James Willson

Glucose Deprivation Contributes to the Development of KRAS Pathway Mutations in Tumor Cells

Desperately Seeking Glucose Mutations in oncogenes and tumor suppressor genes allow cancer cells to outgrow their neighboring healthy cells. What microenvironmental conditions provide a selective growth advantage to these cells? Yun et al. (p. 1555, published online 6 August) identify low glucose availability as a microenvironmental factor driving the acquisition of KRAS oncogenic mutations that allow cancer cells to survive and grow. In genetically matched colorectal cancer cells that differed only in the mutational status of the KRAS oncogene, mutant cells selectively overexpressed glucose transporter-1 and exhibited enhanced glucose uptake and glycolysis. When cells with wild-type KRAS were placed in a low-glucose environment, very few cells survived but most of the survivors expressed high levels of glucose transporter-1, and a small percentage of the survivors had acquired new KRAS mutations. Thus, glucose deprivation may help to drive the acquisition of cell growth–promoting oncogenic mutations during tumor development. Glucose deprivation can drive the acquisition of certain oncogenic mutations in human cancer cells. Tumor progression is driven by genetic mutations, but little is known about the environmental conditions that select for these mutations. Studying the transcriptomes of paired colorectal cancer cell lines that differed only in the mutational status of their KRAS or BRAF genes, we found that GLUT1, encoding glucose transporter-1, was one of three genes consistently up-regulated in cells with KRAS or BRAF mutations. The mutant cells exhibited enhanced glucose uptake and glycolysis and survived in low-glucose conditions, phenotypes that all required GLUT1 expression. In contrast, when cells with wild-type KRAS alleles were subjected to a low-glucose environment, very few cells survived. Most surviving cells expressed high levels of GLUT1, and 4% of these survivors had acquired KRAS mutations not present in their parents. The glycolysis inhibitor 3-bromopyruvate preferentially suppressed the growth of cells with KRAS or BRAF mutations. Together, these data suggest that glucose deprivation can drive the acquisition of KRAS pathway mutations in human tumors.

Efficient Algorithms for Topology Control Problem with Routing Cost Constraints in Wireless Networks

Topology control is one vital factor to a wireless networks efficiency. A Connected Dominating Set (CDS) can be a useful basis of a backbone topology construction. In this paper, a special CDS, named α Minimum routing Cost CDS (α-MOC-CDS), will be studied to improve the performance of CDS based broadcasting and routing. In this paper, we prove that construction of a minimum α-MOC-CDS is NP-hard in a general graph and we propose a heuristic algorithm for construction of α-MOC-CDS.

Constant-approximation for target coverage problem in wireless sensor networks

When a large amount of sensors are randomly deployed into a field, how can we make a sleep/activate schedule for sensors to maximize the lifetime of target coverage in the field? This is a well-known problem, called Maximum Lifetime Coverage Problem (MLCP), which has been studied extensively in the literature. It is a long-standing open problem whether MLCP has a polynomial-time constant-approximation. The best-known approximation algorithm has performance ratio 1 + ln n where n is the number of sensors in the network, which was given by Berman et. al [1]. In their work, MLCP is reduced to Minimum Weight Sensor Coverage Problem (MWSCP) which is to find the minimum total weight of sensors to cover a given area or a given set of targets with a given set of weighted sensors. In this paper, we present a polynomial-time (4 + ∈)-approximation algorithm for MWSCP and hence we obtain a polynomial-time (4 + ξ)-approximation algorithm for MLCP, where ∈ >; 0, ξ >; 0.

A dominating and absorbent set in a wireless ad-hoc network with different transmission ranges

Unlike a cellular or wired network, there is no base station or network infrastructure in a wireless ad-hoc network, in which nodes communicate with each other via peer communications. In order to make routing and flooding efficient in such an infrastructureless network, Connected Dominating Set (CDS) as a virtual backbone has been extensively studied. Most of the existing studies on the CDS problem have focused on unit disk graphs, where every node in a network has the same transmission range. However, nodes may have different powers due to difference in functionalities, power control, topology control, and so on. In this case, it is desirable to model such a network as a disk graph where each node has different transmission range. In this paper, we define Minimum Strongly Connected Dominating and Absorbent Set (MSCDAS) in a disk graph, which is the counterpart of minimum CDS in unit disk graph. We propose a constant approximation algorithm when the ratio of the maximum to the minimum in transmission range is bounded. We also present two heuristics and compare the performances of the proposed schemes through simulation.

EFFICIENT DISTRIBUTED ALGORITHMS FOR TOPOLOGY CONTROL PROBLEM WITH SHORTEST PATH CONSTRAINTS

A Connected Dominating Set (CDS) can be used to construct a virtual backbone for wireless and mobile ad-hoc networks to make the system hierarchical and efficient. A virtual backbone can significantly improve network throughput, optimize broadband utilization, extend network lifetime, and reduce interference as well as packet retransmissions. Calculating a minimum backbone for a network is critical to reduce routing computation and energy consumption. This problem is a well-known NP-hard optimization problem, which has various applications in practice. In this paper, we propose a new problem based on customer fairness, which looks for a minimum CDS in a given communication model with shortest path constraints. It guarantees that any two clients can communicate with each other through this CDS with hop counts the same as the best path from the original graph, which means that routing on such a CDS will not bring additional traffic for every client. We name this problem as shortest path connected dominating set (SPCDS) and prove its NP-hardness by reduction from Hitting Set .T hen we propose a centralized greedy algorithm and an efficient distributed approximation algorithm with approximation ratio ∆ to solve SPCDS, where ∆ is the maximum vertex degree in the given topology. We also analyze the time complexity, message complexity, and evaluate the efficiency of our distributed heuristic by several numerical experiments and comparisons with previous literatures.

Induction of KIAA1199/CEMIP is associated with colon cancer phenotype and poor patient survival

Genes induced in colon cancer provide novel candidate biomarkers of tumor phenotype and aggressiveness. We originally identified KIAA1199 (now officially called CEMIP) as a transcript highly induced in colon cancer: initially designating the transcript as Colon Cancer Secreted Protein 1. We molecularly characterized CEMIP expression both at the mRNA and protein level and found it is a secreted protein induced an average of 54-fold in colon cancer. Knockout of CEMIPreduced the ability of human colon cancer cells to form xenograft tumors in athymic mice. Tumors that did grow had increased deposition of hyaluronan, linking CEMIP participation in hyaluronan degradation to the modulation of tumor phenotype. We find CEMIP mRNA overexpression correlates with poorer patient survival. In stage III only (n = 31) or in combined stage II plus stage III colon cancer cases (n = 73), 5-year overall survival was significantly better (p = 0.004 and p = 0.0003, respectively) among patients with low CEMIP expressing tumors than those with high CEMIP expressing tumors. These results demonstrate that CEMIP directly facilitates colon tumor growth, and high CEMIP expression correlates with poor outcome in stage III and in stages II+III combined cohorts. We present CEMIP as a candidate prognostic marker for colon cancer and a potential therapeutic target.

Construction of directional virtual backbones with minimum routing cost in wireless networks

It is well-known that the application of directional antennas can help conserve bandwidth and energy consumption in wireless networks. Thus, to achieve efficiency in wireless networks, we study a special virtual backbone (VB) using directional antennas, requiring that from one node to any other node in the network, there exists at least one directional shortest path all of whose intermediate directions should belong to the VB, named as Minimum rOuting Cost Directional VB (MOC-DVB). In addition, VB has been well studied in Unit Disk Graph (UDG). However, radio wave based communications in wireless networks may be interrupted by obstacles (e.g., buildings and mountains). Thus, in this paper, we model a network as a general directed graph. We prove that construction of a minimum MOC-DVB is an NP-hard problem in a general directed graph and in term of the size of MOC-DVB, there exists an unreachable lower bound of the polynomial-time selected MOC-DVB. Therefore, we propose a distributed approximation algorithm for constructing MOC-DVB with approximation ratio of 1 + lnK + 2lnδD, where K is the number of antennas on each node and δD is the maximum direction degree in the network. Extensive simulations demonstrate that our constructed MOC-DVB is much more efficient in the sense of MOC-DVB size and routing cost compared to other VBs.

FAST INFORMATION PROPAGATION IN SOCIAL NETWORKS

Social networks are attracting more attention from both industry and academia. Prior works on social networks focus on analyzing their roles in information propagation and decision making. However, few of these works consider the latency of information propagation, which is an essential issue especially in time-critical scenarios in social networks. In this paper, we introduce a new problem called FAST INFORMATION PROPAGATION PROBLEM, which is an optimization problem to identify the minimum set of influential nodes that could influence the whole network within a given latency bound d. We show our complexity result on this problem in the deterministic threshold models and present a greedy hill-climbing algorithm as the solution. For the d = 1 case, we prove that under the majority threshold model, this approximation algorithm has a performance ratio of H(Δ + 1) - 1 + ⌈ Δ/2⌉. Extensive experiments are conducted on a large collaboration network and the results show that our approximation algorithm outperforms the node-selection heuristics, which utilize the well-known approaches based on the node centrality in the social network analysis.

On dual power assignment optimization for biconnectivity

Topology control is an important technology of wireless ad hoc networks to achieve energy efficiency and fault tolerance. In this paper, we study the dual power assignment problem for 2-edge connectivity and 2-vertex connectivity in the symmetric graphical model which is a combinatorial optimization problem from topology control technology.The problem is arisen from the following origin. In a wireless ad hoc network where each node can switch its transmission power between high-level and low-level, how can we establish a fault-tolerantly connected network topology in the most energy-efficient way? Specifically, the objective is to minimize the number of nodes assigned with high power and yet achieve 2-edge connectivity or 2-vertex connectivity.We addressed these optimization problems (2-edge connectivity and 2-vertex connectivity version) under the general graph model in (Wang et al. in Theor. Comput. Sci., 2008). In this paper, we propose a novel approximation algorithm, called Candidate Set Filtering algorithm, to compute nearly-optimal solutions. Specifically, our algorithm can achieve 3.67-approximation ratio for both 2-edge connectivity and 2-vertex connectivity, which improves the existing 4-approximation algorithms for these two cases.

On approximate optimal dual power assignment for biconnectivity and edge-biconnectivity

Topology control is one of the major approaches to achieve energy efficiency as well as fault tolerance in wireless networks. In this paper, we study the dual power assignment problem for 2-edge connectivity and 2-vertex connectivity in the symmetric graphical model. The problem has arisen from the following practical origin. In a wireless ad hoc network where each node can switch its transmission power between high-level and low-level, how can we establish a fault-tolerant connected network topology in the most energy-efficient way? Specifically, the objective is to minimize the number of nodes assigned with high power and yet achieve 2-edge connectivity or 2-vertex connectivity. Note that to achieve a minimum number of high-power nodes is harder than an optimization problem in the same model whose objective is to minimize the total power cost. We first address these two optimization problems (2-edge connectivity and 2-vertex connectivity version) under the general graph model. Due to the NP-hardness, we propose an approximation algorithm, called prioritized edge selection algorithm, which achieves a 4-ratio approximation for 2-edge connectivity. After that, we modify the algorithm to solve the problem for 2-vertex connectivity and also achieve the same approximation ratio. We also show that the 4-ratio is tight for our algorithms in both cases.