects of
genes relative to wild type we used next generation sequencing. We found that the DFEs
of horizontally transferred genes are highly dependent on the environment, with
abundant gene–by-environment interactions. Furthermore, we demonstrated a
relationship between average fitness effect of a gene across all environments and its
environmental variance, and thus its predictability. Finally, in spite of the fitness effects
of genes being highly environment-dependent, we still observed a common shape of
DFEs across all tested environments.},
author = {Acar, Hande},
pages = {75},
publisher = {IST Austria},
title = {{Selective barriers to horizontal gene transfer}},
year = {2016},
}
@phdthesis{1126,
abstract = {Traditionally machine learning has been focusing on the problem of solving a single
task in isolation. While being quite well understood, this approach disregards an
important aspect of human learning: when facing a new problem, humans are able to
exploit knowledge acquired from previously learned tasks. Intuitively, access to several
problems simultaneously or sequentially could also be advantageous for a machine
learning system, especially if these tasks are closely related. Indeed, results of many
empirical studies have provided justification for this intuition. However, theoretical
justifications of this idea are rather limited.
The focus of this thesis is to expand the understanding of potential benefits of information
transfer between several related learning problems. We provide theoretical
analysis for three scenarios of multi-task learning - multiple kernel learning, sequential
learning and active task selection. We also provide a PAC-Bayesian perspective on
lifelong learning and investigate how the task generation process influences the generalization
guarantees in this scenario. In addition, we show how some of the obtained
theoretical results can be used to derive principled multi-task and lifelong learning
algorithms and illustrate their performance on various synthetic and real-world datasets.},
author = {Pentina, Anastasia},
pages = {127},
publisher = {IST Austria},
title = {{Theoretical foundations of multi-task lifelong learning}},
doi = {10.15479/AT:ISTA:TH_776},
year = {2016},
}
@phdthesis{1397,
abstract = {We study partially observable Markov decision processes (POMDPs) with objectives used in verification and artificial intelligence. The qualitative analysis problem given a POMDP and an objective asks whether there is a strategy (policy) to ensure that the objective is satisfied almost surely (with probability 1), resp. with positive probability (with probability greater than 0). For POMDPs with limit-average payoff, where a reward value in the interval [0,1] is associated to every transition, and the payoff of an infinite path is the long-run average of the rewards, we consider two types of path constraints: (i) a quantitative limit-average constraint defines the set of paths where the payoff is at least a given threshold L1 = 1. Our main results for qualitative limit-average constraint under almost-sure winning are as follows: (i) the problem of deciding the existence of a finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding the existence of an infinite-memory controller is undecidable. For quantitative limit-average constraints we show that the problem of deciding the existence of a finite-memory controller is undecidable. We present a prototype implementation of our EXPTIME algorithm. For POMDPs with w-regular conditions specified as parity objectives, while the qualitative analysis problems are known to be undecidable even for very special case of parity objectives, we establish decidability (with optimal complexity) of the qualitative analysis problems for POMDPs with parity objectives under finite-memory strategies. We establish optimal (exponential) memory bounds and EXPTIME-completeness of the qualitative analysis problems under finite-memory strategies for POMDPs with parity objectives. Based on our theoretical algorithms we also present a practical approach, where we design heuristics to deal with the exponential complexity, and have applied our implementation on a number of well-known POMDP examples for robotics applications. For POMDPs with a set of target states and an integer cost associated with every transition, we study the optimization objective that asks to minimize the expected total cost of reaching a state in the target set, while ensuring that the target set is reached almost surely. We show that for general integer costs approximating the optimal cost is undecidable. For positive costs, our results are as follows: (i) we establish matching lower and upper bounds for the optimal cost, both double and exponential in the POMDP state space size; (ii) we show that the problem of approximating the optimal cost is decidable and present approximation algorithms that extend existing algorithms for POMDPs with finite-horizon objectives. We show experimentally that it performs well in many examples of interest. We study more deeply the problem of almost-sure reachability, where given a set of target states, the question is to decide whether there is a strategy to ensure that the target set is reached almost surely. While in general the problem EXPTIME-complete, in many practical cases strategies with a small amount of memory suffice. Moreover, the existing solution to the problem is explicit, which first requires to construct explicitly an exponential reduction to a belief-support MDP. We first study the existence of observation-stationary strategies, which is NP-complete, and then small-memory strategies. We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. We report experimental results demonstrating the scalability of our symbolic (SAT-based) approach. Decentralized POMDPs (DEC-POMDPs) extend POMDPs to a multi-agent setting, where several agents operate in an uncertain environment independently to achieve a joint objective. In this work we consider Goal DEC-POMDPs, where given a set of target states, the objective is to ensure that the target set is reached with minimal cost. We consider the indefinite-horizon (infinite-horizon with either discounted-sum, or undiscounted-sum, where absorbing goal states have zero-cost) problem. We present a new and novel method to solve the problem that extends methods for finite-horizon DEC-POMDPs and the real-time dynamic programming approach for POMDPs. We present experimental results on several examples, and show that our approach presents promising results. In the end we present a short summary of a few other results related to verification of MDPs and POMDPs.},
author = {Chmelik, Martin},
pages = {232},
publisher = {IST Austria},
title = {{Algorithms for partially observable markov decision processes}},
year = {2016},
}