Effect of cell cycle arrest on intermediate metabolism in the marine diatom Phaeodactylum tricornutum.

The inhibitor NU 2058 [6-(cyclohexylmethoxy)-9H-purin-2-amine] leads to G1-phase cell cycle arrest in the marine diatom, Phaeodactylum tricornutum, by binding to two cyclin-dependent kinases, CDKA1 and CDKA2. NU 2058 has no effect on photosynthetic attributes, such as Fv/Fm, chlorophyll a/cell, levels of D2 PSII subunits, or RbcL; however, cell cycle arrest leads to unbalanced growth whereby photosynthetic products that can no longer be used for cell division are redirected toward carbohydrates and triacylglycerols (TAGs). Arrested cells up-regulate most genes involved in fatty acid synthesis, including acetyl-CoA carboxylase, and three out of five putative type II diglyceride acyltransferases (DGATs), the enzymes that catalyze TAG production. Correlation of transcriptomes in arrested cells with a flux balance model for P. tricornutum predicts that reactions in the mitochondrion that supply glycerate may support TAG synthesis. Our results reveal that sources of intermediate metabolites and macromolecular sinks are tightly coupled to the cell cycle in a marine diatom, and that arresting cells in the G1 phase leads to remodeling of intermediate metabolism and unbalanced growth.

Statistical modeling of STR capillary electrophoresis signal.

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.

MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

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: dslun@rutgers.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rewiring of Cyanobacterial Metabolism for Hydrogen Production: Synthetic Biology Approaches and Challenges.

With the demand for renewable energy growing, hydrogen (H2) is becoming an attractive energy carrier. Developing H2 production technologies with near-net zero carbon emissions is a major challenge for the "H2 economy." Certain cyanobacteria inherently possess enzymes, nitrogenases, and bidirectional hydrogenases that are capable of H2 evolution using sunlight, making them ideal cell factories for photocatalytic conversion of water to H2. With the advances in synthetic biology, cyanobacteria are currently being developed as a "plug and play" chassis to produce H2. This chapter describes the metabolic pathways involved and the theoretical limits to cyanobacterial H2 production and summarizes the metabolic engineering technologies pursued.

Remodeling of intermediate metabolism in the diatom Phaeodactylum tricornutum under nitrogen stress.

Diatoms are unicellular algae that accumulate significant amounts of triacylglycerols as storage lipids when their growth is limited by nutrients. Using biochemical, physiological, bioinformatics, and reverse genetic approaches, we analyzed how the flux of carbon into lipids is influenced by nitrogen stress in a model diatom, Phaeodactylum tricornutum. Our results reveal that the accumulation of lipids is a consequence of remodeling of intermediate metabolism, especially reactions in the tricarboxylic acid and the urea cycles. Specifically, approximately one-half of the cellular proteins are cannibalized; whereas the nitrogen is scavenged by the urea and glutamine synthetase/glutamine 2-oxoglutarate aminotransferase pathways and redirected to the de novo synthesis of nitrogen assimilation machinery, simultaneously, the photobiological flux of carbon and reductants is used to synthesize lipids. To further examine how nitrogen stress triggers the remodeling process, we knocked down the gene encoding for nitrate reductase, a key enzyme required for the assimilation of nitrate. The strain exhibits 40-50% of the mRNA copy numbers, protein content, and enzymatic activity of the wild type, concomitant with a 43% increase in cellular lipid content. We suggest a negative feedback sensor that couples photosynthetic carbon fixation to lipid biosynthesis and is regulated by the nitrogen assimilation pathway. This metabolic feedback enables diatoms to rapidly respond to fluctuations in environmental nitrogen availability.

Flux balance analysis of photoautotrophic metabolism: Uncovering new biological details of subsystems involved in cyanobacterial photosynthesis.

We have constructed and experimentally tested a comprehensive genome-scale model of photoautotrophic growth, denoted iSyp821, for the cyanobacterium Synechococcus sp. PCC 7002. iSyp821 incorporates a variable biomass objective function (vBOF), in which stoichiometries of the major biomass components vary according to light intensity. The vBOF was constrained to fit the measured cellular carbohydrate/protein content under different light intensities. iSyp821 provides rigorous agreement with experimentally measured cell growth rates and inorganic carbon uptake rates as a function of light intensity. iSyp821 predicts two observed metabolic transitions that occur as light intensity increases: 1) from PSI-cyclic to linear electron flow (greater redox energy), and 2) from carbon allocation as proteins (growth) to carbohydrates (energy storage) mode. iSyp821 predicts photoautotrophic carbon flux into 1) a hybrid gluconeogenesis-pentose phosphate (PP) pathway that produces glycogen by an alternative pathway than conventional gluconeogenesis, and 2) the photorespiration pathway to synthesize the essential amino acid, glycine. Quantitative fluxes through both pathways were verified experimentally by following the kinetics of formation of 13C metabolites from 13CO2 fixation. iSyp821 was modified to include changes in gene products (enzymes) from experimentally measured transcriptomic data and applied to estimate changes in concentrations of metabolites arising from nutrient stress. Using this strategy, we found that iSyp821 correctly predicts the observed redistribution pattern of carbon products under nitrogen depletion, including decreased rates of CO2 uptake, amino acid synthesis, and increased rates of glycogen and lipid synthesis.

Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum.

Diatoms (Bacillarophyceae) are photosynthetic unicellular microalgae that have risen to ecological prominence in oceans over the past 30 million years. They are of interest as potential feedstocks for sustainable biofuels. Maximizing production of these feedstocks will require genetic modifications and an understanding of algal metabolism. These processes may benefit from genome-scale models, which predict intracellular fluxes and theoretical yields, as well as the viability of knockout and knock-in transformants. Here we present a genome-scale metabolic model of a fully sequenced and transformable diatom: Phaeodactylum tricornutum. The metabolic network was constructed using the P. tricornutum genome, biochemical literature, and online bioinformatic databases. Intracellular fluxes in P. tricornutum were calculated for autotrophic, mixotrophic and heterotrophic growth conditions, as well as knockout conditions that explore the in silico role of glycolytic enzymes in the mitochondrion. The flux distribution for lower glycolysis in the mitochondrion depended on which transporters for TCA cycle metabolites were included in the model. The growth rate predictions were validated against experimental data obtained using chemostats. Two published studies on this organism were used to validate model predictions for cyclic electron flow under autotrophic conditions, and fluxes through the phosphoketolase, glycine and serine synthesis pathways under mixotrophic conditions. Several gaps in annotation were also identified. The model also explored unusual features of diatom metabolism, such as the presence of lower glycolysis pathways in the mitochondrion, as well as differences between P. tricornutum and other photosynthetic organisms.

Overexpression of a diacylglycerol acyltransferase gene in Phaeodactylum tricornutum directs carbon towards lipid biosynthesis.

Under nutrient deplete conditions, diatoms accumulate between 15% to 25% of their dry weight as lipids, primarily as triacylglycerols (TAGs). As in most eukaryotes, these organisms produce TAGs via the acyl-CoA dependent Kennedy pathway. The last step in this pathway is catalyzed by diacylglycerol acyltransferase (DGAT) that acylates diacylglycerol (DAG) to produce TAG. To test our hypothesis that DGAT plays a major role in controlling the flux of carbon towards lipids, we overexpressed a specific type II DGAT gene, DGAT2D, in the model diatom Phaeodactylum tricornutum. The transformants had 50- to 100-fold higher DGAT2D mRNA levels and the abundance of the enzyme increased 30- to 50-fold. More important, these cells had a 2-fold higher total lipid content and incorporated carbon into lipids more efficiently than the wild type (WT) while growing only 15% slower at light saturation. Based on a flux analysis using 13 C as a tracer, we found that the increase in lipids was achieved via increased fluxes through pyruvate and acetyl-CoA. Our results reveal overexpression of DAGT2D increases the flux of photosynthetically fixed carbon towards lipids, and leads to a higher lipid content than exponentially grown WT cells.

MOST: a software environment for constraint-based metabolic modeling and strain design.

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: dslun@rutgers.edu.

Constraint-based modeling of heterologous pathways: application and experimental demonstration for overproduction of fatty acids in Escherichia coli.

Constraint-based modeling has been shown, in many instances, to be useful for metabolic engineering by allowing the prediction of the metabolic phenotype resulting from genetic manipulations. But the basic premise of constraint-based modeling-that of applying constraints to preclude certain behaviors-only makes sense for certain genetic manipulations (such as knockouts and knockdowns). In particular, when genes (such as those associated with a heterologous pathway) are introduced under artificial control, it is unclear how to predict the correct behavior. In this paper, we introduce a modeling method that we call proportional flux forcing (PFF) to model artificially induced enzymatic genes. The model modifications introduced by PFF can be transformed into a set of simple mass balance constraints, which allows computational methods for strain optimization based on flux balance analysis (FBA) to be utilized. We applied PFF to the metabolic engineering of Escherichia coli (E. coli) for free fatty acid (FFA) production-a metabolic engineering problem that has attracted significant attention because FFAs are a precursor to liquid transportation fuels such as biodiesel and biogasoline. We show that PFF used in conjunction with FBA-based computational strain optimization methods can yield non-obvious genetic manipulation strategies that significantly increase FFA production in E. coli. The two mutant strains constructed and successfully tested in this work had peak fatty acid (FA) yields of 0.050 g FA/g carbon source (17.4% theoretical yield) and 0.035 g FA/g carbon source (12.3% theoretical yield) when they were grown using a mixed carbon source of glucose and casamino acids in a ratio of 2-to-1. These yields represent increases of 5.4- and 3.8-fold, respectively, over the baseline strain.

Calculation of the Weight of Evidence for Combined Single-Cell and Extracellular Forensic DNA.

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.

Single-cell investigative genetics: Single-cell data produces genotype distributions concentrated at the true genotype across all mixture complexities.

In the absence of a suspect the forensic aim is investigative, and the focus is one of discerning what genotypes best explain the evidence. In traditional systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor's contribution and, as a result, possibly overlooked or inefficient investigative leads. Recent developments in single-cell analysis offer a way forward, by producing data capable of discriminating genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster's content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes' rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least 1-α. This is the credible genotype set and is used to inform database search criteria. Within this work we demonstrate the salience of single-cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most probable genotype was the true genotype for 97% of the loci when the number of cells in a cluster was at least two. Since efficient investigative leads will be borne by posterior mass distributions that are narrow and concentrated at the true genotype, we report that, for this test set, 47,900 (86%) loci returned only one credible genotype and of these 47,551 (99%) were the true genotype. When determining the LR for true contributors, 91% of the clusters rendered LR>1018, showing the potential of single-cell data to positively affect investigative reporting.

Quorum-sensing agr system of Staphylococcus aureus primes gene expression for protection from lethal oxidative stress.

The agr quorum-sensing system links Staphylococcus aureus metabolism to virulence, in part by increasing bacterial survival during exposure to lethal concentrations of H2O2, a crucial host defense against S. aureus. We now report that protection by agr surprisingly extends beyond post-exponential growth to the exit from stationary phase when the agr system is no longer turned on. Thus, agr can be considered a constitutive protective factor. Deletion of agr increased both respiration and fermentation but decreased ATP levels and growth, suggesting that Δagr cells assume a hyperactive metabolic state in response to reduced metabolic efficiency. As expected from increased respiratory gene expression, reactive oxygen species (ROS) accumulated more in the agr mutant than in wild-type cells, thereby explaining elevated susceptibility of Δagr strains to lethal H2O2 doses. Increased survival of wild-type agr cells during H2O2 exposure required sodA, which detoxifies superoxide. Additionally, pretreatment of S. aureus with respiration-reducing menadione protected Δagr cells from killing by H2O2. Thus, genetic deletion and pharmacologic experiments indicate that agr helps control endogenous ROS, thereby providing resilience against exogenous ROS. The long-lived "memory" of agr-mediated protection, which is uncoupled from agr activation kinetics, increased hematogenous dissemination to certain tissues during sepsis in ROS-producing, wild-type mice but not ROS-deficient (Nox2-/-) mice. These results demonstrate the importance of protection that anticipates impending ROS-mediated immune attack. The ubiquity of quorum sensing suggests that it protects many bacterial species from oxidative damage.

Quorum-sensing agr system of Staphylococcus aureus primes gene expression for protection from lethal oxidative stress.

The agr quorum-sensing system links Staphylococcus aureus metabolism to virulence, in part by increasing bacterial survival during exposure to lethal concentrations of H2O2, a crucial host defense against S. aureus. We now report that protection by agr surprisingly extends beyond post-exponential growth to the exit from stationary phase when the agr system is no longer turned on. Thus, agr can be considered a constitutive protective factor. Deletion of agr resulted in decreased ATP levels and growth, despite increased rates of respiration or fermentation at appropriate oxygen tensions, suggesting that Δagr cells undergo a shift towards a hyperactive metabolic state in response to diminished metabolic efficiency. As expected from increased respiratory gene expression, reactive oxygen species (ROS) accumulated more in the agr mutant than in wild-type cells, thereby explaining elevated susceptibility of Δagr strains to lethal H2O2 doses. Increased survival of wild-type agr cells during H2O2 exposure required sodA, which detoxifies superoxide. Additionally, pretreatment of S. aureus with respiration-reducing menadione protected Δagr cells from killing by H2O2. Thus, genetic deletion and pharmacologic experiments indicate that agr helps control endogenous ROS, thereby providing resilience against exogenous ROS. The long-lived 'memory' of agr-mediated protection, which is uncoupled from agr activation kinetics, increased hematogenous dissemination to certain tissues during sepsis in ROS-producing, wild-type mice but not ROS-deficient (Cybb-/-) mice. These results demonstrate the importance of protection that anticipates impending ROS-mediated immune attack. The ubiquity of quorum sensing suggests that it protects many bacterial species from oxidative damage.

Truncated branch and bound achieves efficient constraint-based genetic design.

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.

Evidentiary evaluation of single cells renders highly informative forensic comparisons across multifarious admixtures.

The consistency between DNA evidence and person(s) of interest (PoI) is summarized by a likelihood ratio (LR): the probability of the data given the PoI contributed divided by the probability given they did not. It is often the case that there are several PoI who may have individually or jointly contributed to the stain. If there is more than one PoI, or the number of contributors (NoC) cannot easily be determined, then several sets of hypotheses are needed, requiring significant resources to complete the interpretation. Recent technological developments in laboratory systems offer a way forward, by enabling production of single cell data. Though single-cell data may be procured by next generation sequencing or capillary electrophoresis workflows, in this work we focus our attention on assessing the consistency between PoIs and a collection of single cell electropherograms (scEPGs) from diploid cells - i.e., leukocytes and epithelial cells. Specifically, we introduce a framework that: I) clusters scEPGs into collections, each originating from one genetic source; II) for each PoI, determines a LR for each cluster of scEPGs; and III) by averaging the likelihood ratios for each PoI across all clusters provides a whole-sample weight of evidence summary. By using Model Based Clustering (MBC) in step I) and an algorithm, named EESCIt for Evidentiary Evaluation of Single Cells, that computes single-cell LRs in step II), we show that 99% of the comparisons rendered log LR values > 0 for true contributors, and of these all but one gave log LR > 5, regardless of the number of donors or whether the smallest contributor donated less than 20% of the cells, greatly expanding the collection of cases for which DNA forensics provides informative results.

Evolution of BMP signaling in Drosophila oogenesis: a receptor-based mechanism.

The bone morphogenetic protein (BMP) signaling pathway is a conserved regulator of cellular and developmental processes in animals. The mechanisms underlying BMP signaling activation differ among tissues and mostly reflect changes in the expression of pathway components. BMP signaling is one of the major pathways responsible for the patterning of the Drosophila eggshell, a complex structure derived from a layer of follicle cells (FCs) surrounding the developing oocyte. Activation of BMP signaling in the FCs is dynamic. Initially, signaling is along the anterior-posterior (A/P) axis; later, signaling acquires dorsal-ventral (D/V) polarity. These dynamics are regulated by changes in the expression pattern of the type I BMP receptor thickveins (tkv). We recently found that signaling dynamics and TKV patterning are highly correlated in the FCs of multiple Drosophila species. In addition, we showed that signaling patterns are spatially different among species. Here, we use a mathematical model to simulate the dynamics and differences of BMP signaling in numerous species. This model predicts that qualitative and quantitative changes in receptor expression can lead to differences in the spatial pattern of BMP signaling. We tested these predications experimentally in three different Drosophila species and through genetic perturbations of BMP signaling in D. melanogaster. On the basis of our results, we concluded that changes in tkv patterning can account for the experimentally observed differences in the patterns of BMP signaling in multiple Drosophila species.

Upregulation of the phthiocerol dimycocerosate biosynthetic pathway by rifampin-resistant, rpoB mutant Mycobacterium tuberculosis.

Multidrug-resistant tuberculosis has emerged as a major threat to tuberculosis control. Phylogenetically related rifampin-resistant actinomycetes with mutations mapping to clinically dominant Mycobacterium tuberculosis mutations in the rpoB gene show upregulation of gene networks encoding secondary metabolites. We compared the expressed proteomes and metabolomes of two fully drug-susceptible clinical strains of M. tuberculosis (wild type) to those of their respective rifampin-resistant, rpoB mutant progeny strains with confirmed rifampin monoresistance following antitubercular therapy. Each of these strains was also used to infect gamma interferon- and lipopolysaccharide-activated murine J774A.1 macrophages to analyze transcriptional responses in a physiologically relevant model. Both rpoB mutants showed significant upregulation of the polyketide synthase genes ppsA-ppsE and drrA, which constitute an operon encoding multifunctional enzymes involved in the biosynthesis of phthiocerol dimycocerosate and other lipids in M. tuberculosis, but also of various secondary metabolites in related organisms, including antibiotics, such as erythromycin and rifamycins. ppsA (Rv2931), ppsB (Rv2932), and ppsC (Rv2933) were also found to be upregulated more than 10-fold in the Beijing rpoB mutant strain relative to its wild-type parent strain during infection of activated murine macrophages. In addition, metabolomics identified precursors of phthiocerol dimycocerosate, but not the intact molecule itself, in greater abundance in both rpoB mutant isolates. These data suggest that rpoB mutation in M. tuberculosis may trigger compensatory transcriptional changes in secondary metabolism genes analogous to those observed in related actinobacteria. These findings may assist in developing novel methods to diagnose and treat drug-resistant M. tuberculosis infections.

The a posteriori probability of the number of contributors when conditioned on an assumed contributor.

Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpretation. Over-expressions of stutter, allele drop-out, allele drop-in, degradation, differential degradation, and the like, make forensic DNA profiles too complicated to evaluate by manual methods. In response, computational tools that make point estimates on the Number of Contributors (NOC) to a sample have been developed, as have Bayesian methods that evaluate an A Posteriori Probability (APP) distribution on the NOC. In cases where an overly narrow NOC range is assumed, the downstream strength of evidence may be incomplete insofar as the evidence is evaluated with an inadequate set of propositions. In the current paper, we extend previous work on NOCIt, a Bayesian method that determines an APP on the NOC given an electropherogram, by reporting on an implementation where the user can add assumed contributors. NOCIt is a continuous system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise, and allelic drop-out, while being cognizant of allele frequencies in a reference population. When conditioned on a known contributor, we found that the mode of the APP distribution can shift to one greater when compared with the circumstance where no known contributor is assumed, and that occurred most often when the assumed contributor was the minor constituent to the mixture. In a development of a result of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establishes the NOC can be treated as a nuisance variable in the computation of a likelihood ratio between the prosecution and defense hypotheses, we show that this computation must not only use coincident models, but also coincident contextual information. The results reported here, therefore, illustrate the power of modern probabilistic systems to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple contexts.

A series of developmental validation tests for Number of Contributors platforms: Exemplars using NOCIt and a neural network.

Complex DNA mixtures are challenging to interpret and require computational tools that aid in that interpretation. Recently, several computational methods that estimate the number of contributors (NOC) to a sample have been developed. Unlike analogous tools that interpret profiles and report LRs, NOC tools vary widely in their operational principle where some are Bayesian and others are machine learning tools. Conjunctionally, NOC tools may return a single n estimate, or a distribution on n. This vast array of constructs, coupled with a gap in standardized methods by which to validate NOC systems, warrants an exploration into the measures by which differing NOC systems might be tested for operations. In the current paper, we use two exemplar NOC systems: a probabilistic system named NOCIt, which renders an a posteriori probability (APP) distribution on the number of contributors given an electropherogram and an artificial neural network (ANN). NOCIt is a continuous Bayesian inference system incorporating models of peak height, degradation, differential degradation, forward and reverse stutter, noise and allelic drop-out while considering allele frequencies in a reference population. The ANN is also a continuous method, taking all the same features (barring degradation) into account. Unlike its Bayesian counterpart, it demands substantively more data to parameterize, requiring synthetic data. We explore each system's performance by conducting tests on 214 PROVEDIt mixtures where the limit of detection was 1-copy of DNA. We found that after a lengthy training period of approximately 24 h, the ANN's evaluation process was very fast and perfectly repeatable. In contrast, NOCIt only took a few minutes to train but took tens of minutes to complete each sample and was less repeatable. In addition, it rendered a probability distribution that was more sensitive and specific, affording a reasonable method by which to report all reasonable n that explain the evidence for a given sample. Whatever the method, by acknowledging the inherent differences between NOC systems, we demonstrate that validation constructs will necessarily be guided by the needs of the forensic domain and be dependent upon whether the laboratory seeks to assign a single n or range of n.