Perioperative Antibiotics and Other Factors Associated with Postcesarean Infections: A Case-Control Study.

OBJECTIVE: Our objective was to determine modifiable risk factors associated with surgical site infection (SSI) and postpartum endometritis. We hypothesized that inappropriate surgical antibiotic prophylaxis would be a risk factor for both types of infections. STUDY DESIGN: This was a single-center case-control study of SSI and endometritis after cesarean delivery over a 2-year period from 2016 to 2017. Cases were identified by International Classification of Diseases, 10th Revision diagnosis codes, infection control surveillance, and electronic medical records search and were subsequently confirmed by chart review. Three controls were randomly selected for each case from all cesareans ± 48 hours from case delivery. Demographic, pregnancy, and delivery characteristics were abstracted. Separate multivariable logistic regression models were used to assess factors associated with SSI and endometritis. Postpartum outcomes, including length of stay and readmission, were also compared. RESULTS: We identified 141 cases of SSI and endometritis with an overall postpartum infection rate of 4.0% among all cesarean deliveries. In adjusted analysis, factors associated with both SSI and endometritis were intrapartum delivery, classical or other (non-low-transverse) uterine incision, and blood transfusion. Factors associated with SSI only included inadequate antibiotic prophylaxis, public insurance, hypertensive disorder of pregnancy, and nonchlorhexidine abdominal preparation; factors only associated with endometritis included ß-lactam allergy, anticoagulation therapy, and chorioamnionitis. Among cases, 34% of those with SSI and 25% of those with endometritis did not receive adequate antibiotic prophylaxis, compared with 12.9 and 13.5% in control groups, respectively. Failure to receive appropriate antibiotic prophylaxis was associated with an increased risk of SSI (adjusted odds ratio [aOR]: 4.4, 95% confidence interval [CI]: 1.3-15.6) but not endometritis (aOR 0.9, 95% CI 0.4-2.0). CONCLUSION: Inadequate surgical antibiotic prophylaxis was associated with an increased risk of SSI but not postpartum endometritis, highlighting the different mechanisms of these infections and the importance of prioritizing adequate surgical prophylaxis. Additional potentially modifiable factors which emerged included blood transfusion and chlorhexidine skin preparation. KEY POINTS: · Inadequate antibiotic prophylaxis is associated with a four-fold risk in surgical site infections.. · The most common cause for failure to achieve adequate surgical prophylaxis was inappropriate timing of antibiotics at or after skin incision.. · Blood transfusions are strongly associated (>10-fold risk) with both SSI and endometritis..

Measurement of hemorrhage-related severe maternal morbidity with billing versus electronic medical record data.

OBJECTIVE: Measurement of obstetric hemorrhage-related morbidity is important for quality assurance purposes but presents logistical challenges in large populations. Billing codes are typically used to track severe maternal morbidity but may be of suboptimal validity. The objective of this study was to evaluate the validity of billing code diagnoses for hemorrhage-related morbidity compared to data obtained from the electronic medical record. STUDY DESIGN: Deliveries occurring between July 2014 and July 2017 from three hospitals within a single system were analyzed. Three outcomes related to obstetric hemorrhage that are part of the Centers for Disease Control and Prevention definition of severe maternal morbidity (SMM) were evaluated: (i) transfusion, (ii) disseminated intravascular coagulation (DIC), and (iii) acute renal failure (ARF). ICD-9-CM and ICD-10-CM for these conditions were ascertained and compared to blood bank records and laboratory values. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) with 95% confidence intervals (CI) were calculated. Ancillary analyses were performed comparing codes and outcomes between hospitals and comparing ICD-9-CM to ICD-10-CM codes. Comparisons of categorical variables were performed with the chi-squared test. T-tests were used to compare continuous outcomes. RESULTS: 35,518 deliveries were analyzed. 786 women underwent transfusion, 168 had serum creatinine ≥1.2 mg/dL, and 99, 40, and 16 had fibrinogen ≤200, ≤150, and ≤100 mg/dL, respectively. Transfusion codes were 65% sensitive (95% CI 62-69%) with a 91% PPV (89-94%) for blood bank records of transfusion. DIC codes were 22% sensitive (95% CI 15-32%) for a fibrinogen cutoff of ≤200 mg/dL with 15% PPV (95% CI 10-22%). Sensitivity for ARF was 33% (95% CI 26-41%) for a creatinine of 1.2 mg/dL with a PPV of 63% (95% CI 52-73%). Sensitivity of ICD-9-CM for transfusion was significantly higher than ICD-10-CM (81%, 95% CI 76-86% versus 56%, 95% CI 51-60%, p < .01). Evaluating sensitivity of codes by individual hospitals, sensitivity of diagnosis codes for transfusion varied significantly (Hospital A 47%, 95% CI 36-58% versus Hospital B 63%, 95% CI 58-67% versus Hospital C 80%, 95% CI 74-86%, p < .01). CONCLUSION: Use of administrative billing codes for postpartum hemorrhage complications may be appropriate for measuring trends related to disease burden and resource utilization, particularly in the case of transfusion, but may be suboptimal for measuring clinical outcomes within and between hospitals.

Medication Use and Physical Assaults in the Psychiatric Emergency Department.

Objective: To evaluate the relationship between medications used to treat acute agitation (antipsychotics, mood stabilizers, and benzodiazepines) and subsequent assault incidence in the psychiatric emergency department.Methods: Medication orders and assault incident reports were obtained from electronic health records for 17,056 visits to an urban psychiatric emergency department from 2014 to 2019. Assault risk was modeled longitudinally using Poisson mixed-effects regression.Results: Assaults were reported during 0.5% of visits. Intramuscular (IM) medications were ordered in 23.3% of visits overall and predominantly were ordered within the first 4 hours of a visit. IM medication orders were correlated with assault (incident rate ratio [IRR] = 24.2; 95% CI, 5.33-110.0), often because IM medications were ordered immediately subsequent to reported assaults. Interacted with time, IM medications were not significantly associated with reduction in subsequent assaults (IRR = 0.700; 95% CI, 0.467-1.04). Neither benzodiazepines nor mood stabilizers were associated with subsequent changes to the risk of reported assault. By contrast, antipsychotic medications were associated with decreased assault risk across time (IRR = 0.583; 95% CI, 0.360-0.942).Conclusions: Although assault prevention is not the sole reason for ordering IM medications, IM medication order rates are high relative to overall assault incident risk. Of the 3 major categories of medications ordered commonly in the psychiatric emergency setting, only antipsychotic medications were associated with measurable decreases in subsequent assault risk. As antipsychotic medication can have a significant side effect burden, careful weighing of the risks and benefits of medications is encouraged.

Comparative incidence and burden of respiratory viruses associated with hospitalization in adults in New York City.

BACKGROUND: Although the burden of influenza is well characterized, the burden of community-onset non-influenza respiratory viruses has not been systematically assessed. Understanding the severity and seasonality of non-influenza viruses, including human coronaviruses, will provide a better understanding of the overall disease burden from respiratory viruses that could better inform resource utilization for hospitals and highlight the value of preventative strategies, including vaccines. METHODS: From October 2017 to September 2019, a retrospective study was performed in a pre-defined catchment area to estimate the population-based incidence of community-onset respiratory viruses associated with hospitalization. Included patients were ≥18 years old, resided in New York City, were hospitalized for ≥24 hours, and had a respiratory virus detected within 3 calendar-days of admission. Disease burden was measured by hospital length of stay (LOS), intensive care unit (ICU) admissions, and in-hospital mortality and compared among those with laboratory-confirmed influenza versus those with laboratory-confirmed non-influenza viruses (human coronaviruses, parainfluenza viruses, respiratory syncytial virus, human metapneumovirus, and adenovirus). RESULTS: During the study period, 4232 eligible patients were identified of whom 50.9% were ≥65 years of age. For each virus, the population-based incidence was highest for those ≥80 years of age. When compared to those with influenza viruses detected, those with non-influenza respiratory viruses detected (combined) had higher population-based incidence, significantly more ICU admissions, and higher in-house mortality. CONCLUSIONS: The burden of non-influenza respiratory viruses for hospitalized adults is substantial. Prevention and treatment strategies are needed for non-influenza respiratory viruses, particularly for older adults.

Epidemiology, clinical features, and resource utilization associated with respiratory syncytial virus in the community and hospital.

BACKGROUND: The epidemiology, clinical features, and resource utilization of respiratory syncytial virus (RSV) cases in the community and the hospital are not fully characterized. METHODS: We identified individuals of all ages with laboratory-confirmed RSV from two sources, a community cohort undergoing surveillance for acute respiratory infections (ARIs) and hospitalized patients from the same geographic area of New York City between 2013 and 15. The epidemiology, clinical features, and resource utilization (antibiotic/steroid/ribavirin usage, chest X-rays, respiratory-support (continuous positive airway pressure [CPAP], mechanical ventilation or extracorporeal membrane oxygenation [ECMO]), and indicators of disease severity (respiratory-support, and/or ICU admission or death)) were compared among age groups using univariate and bivariate analyses. RESULTS: In the community cohort (1777 people with 1805 ARIs), 66(3.7%) tested RSV-positive (3.8% of <1-year-olds; 3.8% of adults ≥65); 40.9% were medically attended, and 23.1% reported antibiotic usage. Among 40,461 tests performed on hospital patients, 2.7% were RSV-positive within ± 2 days of admission (37.3% <1 year old; 17.4% ≥65 years old). Among RSV-positive hospitalized adults ≥65%, 92.7%, 89.6% and 78.1% received a chest X-ray, antibiotics and/or steroids respectively, compared with 48.9%, 45.7%, and 48.7% of children <1. Severe illness occurred in 27.0% RSV-positive hospitalized <1-year-olds and 19.8% ≥65-year-olds. CONCLUSIONS: Respiratory syncytial virus had a demonstrated impact in the community and hospital. Only 40% of RSV community cases were medically attended. In the hospitalized-cohort, <1- and ≥ 65-year-olds accounted for the majority of patients and had similar rates of severe illness. In addition, resource utilization was high in older adults, making both young children and older adults important potential RSV vaccine targets.

A Framework for Improving Characterization of Obstetric Hemorrhage Using Informatics Data.

OBJECTIVE: To characterize postpartum hemorrhage trends and outcomes using bioinformatics and electronic health record data. METHODS: This retrospective analysis included all women who delivered in a four-hospital system from July 2014 to July 2017 during implementation of a postpartum hemorrhage bundle. Data on billing codes, uterotonics, transfusion, intrauterine tamponade device placement, and hysterectomy were analyzed. A framework of four postpartum hemorrhage levels based on hemorrhage interventions was created using this informatics data. Levels were analyzed in relation to hematocrit drop from the highest predelivery to the lowest postpartum level. Changes in treatment patterns were assessed with risk-adjusted regression models with adjusted odds ratios (aOR) and 95% CI as the measures of effect. Postpartum hemorrhage-associated severe maternal morbidities were analyzed with adjusted models. RESULTS: The cohort included 43,657 deliveries. Four mutually exclusive postpartum hemorrhage levels were created based on informatics and billing criteria. Level 1: receipt of uterotonic other than oxytocin (3.7% of patients); level 2: billing diagnosis code for postpartum hemorrhage (3.0% of patients); level 3: placement of the intrauterine tamponade device, transfusion of 1-3 units red blood cells (RBCs), or both (1.8% of patients); and Level 4: hysterectomy, 4 or more units RBCs, or both (0.6% of patients). Higher postpartum hemorrhage levels were associated with higher hematocrit drops. In postpartum hemorrhage levels 1 through 4, 1.6%, 5.6%, 30.2%, and 30.7% of women had hematocrit drops greater than 40%, compared with 0.4% of women without postpartum hemorrhage. Over the course of the study, hematocrit drops within a given level did not change. Postpartum hemorrhage interventions such as uterotonics increased significantly (aOR 1.16, 95% CI 1.11-1.21, with aOR denoting change in outcome across 1 year). Although severe maternal morbidity did not change significantly, risk of hysterectomy decreased significantly (aOR 0.52, 95% CI 0.40-0.68). CONCLUSION: Postpartum hemorrhage can be characterized in a granular fashion with informatics data. Informatics data are becoming increasingly available and can provide detailed assessment of postpartum hemorrhage incidence, management, and outcomes to facilitate surveillance and quality improvement.

A principled framework for phenotyping postpartum hemorrhage across multiple levels of severity.

Maternal morbidity and mortality have gained major attention recently, spurred on by rising domestic rates even as maternal mortality decreases in Europe. A major driver of morbidity and mortality among delivering women is postpartum hemorrhage (PPH). PPH is currently phenotyped using the subjective measure of 'Estimated blood loss' (EBL), which has been shown to be unreliable for tracking quality. Here we present a framework for phenotyping PPH into multiple severity levels, using a combination of data-driven techniques and expert-derived clinical indicators. We validate the framework by predicting large drops in hematocrit and quantitative blood loss, finding that the framework performs better in predicting coded PPH than a hematocrit-based predictor or predictors based on other metrics such as blood transfusions, and does better in predicting quantitative blood loss, a gold standard metric for blood loss that we have for a subset of patients, than any predictor we could build using hematocrit drops alone. In all, we present a principled framework that can be used to phenotype PPH in hospitals using readily available EHR data, and that will perform with more granularity and accuracy than existing methods.

Seasonality and clinical impact of human parainfluenza viruses.

BACKGROUND: Widespread availability of rapid diagnostic testing for respiratory viruses allows more in-depth studies of human parainfluenza viruses (HPIV). OBJECTIVES: This study aimed to assess seasonality of HPIV types 1-4, clinical outcomes by HPIV type, and risk factors for illness severity. PATIENTS/METHODS: This retrospective study was performed from January 2013 to December 2015 in children and adults with HPIV, detected by multiplex reverse transcription polymerase chain reaction, participating in a community surveillance study of acute respiratory infections (ARIs) in New York City and patients admitted to a tertiary care center in the same neighborhood. Seasonality trends by HPIV type were compared between the community and hospital groups. The associations between HPIV type, demographics, clinical characteristics, and illness severity were assessed. RESULTS: HPIV was detected in 69 (4%) of 1753 community surveillance participants (median age 9.2 years) and 680 hospitalized patients (median age 6.8 years). Seasonality for HPIV types 1-3 agreed with previously described patterns; HPIV-4 occurred annually in late summer and fall. In the community cohort, 22 (32%) participants sought medical care, 9 (13%) reported antibiotic use, and 20 (29%) reported ≥1 day of missed work or school. Among hospitalized patients, 24% had ≥4 chronic conditions. Multivariable ordinal logistic regression demonstrated that increased severity of illness was significantly associated with HPIV-4 and chronic cardiovascular and respiratory conditions in children and with age ≥65 years and chronic respiratory conditions in adults. CONCLUSIONS: HPIV-4 presented late summer and early fall annually and was associated with increased severity of illness in hospitalized children.

Essential Genes Embody Increased Mutational Robustness to Compensate for the Lack of Backup Genetic Redundancy.

Genetic robustness is a hallmark of cells, occurring through many mechanisms and at many levels. Essential genes lack the common robustness mechanism of genetic redundancy (i.e., existing alongside other genes with the same function), and thus appear at first glance to leave cells highly vulnerable to genetic or environmental perturbations. Here we explore a hypothesis that cells might protect against essential gene loss through mechanisms that occur at various cellular levels aside from the level of the gene. Using Escherichia coli and Saccharomyces cerevisiae as models, we find that essential genes are enriched over non-essential genes for properties we call "coding efficiency" and "coding robustness", denoting respectively a gene's efficiency of translation and robustness to non-synonymous mutations. The coding efficiency levels of essential genes are highly positively correlated with their evolutionary conservation levels, suggesting that this feature plays a key role in protecting conserved, evolutionarily important genes. We then extend our hypothesis into the realm of metabolic networks, showing that essential metabolic reactions are encoded by more "robust" genes than non-essential reactions, and that essential metabolites are produced by more reactions than non-essential metabolites. Taken together, these results testify that robustness at the gene-loss level and at the mutation level (and more generally, at two cellular levels that are usually treated separately) are not decoupled, but rather, that cellular vulnerability exposed due to complete gene loss is compensated by increased mutational robustness. Why some genes are backed up primarily against loss and others against mutations still remains an open question.

Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.

Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients' survival in an independent cohort than using the canonical human metabolic pathways (C-index = 0.69 vs. 0.52, respectively). Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism.

Metabolic Network Prediction of Drug Side Effects.

Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.7) for >70 drug side effects, including very serious ones such as interstitial nephritis and extrapyramidal disorders. We evaluate AMPP's predictive signal through cross-validation, comparison across multiple versions of a side effects database, and co-occurrence analysis of drug side effect associations in scientific abstracts (hypergeometric p value = 2.2e-40). AMPP outperforms a previous biochemical structure-based method in predicting metabolically based side effects (aggregate AUC = 0.65 versus 0.59). Importantly, AMPP enables the identification of key metabolic reactions and biomarkers that are predictive of specific side effects. Taken together, this work lays a foundation for future detection of metabolically grounded side effects during early stages of drug development.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.

Harnessing the landscape of microbial culture media to predict new organism-media pairings.

Culturing microorganisms is a critical step in understanding and utilizing microbial life. Here we map the landscape of existing culture media by extracting natural-language media recipes into a Known Media Database (KOMODO), which includes >18,000 strain-media combinations, >3300 media variants and compound concentrations (the entire collection of the Leibniz Institute DSMZ repository). Using KOMODO, we show that although media are usually tuned for individual strains using biologically common salts, trace metals and vitamins/cofactors are the most differentiating components between defined media of strains within a genus. We leverage KOMODO to predict new organism-media pairings using a transitivity property (74% growth in new in vitro experiments) and a phylogeny-based collaborative filtering tool (83% growth in new in vitro experiments and stronger growth on predicted well-scored versus poorly scored media). These resources are integrated into a web-based platform that predicts media given an organism's 16S rDNA sequence, facilitating future cultivation efforts.

Glycan degradation (GlyDeR) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations.

UNLABELLED: Glycans form the primary nutritional source for microbes in the human gut, and understanding their metabolism is a critical yet understudied aspect of microbiome research. Here, we present a novel computational pipeline for modeling glycan degradation (GlyDeR) which predicts the glycan degradation potency of 10,000 reference glycans based on either genomic or metagenomic data. We first validated GlyDeR by comparing degradation profiles for genomes in the Human Microbiome Project against KEGG reaction annotations. Next, we applied GlyDeR to the analysis of human and mammalian gut microbial communities, which revealed that the glycan degradation potential of a community is strongly linked to host diet and can be used to predict diet with higher accuracy than sequence data alone. Finally, we show that a microbe's glycan degradation potential is significantly correlated (R = 0.46) with its abundance, with even higher correlations for potential pathogens such as the class Clostridia (R = 0.76). GlyDeR therefore represents an important tool for advancing our understanding of bacterial metabolism in the gut and for the future development of more effective prebiotics for microbial community manipulation. IMPORTANCE: The increased availability of high-throughput sequencing data has positioned the gut microbiota as a major new focal point for biomedical research. However, despite the expenditure of huge efforts and resources, sequencing-based analysis of the microbiome has uncovered mostly associative relationships between human health and diet, rather than a causal, mechanistic one. In order to utilize the full potential of systems biology approaches, one must first characterize the metabolic requirements of gut bacteria, specifically, the degradation of glycans, which are their primary nutritional source. We developed a computational framework called GlyDeR for integrating expert knowledge along with high-throughput data to uncover important new relationships within glycan metabolism. GlyDeR analyzes particular bacterial (meta)genomes and predicts the potency by which they degrade a variety of different glycans. Based on GlyDeR, we found a clear connection between microbial glycan degradation and human diet, and we suggest a method for the rational design of novel prebiotics.

A novel nutritional predictor links microbial fastidiousness with lowered ubiquity, growth rate, and cooperativeness.

Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited; (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks; and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect the adaptation of fastidious microorganisms to unique niches with few cohabitating species. They also explain how non-fastidious species inhabit many ecological niches with high abundance rates. Taken together, these results advance our understanding microbial nutrition on a large scale, by presenting new nutrition-related associations that govern the distribution of microorganisms in nature.

Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms.

Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization). The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth.

Metabolically re-modeling the drug pipeline.

Costs for drug development have soared, exposing a clear need for new R&D strategies. Systems biology has meanwhile emerged as an attractive vehicle for integrating omics data and other post-genomic technologies into the drug pipeline. One of the emerging areas of computational systems biology is constraint-based modeling (CBM), which uses genome-scale metabolic models (GSMMs) as platforms for integrating and interpreting diverse omics datasets. Here we review current uses of GSMMs in drug discovery, focusing on prediction of novel drug targets and promising lead compounds. We then expand our discussion to prediction of toxicity and selectivity of potential drug targets. We discuss successes as well as limitations of GSMMs in these areas. Finally, we suggest new ways in which GSMMs may contribute to drug discovery, offering our vision of how GSMMs may re-model the drug pipeline in years to come.