Continuous computation in engineered gene circuits

In this paper we consider the problem of representation and measurement in genetic circuits, and investigate how they can affect the reliability of engineered systems. We propose a design scheme, based on the notion of continuous computation, which addresses these issues. We illustrate the methodology by showing how a concept from computer architecture (namely, branch prediction) may be implemented in vivo, using a distributed approach. Simulation results confirm the in-principle feasibility of our method, and offer valuable insights into its future laboratory validation.     

Strategic allocation of cyclically calling vessels for multi-terminal container operators

We consider a terminal operator who provides container handling services at multiple terminals within the same port. In this setting, the well-known berth allocation problem can no longer be considered for each terminal in isolation since vessel calls should be spread over the various terminals to avoid peaks and troughs in quay crane utilization, and an allocation of two connecting vessels to different terminals will generate inter-terminal container transport. In this paper, we address the problem of spreading a set of cyclically calling vessels over the various terminals and allocating a berthing and departure time to each of them. The objectives are (1) to balance the quay crane workload over the terminals and over time and (2) to minimize the amount of inter-terminal container transport. We develop a solution approach based on mixed-integer programming that allows to solve real-life instances of the problem within satisfactory time. Additionally, a practical case study is presented based on data from the terminal operator PSA Antwerp who operates multiple terminals in the port of Antwerp, Belgium. The computational results show the cost of the currently agreed schedules, and that relatively small modifications can significantly reduce the required crane capacities and inter-terminal transport.     

A new approach to the Container Positioning Problem

In this paper the Container Positioning Problem is revisited. This problem arises at busy container terminals and requires one to minimize the use of block cranes in handling the containers that must wait at the terminal until their next means of transportation. We propose a new Mixed Integer Programming model that not only improves on earlier attempts at this problem, but also better reflects reality. In particular, the proposed model adopts a preference to reshuffle containers in line with a just-in-time concept, as it is assumed that data is more accurate the closer to a container’s scheduled departure the time is. Other important improvements include a reduction in the model size, and the ability of the model to consider containers initially at the terminal. In addition, we describe several classes of valid inequalities for this new formulation and present a rolling horizon based heuristic for solving larger instances of the problem. We show that this new formulation drastically outperforms previous attempts at the problem through a direct comparison on instances available in the literature. Furthermore, we also show that the rolling horizon based heuristic can further reduce the solution time on the larger of these instances as well as find acceptable solutions to much bigger, artificially generated, instances.     

A cooperative quay crane-based stochastic model to estimate vessel handling time

Having a good estimate of a vessel’s handling time is essential for planning and scheduling container terminal resources, such as berth positions, quay cranes (QCs) and transport vehicles. However, estimating the expected vessel handling time is not straightforward, because it depends on vessel characteristics, resource allocation decisions, and uncertainties in terminal processes. To estimate the expected vessel handling time, we propose a two-level stochastic model. The higher level model consists of a continuous-time Markov chain (CTMC) that captures the effect of QC assignment and scheduling on vessel handling time. The lower level model is a multi-class closed queuing network that models the dynamic interactions among the terminal resources and provides an estimate of the transition rate input parameters to the higher level CTMC model. We estimate the expected vessel handling times for several container load and unload profiles and discuss the effect of terminal layout parameters and crane service time variabilities on vessel handling times. From numerical experiments, we find that by having QCs cooperate, the vessel handling times are reduced by up to 15 %. The vessel handling time is strongly dependent on the variation in the QC service time and on the vehicle travel path topology.     

A New Cyclic Scheduling Algorithm for Flexible Manufacturing Systems

Flexible manufacturing system control is an NP-hard problem. A cyclic approach has been demonstrated to be adequate for an infinite scheduling problem because of maximal throughput reachability. However, it is not the only optimization criterion in general. In this article we consider the minimization of the work in process (WIP) as an economical and productivity factor. We propose a new cyclic scheduling algorithm giving the maximal throughput (a hard constraint) while minimizing WIP. This algorithm is based on progressive operations placing. A controlled beam search approach has been developed to determine at each step the schedule of the next operations. After presenting the main principles of the algorithm, we compare our approach to several most known cyclic scheduling algorithms using a significant existing example from the literature.     

Clutch size, offspring performance, and intergenerational fitness

It is now generally recognized that clutch size affects morethan offspring number. In particular, clutch size affects asuite of traits associated with offspring reproductive performance.Optimal clutch size is therefore determined not by the numericallymost productive clutch but by the clutch that maximizes collectiveoffspring reproductive success. Calculation of optimal clutchsize thus requires a consideration of ecological factors operatingduring an intergenerational time frame, spanning the lifetimeof the egglaying adult and the lifetimes of her offspring. Theoptimal clutch cannot define reproductive values in advance,but instead requires that the strategy chosen is the best responseto the set of reproductive values that it itself generates.In this article, we introduce methods for solving this problem,based on an iterative solution of the equation characterizingexpected lifetime reproductive success. We begin by consideringa semelparous organism, in which case lifetime reproductivesuccess is a function only of the state of the organism. Foran iteroparous organism, lifetime reproductive success dependsupon both state and time, so that our methods extend the usualstochastic dynamic programming approach to the evaluation oflifetime reproductive success. The methods are intuitive andeasily used. We consider both semelparous and iteroparous organisms,stable and varying environments, and describe how our methodscan be employed empirically.     

Attribute-Based Proxy Re-Encryption with Keyword Search

Keyword search on encrypted data allows one to issue the search token and conduct search operations on encrypted data while still preserving keyword privacy. In the present paper, we consider the keyword search problem further and introduce a novel notion called attribute-based proxy re-encryption with keyword search (), which introduces a promising feature: In addition to supporting keyword search on encrypted data, it enables data owners to delegate the keyword search capability to some other data users complying with the specific access control policy. To be specific, allows (i) the data owner to outsource his encrypted data to the cloud and then ask the cloud to conduct keyword search on outsourced encrypted data with the given search token, and (ii) the data owner to delegate other data users keyword search capability in the fine-grained access control manner through allowing the cloud to re-encrypted stored encrypted data with a re-encrypted data (embedding with some form of access control policy). We formalize the syntax and security definitions for , and propose two concrete constructions for : key-policy and ciphertext-policy . In the nutshell, our constructions can be treated as the integration of technologies in the fields of attribute-based cryptography and proxy re-encryption cryptography.     

A reduced-order model for wall shear stress in abdominal aortic aneurysms by proper orthogonal decomposition

In this paper, we introduce a method to construct a Reduced-Order Model (ROM) to study the physiological flow and the Wall Shear Stress (WSS) conditions in Abdominal Aortic Aneurysms (AAA). We start the process by running a training case using Computational Fluid Dynamics (CFD) simulations with time-varying flow parameters, such that these parameters cover the range of parameters that we would like to consider in our ROM. We use the inflow angle as the variable parameter in the current study. Then we use the snapshot Proper Orthogonal Decomposition (POD) to construct the reduced-order bases, which are subsequently enhanced using a QR-factorization technique to satisfy the relevant fluid boundary conditions. The resulting ROM enables us to study the flow pattern and the WSS distribution over a range of system parameters computationally very efficiently. We have used this method to show how the WSS varies significantly for an AAA with a simplified geometry, over a range of inflow angles usually considered mild in clinical terms. We have validated the ROM results with CFD results. This approach enables comprehensive analysis of the model system across a range of inflow angles and frequencies without the need to re-compute the simulation for small changes.     

A random time interval approach for analysing the impact of a possible intermediate event on a terminal event

We consider the impact of a possible intermediate event on a terminal event in an illness-death model with states 'initial', 'intermediate' and 'terminal'. One aim is to unambiguously describe the occurrence of the intermediate event in terms of the observable data, the problem being that the intermediate event may not occur. We propose to consider a random time interval, whose length is the time spent in the intermediate state. We derive an estimator of the joint distribution of the left and right limit of the random time interval from the Aalen-Johansen estimator of the matrix of transition probabilities and study its asymptotic properties. We apply our approach to hospital infection data. Estimating the distribution of the random time interval will usually be only a first step of an analysis. We illustrate this by analysing change in length of hospital stay following an infection and derive the large sample properties of the respective estimator.     

The optimal configuration and workload allocation problem in flexible manufacturing systems

In this article we consider the problem of determining the minimum cost configuration (number of machines and pallets) for a flexible manufacturing system with the constraint of meeting a prespecified throughput, while simultaneously allocating the total workload among the machines (or groups of machines). Our procedure allows consideration of upper and lower bounds on the workload at each machine group. These bounds arise as a consequence of precedence constraints among the various operations and/or limitations on the number or combinations of operations that can be assigned to a machine because of constraints on tool slots or the space required to store assembly components. Earlier work on problems of this nature assumes that the workload allocation is given. For the single-machine-type problem we develop an efficient implicit enumeration procedure that uses fathoming rules to eliminate dominated configurations, and we present computational results. We discuss how this procedure can be used as a building block in solving the problem with multiple machine types.     

Operating room scheduling and rescheduling: a rolling horizon approach

In this work we consider the problem of selecting a set of patients among a given waiting list of elective patients and assigning them to a set of available operating room blocks. We assume a block scheduling strategy in which the number and the length of available blocks are given. As each block is related to a specific day, by assigning a patient to a block his/her surgery date is fixed, as well. Each patient is characterized by a recommended maximum waiting time and an uncertain surgery duration. In practical applications, new patients enter the waiting list continuously. Patient selection and assignment is performed by surgery departments on a short-term, usually a week, regular base. We propose a so-called rolling horizon approach for the patient selection and assignment. At each iteration short-term patient assignment is decided. However, in a look-ahead perspective, a longer planning horizon is considered when looking for the patient selection. The mid-term assignment over the next \(n\) weeks is generated by solving an ILP problem, minimizing a penalty function based on total waiting time and tardiness of patients. The approach is iteratively applied by shifting ahead the mid-term planning horizon. When applying the first week solution, unpredictable extensions of surgeries may disrupt the schedule. Such disruptions are recovered in the next iteration: the mid-term solution is rescheduled limiting the number of variations from the previously computed plan. Besides, the approach allows to deal with new patient arrivals. To keep limited the number of disruptions due to uncertain surgery duration, we propose also a robust formulation of the ILP problem. The deterministic and the robust formulation based frameworks are compared over a set of instances, including different stochastic realization of surgery times.     

The “molecular” traveling salesman

We consider a method for optimization of NP-problems motivated by natural evolution. The basic entity is a population of individuals searching in state space defined by the problem. A message exchange mechanism between individuals enables the system to proceed fast and to avoid local optima. We introduce the concept of isolated evolution to maintain a certain degree of variance in the population. The global optimum can be approached to an arbitrary degree. The method is applied to the TSP and its behavior is shown in a couple of simulations.On leave from: Institut für Theoretische Physik und Synergetik, Universität Stuttgart, Pfaffenwaldring 57/IV, D-7000 Stuttgart 80, Federal Republic of Germany     

Models for capacity acquisition decisions considering operational costs

In this article, we study a capacity acquisition problem by considering technology choice and operational factors in a stochastic environment. The motivation for our work comes from developments in modern flexible technologies and a problem encountered in a real industrial setting. We study the impact of operational factors such as setup times, demand patterns, and inventory/back order costs on the decisions of capacity acquisition and technology choice. We consider three alternatives in capacity and technology decisions: (i) a flexible system, (ii) a dedicated system, and (iii) a combination of these two systems. For each system, we develop a model that integrates investment decisions and operational decisions to determine an optimal amount of capacity to purchase and the time and the types of parts to produce. The objective is to minimize the capacity acquisition cost at the beginning of the planning horizon and the total expected operational costs over an infinite planning horizon. To solve the problem in this article, a solution procedure is proposed. Managerial insights are also derived from extensive computational results.     

Open-Phylo: a customizable crowd-computing platform for multiple sequence alignment

Citizen science games such as Galaxy Zoo, Foldit, and Phylo aim to harness the intelligence and processing power generated by crowds of online gamers to solve scientific problems. However, the selection of the data to be analyzed through these games is under the exclusive control of the game designers, and so are the results produced by gamers. Here, we introduce Open-Phylo, a freely accessible crowd-computing platform that enables any scientist to enter our system and use crowds of gamers to assist computer programs in solving one of the most fundamental problems in genomics: the multiple sequence alignment problem.     

Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self-Selected Right Censoring

We investigate the use of follow-up samples of individuals to estimate survival curves from studies that are subject to right censoring from two sources: (i) early termination of the study, namely, administrative censoring, or (ii) censoring due to lost data prior to administrative censoring, so-called dropout. We assume that, for the full cohort of individuals, administrative censoring times are independent of the subjects' inherent characteristics, including survival time. To address the loss to censoring due to dropout, which we allow to be possibly selective, we consider an intensive second phase of the study where a representative sample of the originally lost subjects is subsequently followed and their data recorded. As with double-sampling designs in survey methodology, the objective is to provide data on a representative subset of the dropouts. Despite assumed full response from the follow-up sample, we show that, in general in our setting, administrative censoring times are not independent of survival times within the two subgroups, nondropouts and sampled dropouts. As a result, the stratified Kaplan-Meier estimator is not appropriate for the cohort survival curve. Moreover, using the concept of potential outcomes, as opposed to observed outcomes, and thereby explicitly formulating the problem as a missing data problem, reveals and addresses these complications. We present an estimation method based on the likelihood of an easily observed subset of the data and study its properties analytically for large samples. We evaluate our method in a realistic situation by simulating data that match published margins on survival and dropout from an actual hip-replacement study. Limitations and extensions of our design and analytic method are discussed.     

Towards multi-scale modeling of muscle fibers with sarcomere non-uniformities

A theoretical framework for studying the collective behavior of a large ensemble of half sarcomeres in a myofibril is presented. The approach is based on transforming the large system of discrete elements (half-sarcomeres) into a continuum for which macro-behavior is dictated by micro-properties. Specifically, we consider statistical properties of the ensemble rather than solving for each degree of freedom. This enables a reasonable computational effort and provides important insights. We demonstrate that such a multi-scale approach is indispensable for studying quantitatively the role of sarcomere non-uniformities in muscle mechanics. Specifically, we illustrate that adopting a model with a non-physiological number of sarcomeres can lead to a non-realistic behavior and therefore to erroneous interpretation. Further, we demonstrate that the new modeling approach provides a suitable platform for addressing controversial phenomena, such as residual enhanced tension, creep, length redistribution, and damage due to eccentric contraction.     

Molecular solutions to the binary integer programming problem based on DNA computation

Binary optimization is a widely investigated topic in integer linear programming. This study proposes a DNA-based computing algorithm for solving the significantly large binary integer programming (BIP) problem. The proposed approach is based upon Adleman and Lipton's DNA operations to solve the BIP problem. The potential of DNA computation for the BIP problem is promising given the operational time complexity of O(nxk).     

An evolutionary computing approach for parameter estimation investigation of a model for cholera

We consider the problem of using time-series data to inform a corresponding deterministic model and introduce the concept of genetic algorithms (GA) as a tool for parameter estimation, providing instructions for an implementation of the method that does not require access to special toolboxes or software. We give as an example a model for cholera, a disease for which there is much mechanistic uncertainty in the literature. We use GA to find parameter sets using available time-series data from the introduction of cholera in Haiti and we discuss the value of comparing multiple parameter sets with similar performances in describing the data.