The weight of DNA evidence for forensic applications is typically assessed through the calculation of the likelihood ratio (LR). In the standard workflow, DNA is extracted from a collection of cells where the cells of an unknown number of donors are mixed. The DNA is then genotyped, and the LR is calculated through well-established methods. Recently, a method for calculating the LR from single-cell data has been presented. Rather than extracting the DNA while the cells are still mixed, single-cell data is procured by first isolating each cell. Extraction and fragment analysis of relevant forensic loci follows such that individual cells are genotyped. This workflow leads to significantly stronger weights of evidence, but it does not account for extracellular DNA that could also be present in the sample. In this paper, we present a method for calculation of an LR that combines single-cell and extracellular data. We demonstrate the calculation on example data and show that the combined LR can lead to stronger conclusions than would be obtained from calculating LRs on the single-cell and extracellular DNA separately.

BACKGROUND: In order to isolate an individual's genotype from a sample of biological material, most laboratories use PCR and Capillary Electrophoresis (CE) to construct a genetic profile based on polymorphic loci known as Short Tandem Repeats (STRs). The resulting profile consists of CE signal which contains information about the length and number of STR units amplified. For samples collected from the environment, interpretation of the signal can be challenging given that information regarding the quality and quantity of the DNA is often limited. The signal can be further compounded by the presence of noise and PCR artifacts such as stutter which can mask or mimic biological alleles. Because manual interpretation methods cannot comprehensively account for such nuances, it would be valuable to develop a signal model that can effectively characterize the various components of STR signal independent of a priori knowledge of the quantity or quality of DNA. RESULTS: First, we seek to mathematically characterize the quality of the profile by measuring changes in the signal with respect to amplicon size. Next, we examine the noise, allele, and stutter components of the signal and develop distinct models for each. Using cross-validation and model selection, we identify a model that can be effectively utilized for downstream interpretation. Finally, we show an implementation of the model in NOCIt, a software system that calculates the a posteriori probability distribution on the number of contributors. CONCLUSION: The model was selected using a large, diverse set of DNA samples obtained from 144 different laboratory conditions; with DNA amounts ranging from a single copy of DNA to hundreds of copies, and the quality of the profiles ranging from pristine to highly degraded. Implemented in NOCIt, the model enables a probabilisitc approach to estimating the number of contributors to complex, environmental samples.

SUMMARY: Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). AVAILABILITY AND IMPLEMENTATION: MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: MOST (metabolic optimization and simulation tool) is a software package that implements GDBB (genetic design through branch and bound) in an intuitive user-friendly interface with excel-like editing functionality, as well as implementing FBA (flux balance analysis), and supporting systems biology markup language and comma-separated values files. GDBB is currently the fastest algorithm for finding gene knockouts predicted by FBA to increase production of desired products, but GDBB has only been available on a command line interface, which is difficult to use for those without programming knowledge, until the release of MOST. AVAILABILITY AND IMPLEMENTATION: MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. CONTACT: [email protected].

The problem of establishing minimum-cost multicast connections in coded networks can be viewed as an optimization problem, and decentralized algorithms were proposed by Lun to compute the optimal subgraph using the dual subgradient method. However, the convergence rate problem for these algorithms remains open. There are limited results in the literature, which bound the amount of infeasibility of the primal solution recovered after each iterations or the convergence rate. However, due to the special structure of the network coding problem, we have an algorithm that generates a feasible solution after each iterations. In addition, the convergence rate of the primal problem is O(1/n) to a neighborhood of the optimal solution. We also propose heuristics to further improve our algorithm and demonstrate through simulations that the distributed algorithm converges to the optimal subgraph quickly and is robust against network topology changes.

We take a theoretical approach to the problem of identification, or “reverse engineering”, of gene regulatory networks. Through a mathematical model of a gene regulatory network, we examine fundamental questions on the limits and achievability of network identification. We apply simplifying assumptions to construct an acyclic binary model, and we assume that the identification strategy is restricted to perturbing the network by gene expression assignments, followed by expression profile measurements at steady-state. Further, we assume the presence of side information, which we call sensitivity, that is likely to be present in actual gene networks. We show that with sensitivity side information and realistic topology assumptions we can identify the topology of acyclic binary networks using O(n) assignments and measurements, n being the number of genes in the network. Our work establishes a theoretical framework for examining an important technological problem where a number of significant questions remain open.

MOTIVATION: Computer-aided genetic design is a promising approach to a core problem of metabolic engineering-that of identifying genetic manipulation strategies that result in engineered strains with favorable product accumulation. This approach has proved to be effective for organisms including Escherichia coli and Saccharomyces cerevisiae, allowing for rapid, rational design of engineered strains. Finding optimal genetic manipulation strategies, however, is a complex computational problem in which running time grows exponentially with the number of manipulations (i.e. knockouts, knock-ins or regulation changes) in the strategy. Thus, computer-aided gene identification has to date been limited in the complexity or optimality of the strategies it finds or in the size and level of detail of the metabolic networks under consideration. RESULTS: Here, we present an efficient computational solution to the gene identification problem. Our approach significantly outperforms previous approaches--in seconds or minutes, we find strategies that previously required running times of days or more. AVAILABILITY AND IMPLEMENTATION: GDBB is implemented using MATLAB and is freely available for non-profit use at http://crab.rutgers.edu/~dslun/gdbb.

This paper addresses the problem of inter-session network coding to maximize throughput for multiple communication sessions in wireless networks. We introduce virtual multicast connections which can extract packets from original sessions and code them together. Random linear network codes can be used for these virtual multicasts. The problem can be stated as a flow-based convex optimization problem with side constraints. The proposed formulation provides a rate region which is at least as large as the region without inter-session network coding. We show the benefits of our technique for several scenarios by means of simulation.

This paper provides a solution to the question of how, when and where to perform inter-session network coding for a general network model both under wired and wireless conditions. In particular, an original queuing architecture and a dynamic routing-scheduling-coding strategy are introduced for serving multiple sessions when linear network coding is allowed across sessions. This policy provides a novel extension to the class of back-pressure policies by incorporating inter-session coding decisions via simple rules on the relevant queue-length levels. Despite the fact that the capacity region of inter-session coding is a challenging open problem, in this paper, we prove that our algorithm can support any set of rates in a nontrivial characterized region of achievable rates. In addition to its practical implications, this work also provides a theoretical framework in which the gains of inter-session network coding and pure routing can be compared.

Automated estimation of DNA base-sequences is an important step in genomics and in many other emerging fields in biological and medical sciences. Current automated sequencers process single strands only. To improve the utility of existing technologies, we propose to mix two independent strands prior to electrophoresis, and base-call jointly by applying the sum-product algorithm on factor graphs. We first present a statistical model for DNA sequencing data and examine the model parameters. A practical heuristic is then proposed to estimate the peaks, which are then separated into two source sequences (Major/Minor) by passing messages on a factor graph. Simulation results show that joint base-calling can provide less accurate but valid results for the minor. The algorithm presented provides a basis for future investigation of joint sequencing techniques.

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.

To improve the utility of existing technologies based on Sanger sequencing, this paper examines the possibility of base-calling two superposed DNA sequences jointly. This approach allows a single electrophoresis experiment to process two sequences, using the same quantity of reagents and machine hours as for a single sequence. A practical heuristic is proposed to first estimate the peak parameters, then separate them into two sequences (major/minor) by passing messages on a factor graph. Base-calling on the major alone yields accuracy commensurate with single sequence approaches, and joint base-calling provides results for the minor which, while being of lesser quality, incurs no additional cost and can be ultimately used in the genome assembly process.

We consider the problem of applying network coding in wireless networks with random medium access. To optimize the network coding subgraph and the transmission attempt probabilities jointly is a tractable problem only for rather small networks. Therefore, we suggest a suboptimal, yet practical and decentralized algorithm to combine network coding with random access. We illustrate the performance gains of our approach with simulations.

In this paper we consider the application of network coding to a multiple unicast setup. We present two suboptimal, yet practical code construction techniques. One consists of a linear program and the other of an integer program with fewer variables and constraints. We discuss the performance of the proposed techniques as well as their complexity

We consider the problem of establishing minimum-cost multicast connections over coded packet networks, i.e., packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. We consider both wireline and wireless packet networks as well as both static multicast (where membership of the multicast group remains constant for the duration of the connection) and dynamic multicast (where membership of the multicast group changes in time, with nodes joining and leaving the group). For static multicast, we reduce the problem to a polynomial-time solvable optimization problem, and we present decentralized algorithms for solving it. These algorithms, when coupled with existing decentralized schemes for constructing network codes, yield a fully decentralized approach for achieving minimum-cost multicast. By contrast, establishing minimum-cost static multicast connections over routed packet networks is a very difficult problem even using centralized computation, except in the special cases of unicast and broadcast connections. For dynamic multicast, we reduce the problem to a dynamic programming problem and apply the theory of dynamic programming to suggest how it may be solved.

We present decentralized algorithms that compute minimum-cost subgraphs for establishing multicast connections in networks that use coding. These algorithms, coupled with existing decentralized schemes for constructing network codes, constitute a fully decentralized approach for achieving minimum-cost multicast. Our approach is in sharp contrast to the prevailing approach based on approximation algorithms for the directed Steiner tree problem, which is suboptimal and generally assumes centralized computation with full network knowledge. We also give extensions beyond the basic problem of fixed-rate multicast in networks with directed point-to-point links, and consider the case of elastic rate demand as well as the problem of minimum-energy multicast in wireless networks.

In "On Coding for Reliable Communication over Packet Networks" (Lun, Medard, and Effros, Proc. 42nd Annu. Allerton Conf. Communication, Control, and Computing, 2004), a capacity-achieving coding scheme for unicast or multicast over lossy wireline or wireless packet networks is presented. We extend that paper's results in two ways: First, we extend the network model to allow packets received on a link to arrive according to any process with an average rate, as opposed to the assumption of Poisson traffic with i.i.d. losses that was previously made. Second, in the case of Poisson traffic with i.i.d. losses, we derive error exponents that quantify the rate at which the probability of error decays with coding delay

It is known that, when using a multi-tone FSK scheme in a wideband fading channel to achieve the wideband capacity limit, the codeword probability of error decays very slowly with bandwidth. In this paper, we consider a modified multi-tone FSK scheme which employs a feedback link. We show that, a small amount of feedback improves the error performance significantly

This paper shows that, for a particular class of channels that we believe applies to many physical problems of interest, the utility of feedback, insofar as channel capacity is concerned, is simply for allowing the transmitter to perform power control. This class of channels, which assumes noiseless feedback but allows for the feedback to be of arbitrary rate, includes channels that model slow, flat fading channels with variable input power. Thus our result gives some guidance on the design of effective transmission schemes for slow, flat fading channels with feedback.

We analyze the error probability of peaky signaling on bandlimited multipath fading channels, the signaling strategy that achieves the capacity of such channels in the limit of infinite bandwidth under an average power constraint. We first derive an upper bound for general fading, then specialize to the case of Rayleigh fading, where we obtain upper and lower bounds that are exponentially tight and, therefore, yield the reliability function. These bounds constitute a strong coding theorem for the channel, as they not only delimit the range of achievable rates, but also give us a relationship among the error probability, data rate, bandwidth, peakiness, and fading parameters, such as the coherence time. They can be used to compare peaky signaling systems to other large bandwidth systems over fading channels, such as ultra-wideband radio and wideband code-division multiple access. We find that the error probability decreases slowly with the bandwidth W; under Rayleigh fading, the error probability varies roughly as W/sup -/spl alpha//, where /spl alpha/>0. With parameters typical of indoor wireless situations, we study the behavior of the upper and lower bounds on the error probability and the reliability function numerically.