Saad Mneimneh

Conserved Processes and Lineage-Specific Proteins in Fungal Cell Wall Evolution†

ABSTRACT The cell wall is a defining organelle that differentiates fungi from its sister clades in the opisthokont superkingdom. With a sensitive technique to align low-complexity protein sequences, we have identified 187 cell wall-related proteins in Saccharomyces cerevisiae and determined the presence or absence of homologs in 17 other fungal genomes. There were both conserved and lineage-specific cell wall proteins, and the degree of conservation was strongly correlated with protein function. Some functional classes were poorly conserved and lineage specific: adhesins, structural wall glycoprotein components, and unannotated open reading frames. These proteins are primarily those that are constituents of the walls themselves. On the other hand, glycosyl hydrolases and transferases, proteases, lipases, proteins in the glycosyl phosphatidyl-inositol-protein synthesis pathway, and chaperones were strongly conserved. Many of these proteins are also conserved in other eukaryotes and are associated with wall synthesis in plants. This gene conservation, along with known similarities in wall architecture, implies that the basic architecture of fungal walls is ancestral to the divergence of the ascomycetes and basidiomycetes. The contrasting lineage specificity of wall resident proteins implies diversification. Therefore, fungal cell walls consist of rapidly diversifying proteins that are assembled by the products of an ancestral and conserved set of genes.

Switching using parallel input-output queued switches with no speedup

We propose an efficient parallel switching architecture that requires no speedup and guarantees bounded delay. Our architecture consists of k input-output-queued switches with first-in-first-out queues, operating at the line speed in parallel under the control of a single scheduler, with k being independent of the number N of inputs and outputs. Arriving traffic is demultiplexed (spread) over the k identical switches, switched to the correct output, and multiplexed (combined) before departing from the parallel switch.We show that by using an appropriate demultiplexing strategy at the inputs and by applying the same matching at each of the k parallel switches during each cell slot, our scheme guarantees a way for cells of a flow to be read in order from the output queues of the switches, thus, eliminating the need for cell resequencing. Further, by allowing the scheduler to examine the state of only the first of the k parallel switches, our scheme also reduces considerably the amount of state information required by the scheduler. The switching algorithms that we develop are based on existing practical switching algorithms for input-queued switches, and have an additional communication complexity that is optimal up to a constant factor.

Crossing Over…Markov Meets Mendel

Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendels famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage—one of the first efforts towards a computational approach to biology—relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendels laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendels laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the mathematically more elaborate parts. I kept those as separate sections in the exposition.

On the Approximation of Optimal Structures for RNA-RNA Interaction

The interaction of two RNA molecules is a common mechanism for many biological processes. Small interfering RNAs represent a simple example of such an interaction. But other more elaborate instances of RNA-RNA interaction exist. Therefore, algorithms that predict the structure of the RNA complex thus formed are of great interest. Most of the proposed algorithms are based on dynamic programming. RNA-RNA interaction is generally NP-complete; therefore, these algorithms (and other polynomial time algorithms for that matter) are not expected to produce optimal structures. Our goal is to characterize this suboptimality. We demonstrate the existence of constant factor approximation algorithms that are based on dynamic programming. In particular, we describe 1/2 and 2/3 factor approximation algorithms. We define an entangler and prove that 2/3 is a theoretical upper bound on the approximation factor of algorithms that produce entangler-free solutions, e.g., the mentioned dynamic programming algorithms.

On achieving throughput in an input-queued switch

We establish some lower bounds on the speedup required to achieve throughput for some classes of switching algorithms in a input-queued switch with virtual output queues (VOQs). We use a weak notion of throughput, which will only strengthen the results, since an algorithm that cannot achieve weak throughput cannot achieve stronger notions of throughput. We focus on priority switching algorithms, i.e., algorithms that assign priorities to VOQs and forward packets of high priority first. We show a lower bound on the speedup for two fairly general classes of priority switching algorithms: input priority switching algorithms and output priority switching algorithms. An input priority scheme prioritizes the VOQs based on the state of the input queues, while an output priority scheme prioritizes the VOQs based on their output ports. We first show that, for output priority switching algorithms, a speedup S ≥ 2 is required to achieve weak throughput. From this, we deduce that both maximal and maximum size matching switching algorithms do not imply weak throughput unless S ≥ 2. The bound of S ≥ 2 is tight in all cases above, based on a result in Dai et al. Finally, we show that a speedup S ≥ 3/2 is required for the class of input priority switching algorithms to achieve weak throughput.

Matching from the first iteration: an iterative switching algorithm for an input queued switch

An iterative switching algorithm for an input queued switch consists of a number of iterations in every time step, where each iteration computes a disjoint matching. If input i is matched to output j in a given iteration, a packet (if any) is forwarded from i to j in the corresponding time step. Most of the iterative switching algorithms use a Request Grant Accept (RGA) arbitration type (e.g. iSLIP). Unfortunately, due to this particular type of arbitration, the matching computed in one iteration is not necessarily maximal (more input and output ports can still be matched). This is exactly why multiple iterations are needed. However, multiple iterations make the time step larger and reduce the speed of the switch. We present a new iterative switching algorithm (based on the RGA arbitration) called π-RGA with the underlying assumption that the number of iterations is possibly limited to one, hence reducing the time step and allowing the switch to run at a higher speed. We prove that π-RGA achieves throughput and delay guarantees with a speedup of 2 and one iteration under a constant burst traffic model, which makes π-RGA as good as any maximal matching algorithm in the theoretical sense. We also show by simulation that π-RGA achieves relatively high throughput in practice under uniform and non-uniform traffic patterns with one iteration and no speedup.

A Combinatorial Approach for Multiple RNA Interaction: Formulations, Approximations, and Heuristics

The interaction of two RNA molecules involves a complex interplay between folding and binding that warranted recent developments in RNA-RNA interaction algorithms. However, biological mechanisms in which more than two RNAs take part in an interaction exist.

A game-theoretic and stochastic survivability mechanism against induced attacks in Cognitive Radio Networks ☆

Abstract Cognitive Radio Networks (CRNs) are envisioned to provide a solution to the scarcity of the available frequency spectrum. It allows unlicensed secondary users (SUs) to use spectrum bands that are not occupied by licensed primary users (PUs) in an opportunistic manner. This dynamic manner of spectrum access gives rise to vulnerabilities that are unique to CRNs. In the battle over the available spectrum, SUs do not have any means of identifying whether disruption sensed on a band is intentional or unintentional. This problem is further intensified in the case of heterogeneous spectrum, where different bands provide different utilities. A smart malicious agent can use this vulnerability to temporarily disrupt transmissions on certain bands and induce their unavailability on SUs. The motivation for such disruption-induced attacks can be either monopolism, i.e. to capture as much spectrum as possible and make other SUs starve, or denial of service by intentional disruption of other SUs’ communications. This paper proposes an adaptive strategy for robust dynamic spectrum access in the event of induced attacks. Assuming rational players, and considering the notion of channel utility, the optimal strategy is established by modeling such scenarios as zero-sum games that lead to Nash equilibrium. Thereafter, the case of non-stationary channel utilities is investigated, where utilities are subject to abrupt changes due to fluctuations in channel characteristics, as well as arrival and departure of PUs. Through concurrent estimation, learning, and optimal play, it is shown that the proposed mechanism performs robustly even in such dynamic environments. Comparison of the proposed mechanism to other reasonable benchmark strategies in simulation confirms that this mechanism significantly enhances the performance of CRNs.

Multiple RNA Interaction with Sub-optimal Solutions

The interaction of two RNA molecules involves a complex interplay between folding and binding that warranted recent developments in RNA-RNA interaction algorithms. However, biological mechanisms in which more than two RNAs take part in an interaction exist. It is reasonable to believe that interactions involving multiple RNAs are generally more complex to be treated pairwise. In addition, given a pool of RNAs, it is not trivial to predict which RNAs are interacting without sufficient biological knowledge. Therefore, structures resulting from multiple RNA interactions often cannot be predicted by the existing algorithms.

Linear complexity algorithms for maximum advance deflection routing in some networks

We consider routing in a network with no buffers at intermediate nodes: packets must move in a synchronized manner in every time step until they reach their destinations. If contention prevents a packet from advancing, i.e. taking an outgoing link on a shortest path from its current node to its destination, it is deflected on a different link, hence the name deflection routing. One common strategy in the design of deflection routing algorithms is maximum advance, which advances a maximum number of packets at every node in every time step. We examine two settings: non capacitated networks and capacitated networks. We present linear complexity algorithms for maximum advance deflection routing in networks with topological properties as follows: When the network is non capacitated, we require that each packet can advance on at most two links from any intermediate node in the network. When the network is capacitated, we require a special condition on the links in addition to the one mentioned above. Metropolitan and wide area networks typically satisfy those conditions