Estimating Network Kinetics of the MAPK/ERK Pathway Using Biochemical Data

The MAPK/ERK pathway is a major signal transduction system which regulates many fundamental cellular processes including the growth control and the cell death. As a result of these roles, it has a crucial importance in cancer as well as normal developmental processes. Therefore, it has been intensively studied resulting in a wealth of knowledge about its activation. It is also well documented that the activation kinetics of the pathway is crucial to determine the nature of the biological response. However, while individual biochemical steps are well characterized, it is still difficult to predict or even understand how the activation kinetics works. The aim of this paper is to estimate the stochastic rate constants of the MAPK/ERK network dynamics. Accordingly, taking a Bayesian approach, we combined underlying qualitative biological knowledge in several competing dynamic models via sets of quasireactions and estimated the stochastic rate constants of these reactions. Comparing the resulting estimates via the BIC and DIC criteria, we chose a biological model which includes EGFR degradation—Raf-MEK-ERK cascade without the involvement of RKIPs.

Numerical methods for queues with shared service

A queueing system is a mathematical abstraction of a situation where elements, called customers, arrive in a system and wait until they receive some kind of service. Queueing systems are omnipresent in real life. Prime examples include people waiting at a counter to be served, airplanes waiting to take off, traffic jams during rush hour etc. Queueing theory is the mathematical study of queueing phenomena. As often neither the arrival instants of the customers nor their service times are known in advance, queueing theory most often assumes that these processes are random variables. The queueing process itself is then a stochastic process and most often also a Markov process, provided a proper description of the state of the queueing process is introduced. This dissertation investigates numerical methods for a particular type of Markovian queueing systems, namely queueing systems with shared service. These queueing systems differ from traditional queueing systems in that there is simultaneous service of the head-of-line customers of all queues and in that there is no service if there are no customers in one of the queues. The absence of service whenever one of the queues is empty yields particular dynamics which are not found in traditional queueing systems. These queueing systems with shared service are not only beautiful mathematical objects in their own right, but are also motivated by an extensive range of applications. The original motivation for studying queueing systems with shared service came from a particular process in inventory management called kitting. A kitting process collects the necessary parts for an end product in a box prior to sending it to the assembly area. The parts and their inventories being the customers and queues, we get ``shared service'' as kitting cannot proceed if some parts are absent. Still in the area of inventory management, the decoupling inventory of a hybrid make-to-stock/make-to-order system exhibits shared service. The production process prior to the decoupling inventory is make-to-stock and driven by demand forecasts. In contrast, the production process after the decoupling inventory is make-to-order and driven by actual demand as items from the decoupling inventory are customised according to customer specifications. At the decoupling point, the decoupling inventory is complemented with a queue of outstanding orders. As customisation only starts when the decoupling inventory is nonempty and there is at least one order, there is again shared service. Moving to applications in telecommunications, shared service applies to energy harvesting sensor nodes. Such a sensor node scavenges energy from its environment to meet its energy expenditure or to prolong its lifetime. A rechargeable battery operates very much like a queue, customers being discretised as chunks of energy. As a sensor node requires both sensed data and energy for transmission, shared service can again be identified. In the Markovian framework, "solving" a queueing system corresponds to finding the steady-state solution of the Markov process that describes the queueing system at hand. Indeed, most performance measures of interest of the queueing system can be expressed in terms of the steady-state solution of the underlying Markov process. For a finite ergodic Markov process, the steady-state solution is the unique solution of $N-1$ balance equations complemented with the normalisation condition, $N$ being the size of the state space. For the queueing systems with shared service, the size of the state space of the Markov processes grows exponentially with the number of queues involved. Hence, even if only a moderate number of queues are considered, the size of the state space is huge. This is the state-space explosion problem. As direct solution methods for such Markov processes are computationally infeasible, this dissertation aims at exploiting structural properties of the Markov processes, as to speed up computation of the steady-state solution. The first property that can be exploited is sparsity of the generator matrix of the Markov process. Indeed, the number of events that can occur in any state --- or equivalently, the number of transitions to other states --- is far smaller than the size of the state space. This means that the generator matrix of the Markov process is mainly filled with zeroes. Iterative methods for sparse linear systems --- in particular the Krylov subspace solver GMRES --- were found to be computationally efficient for studying kitting processes only if the number of queues is limited. For more queues (or a larger state space), the methods cannot calculate the steady-state performance measures sufficiently fast. The applications related to the decoupling inventory and the energy harvesting sensor node involve only two queues. In this case, the generator matrix exhibits a homogene block-tridiagonal structure. Such Markov processes can be solved efficiently by means of matrix-geometric methods, both in the case that the process has finite size and --- even more efficiently --- in the case that it has an infinite size and a finite block size. Neither of the former exact solution methods allows for investigating systems with many queues. Therefore we developed an approximate numerical solution method, based on Maclaurin series expansions. Rather than focussing on structural properties of the Markov process for any parameter setting, the series expansion technique exploits structural properties of the Markov process when some parameter is sent to zero. For the queues with shared exponential service and the service rate sent to zero, the resulting process has a single absorbing state and the states can be ordered such that the generator matrix is upper-diagonal. In this case, the solution at zero is trivial and the calculation of the higher order terms in the series expansion around zero has a computational complexity proportional to the size of the state space. This is a case of regular perturbation of the parameter and contrasts to singular perturbation which is applied when the service times of the kitting process are phase-type distributed. For singular perturbation, the Markov process has no unique steady-state solution when the parameter is sent to zero. However, similar techniques still apply, albeit at a higher computational cost. Finally we note that the numerical series expansion technique is not limited to evaluating queues with shared service. Resembling shared queueing systems in that a Markov process with multidimensional state space is considered, it is shown that the regular series expansion technique can be applied on an epidemic model for opinion propagation in a social network. Interestingly, we find that the series expansion technique complements the usual fluid approach of the epidemic literature

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

Stochastic Dynamic Programming and Stochastic Fluid-Flow Models in the Design and Analysis of Web-Server Farms

A Web-server farm is a specialized facility designed specifically for housing Web servers catering to one or more Internet facing Web sites. In this dissertation, stochastic dynamic programming technique is used to obtain the optimal admission control policy with different classes of customers, and stochastic uid- ow models are used to compute the performance measures in the network. The two types of network traffic considered in this research are streaming (guaranteed bandwidth per connection) and elastic (shares available bandwidth equally among connections). We first obtain the optimal admission control policy using stochastic dynamic programming, in which, based on the number of requests of each type being served, a decision is made whether to allow or deny service to an incoming request. In this subproblem, we consider a xed bandwidth capacity server, which allocates the requested bandwidth to the streaming requests and divides all of the remaining bandwidth equally among all of the elastic requests. The performance metric of interest in this case will be the blocking probability of streaming traffic, which will be computed in order to be able to provide Quality of Service (QoS) guarantees. Next, we obtain bounds on the expected waiting time in the system for elastic requests that enter the system. This will be done at the server level in such a way that the total available bandwidth for the requests is constant. Trace data will be converted to an ON-OFF source and fluid- flow models will be used for this analysis. The results are compared with both the mean waiting time obtained by simulating real data, and the expected waiting time obtained using traditional queueing models. Finally, we consider the network of servers and routers within the Web farm where data from servers flows and merges before getting transmitted to the requesting users via the Internet. We compute the waiting time of the elastic requests at intermediate and edge nodes by obtaining the distribution of the out ow of the upstream node. This out ow distribution is obtained by using a methodology based on minimizing the deviations from the constituent in flows. This analysis also helps us to compute waiting times at different bandwidth capacities, and hence obtain a suitable bandwidth to promise or satisfy the QoS guarantees. This research helps in obtaining performance measures for different traffic classes at a Web-server farm so as to be able to promise or provide QoS guarantees; while at the same time helping in utilizing the resources of the server farms efficiently, thereby reducing the operational costs and increasing energy savings

Symmetry and Mesoscopic Physics

Symmetry is one of the most important notions in natural science; it lies at the heart of fundamental laws of nature and serves as an important tool for understanding the properties of complex systems, both classical and quantum. Another trend, which has in recent years undergone intensive development, is mesoscopic physics. This branch of physics also combines classical and quantum ideas and methods. Two main directions can be distinguished in mesoscopic physics. One is the study of finite quantum systems of mesoscopic sizes. Such systems, which are between the atomic and macroscopic scales, exhibit a variety of novel phenomena and find numerous applications in creating modern electronic and spintronic devices. At the same time, the behavior of large systems can be influenced by mesoscopic effects, which provides another direction within the framework of mesoscopic physics. The aim of the present book is to emphasize the phenomena that lie at the crossroads between the concept of symmetry and mesoscopic physics

Report / Institute für Physik

The 2016 Report of the Physics Institutes of the Universität Leipzig presents a hopefully interesting overview of our research activities in the past year. It is also testimony of our scientific interaction with colleagues and partners worldwide. We are grateful to our guests for enriching our academic year with their contributions in the colloquium and within our work groups

Essays on Timing and Economic Behavior

Economic activities unfold over time. How does timing influence our choices? How do we control our timing? Economic agents are considered to satisfy their preferences in an optimal fashion subject to constraints. Each chapter in this thesis tackles a different one of these three elements where the timing of behavior is central. In the first chapter, I study the impact of loss aversion on preferences for labor versus leisure. In a real-effort lab experiment, I show that a worker's willingness to persevere in a task is influenced by information about task completion time. To directly assess the location and impact of reference dependence, I structurally estimate labor-leisure preferences with a novel econometric approach drawing on computational neuroscience. Once participants exceed an expectations-based reference point, their subjective values of time rise sharply, and they speed up at the cost of reduced work quality and forgone earnings. In the second chapter, I propose and implement a method to test the optimality of individual deliberative time allocation. I also conduct experiments to study perceptual decision making in both simple decisions, where the difference in values between better and worse choices is known, and complex decisions, where this value difference is uncertain. The test reveals significant departures from optimality when task difficulty and monetary incentives are varied. However, a recently developed model based on optimality provides an improvement in fit over its predecessor. In the third chapter, I investigate the effects of memory constraints on choice over sequentially presented options. In a study that combines experimental paradigms used to analyze memory and judgment separately, I find a close link between order effects in choice and in memory. I show that cognitive load stemming from either an externally-imposed distractor or naturally-occuring fatigue substantially weakens primacy effects. Thus disrupting memory encoding and consolidation can potentially alleviate bias in judgment.</p

Report / Institute für Physik

The 2014 Report of the Physics Institutes of the Universität Leipzig presents a hopefully interesting overview of our research activities in the past year. It is also testimony of our scientific interaction with colleagues and partners worldwide. We are grateful to our guests for enriching our academic year with their contributions in the colloquium and within the work groups. The open full professorship in the Institute for Experimental Physics I has been filled with an outstanding candidate. We could attract Prof. Ralf Seidel from the University of Münster. He is an expert in molecular biophysics that complements the existing strength in cellular biophysics. Prof. Hollands could fill all positions of his ERC Starting Grant, so that the work on the project \"Quantum Fields and Curvature – Novel Constructive Approach via Operator Product Expansion\" is now running at full pace. Within the Horizon 2020 project LOMID \"Large Cost-effective OLED Microdisplays and their Applications\" (2015-2017) with eight European partners including industry the semiconductor physics group contributes with transparent oxide devices. A joint laboratory for single ion implantation was established between the Leibniz-Institute for Surface Modification (IOM) and the university under the guidance of Profs. Rauschenbach and Meijer. The EU IRSES Network DIONICOS \"Dynamics of and in Complex Systems\", a consortium of 6 European and 12 non-European partners, including sites in England, France and Germany as well as in Russia, Ukraine, India, the United States and Venezuela, started in February 2014. In the next four years the Leipzig node headed by Prof. Janke will profit from the numerous international contacts this network provides. With a joint project, Prof. Kroy and Prof. Cichos participate in the newly established priority research programme SPP 1726 \"Microswimmers\", which started with a kick-off workshop in October 2014. In 2014 the International Graduate College \"Statistical Physics of Complex Systems\" run by the computational physics group has commenced its third 3-years granting period funded by Deutsch-Französische Hochschule (DFH-UFA). Besides the main partner Université de Lorraine in Nancy, France, now also Coventry University, UK, and the Institute for Condensed Matter Physis of the National Academy of Sciences of Ukraine in Lviv, Ukraine, participate as associated partners. During the last week of September the TCO2014 conference \"Transparent Conductive Oxides – Fundamentals and Applications\" took place in honor of the 100th anniversary of the death of Prof. Dr. KarlW. Bädeker. In 1907 Karl Bädeker had discovered transparent conductive materials and oxides in Leipzig. About a hundred participants joined for many invited talks from international experts, intense discussion and new cooperations. At the end of November the by now traditional 15th nternational Workshop on Recent Developments in Computational Physics \"CompPhys14\" organized by Prof. Janke took place in Leipzig. Around 60 scientists from over 10 different countries exchanged ideas and discussed recent progress in several fields of computational physics. Work has successfully continued in the Centers of Excellence (Sonderforschungsbereiche) SFB 762 \"Functionality ofOxide Interfaces\" and SFB TRR 102 \"Polymers under Multiple Constraints: Restricted and Controlled Molecular Order and Mobility\" (just renewed for 2015-2019). Our activities and success are only possible with the generous support fromvarious funding agencies for which we are very grateful and which is individually acknowledged in the brief reports