Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations.

J Ethnopharmacol. 2016 Jul 22;

Authors: Prasher B, Varma B, Kumar A, Khuntia BK, Pandey R, Narang A, Tiwari P, Kutum R, Guin D, Kukreti R, Dash D, TRISUTRA Ayurgenomics Consortium, Mukerji M

Abstract
BACKGROUND: Genetic differences in the target proteins, metabolizing enzymes and transporters that contribute to inter-individual differences in drug response are not integrated in contemporary drug development programs. Ayurveda, that has propelled many drug discovery programs albeit for the search of new chemical entities incorporates inter-individual variability "Prakriti" in development and administration of drug in an individualized manner. Prakriti of an individual largely determines responsiveness to external environment including drugs as well as susceptibility to diseases. Prakriti has also been shown to have molecular and genomic correlates. We highlight how integration of Prakriti concepts can augment the efficiency of drug discovery and development programs through a unique initiative of Ayurgenomics TRISUTRA consortium.
METHODS: Five aspects that have been carried out are (1) analysis of variability in FDA approved pharmacogenomics genes/SNPs in exomes of 72 healthy individuals including predominant Prakriti types and matched controls from a North Indian Indo-European cohort (2) establishment of a consortium network and development of five genetically homogeneous cohorts from diverse ethnic and geo-climatic background (3) identification of parameters and development of uniform standard protocols for objective assessment of Prakriti types (4) development of protocols for Prakriti evaluation and its application in more than 7500 individuals in the five cohorts (5) Development of data and sample repository and integrative omics pipelines for identification of genomic correlates.
RESULTS: Highlight of the study are (1) Exome sequencing revealed significant differences between Prakriti types in 28 SNPs of 11 FDA approved genes of pharmacogenomics relevance viz CYP2C19 CYP2B6, ESR1, F2, PGR, HLA-B, HLA-DQA1, HLA-DRB1, LDLR, CFTR, CPS1. These variations are polymorphic in diverse Indian and world populations included in 1000 genomes project. (2) Based on the phenotypic attributes of Prakriti we identified anthropometry for anatomical features, biophysical parameters for skin types, HRV for autonomic function tests, spirometry for vital capacity and gustometry for taste thresholds as objective parameters. (3) Comparison of Prakriti phenotypes across different ethnic, age and gender groups led to identification of invariant features as well as some that require weighted considerations across the cohorts.
CONCLUSION: Considering the molecular and genomics differences underlying Prakriti and relevance in disease pharmacogenomics studies, this novel integrative platform would help in identification of differently susceptible and drug responsive population. Additionally, integrated analysis of phenomic and genomic variations would not only allow identification of clinical and genomic markers of Prakriti for application in personalized medicine but also its integration in drug discovery and development programs.

PMID: 27457695 [PubMed - as supplied by publisher]

Personalized Therapy of Hypertension: the Past and the Future.

Curr Hypertens Rep. 2016 Mar;18(3):24

Authors: Manunta P, Ferrandi M, Cusi D, Ferrari P, Staessen J, Bianchi G

Abstract
During the past 20 years, the studies on genetics or pharmacogenomics of primary hypertension provided interesting results supporting the role of genetics, but no actionable finding ready to be translated into personalized medicine. Two types of approaches have been applied: a "hypothesis-driven" approach on the candidate genes, coding for proteins involved in the biochemical machinery underlying the regulation of BP, and an "unbiased hypothesis-free" approach with GWAS, based on the randomness principles of frequentist statistics. During the past 10-15 years, the application of the latter has overtaken the application of the former leading to an enlargement of the number of previously unknown candidate loci or genes but without any actionable result for the therapy of hypertension. In the present review, we summarize the results of our hypothesis-driven approach based on studies carried out in rats with genetic hypertension and in humans with essential hypertension at the pre-hypertensive and early hypertensive stages. These studies led to the identification of mutant adducin and endogenous ouabain as candidate genetic-molecular mechanisms in both species. Rostafuroxin has been developed for its ability to selectively correct Na(+) pump abnormalities sustained by the two abovementioned mechanisms and to selectively reduce BP in rats and in humans carrying the gene variants underlying the mutant adducin and endogenous ouabain (EO) effects. A clinical trial is ongoing to substantiate these findings. Future studies should apply both the candidate gene and GWAS approaches to fully exploit the potential of genetics in optimizing the personalized therapy.

PMID: 26915067 [PubMed - indexed for MEDLINE]

Endogenous glucose production increases in response to metformin treatment in the glycogen-depleted state in humans: a randomised trial.

Diabetologia. 2015 Nov;58(11):2494-502

Authors: Christensen MM, Højlund K, Hother-Nielsen O, Stage TB, Damkier P, Beck-Nielsen H, Brøsen K

Abstract
AIMS/HYPOTHESIS: Metformin is believed to reduce glucose levels primarily by inhibiting hepatic glucose production. Recent data indicate that metformin antagonises glucagon-dependent glucose output, suggesting that compensatory mechanisms protect against hypoglycaemia. Here, we examined the effect of metformin on glucose metabolism in humans after a glycogen-depleting fast and the role of reduced-function alleles in OCT1 (also known as SLC22A1).
METHODS: In a randomised, crossover trial, healthy individuals with or without reduced-function alleles in OCT1 were fasted for 42 h twice, either with or without prior treatment with 1 g metformin twice daily. Participants were recruited from the Pharmacogenomics Biobank of the University of Southern Denmark. Treatment allocation was generated by the Good Clinical Practice Unit, Odense University Hospital, Denmark. Variables of whole-body glucose metabolism were assessed using [3-(3)H]glucose, indirect calorimetry and measurement of substrates and counter-regulatory hormones. The primary outcome was endogenous glucose production (EGP).
RESULTS: Thirty-seven individuals were randomised. Thirty-four completed the study (12 had none, 13 had one and nine had two reduced-function alleles in OCT1). Three were excluded from the analysis because of early dropout. Metformin significantly stimulated glucose disposal rates and non-oxidative glucose metabolism with no effect on glucose oxidation. This increase in glucose utilisation was explained by a concomitant increase in glycolytic flux and accompanied by increased EGP, most likely mediated by increased plasma lactate, glucagon and cortisol levels. There was no effect of reduced-function OCT1 alleles on any of these measures. All individuals completed the glycogen-depleting fast without hypoglycaemia.
CONCLUSIONS/INTERPRETATION: Metformin stimulates glycolytic glucose utilisation and lactate production in the glycogen-depleted state. This may trigger a rise in glucose counter-regulatory hormones and subsequently an increase in EGP, which protects against hypoglycaemia.
TRIAL REGISTRATION: ClinicalTrials.gov NCT01400191 FUNDING: Danish Research Council for Health and Disease (0602-02695B) and Odense University Hospital Free Research Fund, 2012.

PMID: 26271344 [PubMed - indexed for MEDLINE]

Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts.

BMC Med Inform Decis Mak. 2016;16 Suppl 1:68

Authors: Verspoor KM, Heo GE, Kang KY, Song M

Abstract
BACKGROUND: The Variome corpus, a small collection of published articles about inherited colorectal cancer, includes annotations of 11 entity types and 13 relation types related to the curation of the relationship between genetic variation and disease. Due to the richness of these annotations, the corpus provides a good testbed for evaluation of biomedical literature information extraction systems.
METHODS: In this paper, we focus on assessing performance on extracting the relations in the corpus, using gold standard entities as a starting point, to establish a baseline for extraction of relations important for extraction of genetic variant information from the literature. We test the application of the Public Knowledge Discovery Engine for Java (PKDE4J) system, a natural language processing system designed for information extraction of entities and relations in text, on the relation extraction task using this corpus.
RESULTS: For the relations which are attested at least 100 times in the Variome corpus, we realise a performance ranging from 0.78-0.84 Precision-weighted F-score, depending on the relation. We find that the PKDE4J system adapted straightforwardly to the range of relation types represented in the corpus; some extensions to the original methodology were required to adapt to the multi-relational classification context. The results are competitive with state-of-the-art relation extraction performance on more heavily studied corpora, although the analysis shows that the Recall of a co-occurrence baseline outweighs the benefit of improved Precision for many relations, indicating the value of simple semantic constraints on relations.
CONCLUSIONS: This work represents the first attempt to apply relation extraction methods to the Variome corpus. The results demonstrate that automated methods have good potential to structure the information expressed in the published literature related to genetic variants, connecting mutations to genes, diseases, and patient cohorts. Further development of such approaches will facilitate more efficient biocuration of genetic variant information into structured databases, leveraging the knowledge embedded in the vast publication literature.

PMID: 27454860 [PubMed - in process]

Protein-protein interaction extraction with feature selection by evaluating contribution levels of groups consisting of related features.

BMC Bioinformatics. 2016;17 Suppl 7:246

Authors: Thuy Phan TT, Ohkawa T

Abstract
BACKGROUND: Protein-protein interaction (PPI) extraction from published scientific articles is one key issue in biological research due to its importance in grasping biological processes. Despite considerable advances of recent research in automatic PPI extraction from articles, demand remains to enhance the performance of the existing methods.
RESULTS: Our feature-based method incorporates the strength of many kinds of diverse features, such as lexical and word context features derived from sentences, syntactic features derived from parse trees, and features using existing patterns to extract PPIs automatically from articles. Among these abundant features, we assemble the related features into four groups and define the contribution level (CL) for each group, which consists of related features. Our method consists of two steps. First, we divide the training set into subsets based on the structure of the sentence and the existence of significant keywords (SKs) and apply the sentence patterns given in advance to each subset. Second, we automatically perform feature selection based on the CL values of the four groups that consist of related features and the k-nearest neighbor algorithm (k-NN) through three approaches: (1) focusing on the group with the best contribution level (BEST1G); (2) unoptimized combination of three groups with the best contribution levels (U3G); (3) optimized combination of two groups with the best contribution levels (O2G).
CONCLUSIONS: Our method outperforms other state-of-the-art PPI extraction systems in terms of F-score on the HPRD50 corpus and achieves promising results that are comparable with these PPI extraction systems on other corpora. Further, our method always obtains the best F-score on all the corpora than when using k-NN only without exploiting the CLs of the groups of related features.

PMID: 27454611 [PubMed - in process]

CLASH: Complementary Linkage with Anchoring and Scoring for Heterogeneous biomolecular and clinical data.

BMC Med Inform Decis Mak. 2016;16 Suppl 3:72

Authors: Nam Y, Kim M, Lee K, Shin H

Abstract
BACKGROUND: The study on disease-disease association has been increasingly viewed and analyzed as a network, in which the connections between diseases are configured using the source information on interactome maps of biomolecules such as genes, proteins, metabolites, etc. Although abundance in source information leads to tighter connections between diseases in the network, for a certain group of diseases, such as metabolic diseases, the connections do not occur much due to insufficient source information; a large proportion of their associated genes are still unknown. One way to circumvent the difficulties in the lack of source information is to integrate available external information by using one of up-to-date integration or fusion methods. However, if one wants a disease network placing huge emphasis on the original source of data but still utilizing external sources only to complement it, integration may not be pertinent. Interpretation on the integrated network would be ambiguous: meanings conferred on edges would be vague due to fused information.
METHODS: In this study, we propose a network based algorithm that complements the original network by utilizing external information while preserving the network's originality. The proposed algorithm links the disconnected node to the disease network by using complementary information from external data source through four steps: anchoring, connecting, scoring, and stopping.
RESULTS: When applied to the network of metabolic diseases that is sourced from protein-protein interaction data, the proposed algorithm recovered connections by 97%, and improved the AUC performance up to 0.71 (lifted from 0.55) by using the external information outsourced from text mining results on PubMed comorbidity literatures. Experimental results also show that the proposed algorithm is robust to noisy external information.
CONCLUSION: This research has novelty in which the proposed algorithm preserves the network's originality, but at the same time, complements it by utilizing external information. Furthermore it can be utilized for original association recovery and novel association discovery for disease network.

PMID: 27454118 [PubMed - in process]

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Drug screening: Drug repositioning needs a rethink.

Nature. 2016 Jul 21;535(7612):355

Authors: Ding X

PMID: 27443733 [PubMed - in process]

Prospects for Moxidectin as a New Oral Treatment for Human Scabies.

PLoS Negl Trop Dis. 2016 Mar;10(3):e0004389

Authors: Mounsey KE, Bernigaud C, Chosidow O, McCarthy JS

PMID: 26985995 [PubMed - indexed for MEDLINE]

Web Image Search Re-ranking with Click-based Similarity and Typicality.

IEEE Trans Image Process. 2016 Jul 20;

Authors: Yang X, Mei T, Zhang YD, Liu J, Satoh S

Abstract
In image search re-ranking, besides the well known semantic gap, intent gap, which is the gap between the representation of users' query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image click-through data, which can be viewed as the "implicit feedback" from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis visually similar images should be close in a ranking list and the strategy images with higher relevance should be ranked higher than others are widely accepted. To obtain satisfying search results, thus, image similarity and the level of relevance typicality are determinate factors correspondingly. However, when measuring image similarity and typicality, conventional re-ranking approaches only consider visual information and initial ranks of images, while overlooking the influence of click-through data. This paper presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality (SCCST). First, to learn an appropriate similarity measurement, we propose click-based multi-feature similarity learning algorithm (CMSL), which conducts metric learning based on clickbased triplets selection, and integrates multiple features into a unified similarity space via multiple kernel learning. Then based on the learnt click-based image similarity measure, we conduct spectral clustering to group visually and semantically similar images into same clusters, and get the final re-rank list by calculating click-based clusters typicality and withinclusters click-based image typicality in descending order. Our experiments conducted on two real-world query-image datasets with diverse representative queries show that our proposed reranking approach can significantly improve initial search results, and outperform several existing re-ranking approaches.

PMID: 27448362 [PubMed - as supplied by publisher]

MicroRNAs in hereditary diffuse gastric cancer.

Biomed Rep. 2016 Aug;5(2):151-154

Authors: Suárez-Arriaga MC, Ribas-Aparicio RM, Ruiz-Tachiquín ME

Abstract
In 2012, gastric cancer (GC) was the third cause of mortality due to cancer in men and women. In Central and South America, high mortality rates have been reported. A total of 95% of tumors developed in the stomach are of epithelial origin; thus, these are denominated adenocarcinomas of the stomach. Diverse classification systems have been established, among which two types of GC based on histological type and growth pattern have been described as follows: Intestinal (IGC) and diffuse (DGC). Approximately 1-3% of GC cases are associated with heredity. Hereditary-DGC (HDGC), with 80% penetrance, is an autosomal-type, dominant syndrome in which 40% of cases are carriers of diverse mutations of the CDH1 gene, which encodes for the cadherin protein. By contrast, microRNA are non-encoded, single-chain RNA molecules. These molecules regulate the majority of cellular functions at the post-transcriptional level. However, analysis of these interactions by means of Systems Biology has allowed the understanding of complex and heterogeneous diseases, such as cancer. These molecules are ubiquitous; however, their expression can be specific in different tissues either temporarily or permanently, depending on the stage of the cell. Due to the participation of microRNA in the processes of cellular proliferation, cell cycle control, apoptosis, differentiation and metabolism, these have been indicated to have a role in the development of cancerous processes, finding specific patterns of expression in different neoplasms, including GC, in which the microRNA expression profile is different in samples of non-cancerous versus cancerous tissues. A difference has been observed in the expression patterns of DGC and IGC. However, the role of microRNA in HDGC has not yet been established. The present study reviews the investigations that describe the participation of microRNA in the regulation of genes CDH1, RHOA, CTNNA1, INSR and TGF-β in different neoplasms, such as HDGC.

PMID: 27446532 [PubMed - as supplied by publisher]

The Significance of an Enhanced Concept of the Organism for Medicine.

Evid Based Complement Alternat Med. 2016;2016:1587652

Authors: Rosslenbroich B

Abstract
Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

PMID: 27446221 [PubMed]

Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

Front Plant Sci. 2016;7:903

Authors: Li J, Zhao PX

Abstract
Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

PMID: 27446133 [PubMed]

Editorial: Systems Biological Aspects of Pituitary Tumors.

Front Endocrinol (Lausanne). 2016;7:86

Authors: Zhan X, Desiderio DM

PMID: 27445988 [PubMed]

Systems Biology of Immunomodulation for Post-Stroke Neuroplasticity: Multimodal Implications of Pharmacotherapy and Neurorehabilitation.

Front Neurol. 2016;7:94

Authors: Alam MA, Subramanyam Rallabandi VP, Roy PK

Abstract
AIMS: Recent studies indicate that anti-inflammatory drugs, act as a double-edged sword, not only exacerbating secondary brain injury but also contributing to neurological recovery after stroke. Our aim is to explore whether there is a beneficial role for neuroprotection and functional recovery using anti-inflammatory drug along with neurorehabilitation therapy using transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), so as to improve functional recovery after ischemic stroke.
METHODS: We develop a computational systems biology approach from preclinical data, using ordinary differential equations, to study the behavior of both phenotypes of microglia, such as M1 type (pro-inflammatory) vis-à-vis M2 type (anti-inflammatory) under anti-inflammatory drug action (minocycline). We explore whether pharmacological treatment along with cerebral stimulation using tDCS and rTMS is beneficial or not. We utilize the systems pathway analysis of minocycline in nuclear factor kappa beta (NF-κB) signaling and neurorehabilitation therapy using tDCS and rTMS that act through brain-derived neurotrophic factor (BDNF) and tropomyosin-related kinase B (TrkB) signaling pathways.
RESULTS: We demarcate the role of neuroinflammation and immunomodulation in post-stroke recovery, under minocycline activated-microglia and neuroprotection together with improved neurogenesis, synaptogenesis, and functional recovery under the action of rTMS or tDCS. We elucidate the feasibility of utilizing rTMS/tDCS to increase neuroprotection across the reperfusion stage during minocycline administration. We delineate that the signaling pathways of minocycline by modulation of inflammatory genes in NF-κB and proteins activated by tDCS and rTMS through BDNF, TrkB, and calmodulin kinase (CaMK) signaling. Utilizing systems biology approach, we show that the activation pathways for pharmacotherapy (minocycline) and neurorehabilitation (rTMS applied to ipsilesional cortex and tDCS) results into increased neuronal and synaptic activity that commonly occur through activation of N-methyl-d-aspartate receptors. We construe that considerable additive neuroprotection effect would be obtained and delayed reperfusion injury can be remedied, if one uses multimodal intervention of minocycline together with tDCS and rTMS.
CONCLUSION: Additive beneficial effect is, thus, noticed for pharmacotherapy along with neurorehabilitation therapy, by maneuvering the dynamics of immunomodulation using anti-inflammatory drug and cerebral stimulation for augmenting the functional recovery after stroke, which may engender clinical applicability for enhancing plasticity, rehabilitation, and neurorestoration.

PMID: 27445961 [PubMed]

A Hybrid of the Chemical Master Equation and the Gillespie Algorithm for Efficient Stochastic Simulations of Sub-Networks.

PLoS One. 2016;11(3):e0149909

Authors: Albert J

Abstract
Modeling stochastic behavior of chemical reaction networks is an important endeavor in many aspects of chemistry and systems biology. The chemical master equation (CME) and the Gillespie algorithm (GA) are the two most fundamental approaches to such modeling; however, each of them has its own limitations: the GA may require long computing times, while the CME may demand unrealistic memory storage capacity. We propose a method that combines the CME and the GA that allows one to simulate stochastically a part of a reaction network. First, a reaction network is divided into two parts. The first part is simulated via the GA, while the solution of the CME for the second part is fed into the GA in order to update its propensities. The advantage of this method is that it avoids the need to solve the CME or stochastically simulate the entire network, which makes it highly efficient. One of its drawbacks, however, is that most of the information about the second part of the network is lost in the process. Therefore, this method is most useful when only partial information about a reaction network is needed. We tested this method against the GA on two systems of interest in biology--the gene switch and the Griffith model of a genetic oscillator--and have shown it to be highly accurate. Comparing this method to four different stochastic algorithms revealed it to be at least an order of magnitude faster than the fastest among them.

PMID: 26930199 [PubMed - indexed for MEDLINE]

Foundations and Emerging Paradigms for Computing in Living Cells.

J Mol Biol. 2016 Feb 27;428(5 Pt B):893-915

Authors: Ma KC, Perli SD, Lu TK

Abstract
Genetic circuits, composed of complex networks of interacting molecular machines, enable living systems to sense their dynamic environments, perform computation on the inputs, and formulate appropriate outputs. By rewiring and expanding these circuits with novel parts and modules, synthetic biologists have adapted living systems into vibrant substrates for engineering. Diverse paradigms have emerged for designing, modeling, constructing, and characterizing such artificial genetic systems. In this paper, we first provide an overview of recent advances in the development of genetic parts and highlight key engineering approaches. We then review the assembly of these parts into synthetic circuits from the perspectives of digital and analog logic, systems biology, and metabolic engineering, three areas of particular theoretical and practical interest. Finally, we discuss notable challenges that the field of synthetic biology still faces in achieving reliable and predictable forward-engineering of artificial biological circuits.

PMID: 26908220 [PubMed - indexed for MEDLINE]

Systems biology of immunity to MF59-adjuvanted versus nonadjuvanted trivalent seasonal influenza vaccines in early childhood.

Proc Natl Acad Sci U S A. 2016 Feb 16;113(7):1853-8

Authors: Nakaya HI, Clutterbuck E, Kazmin D, Wang L, Cortese M, Bosinger SE, Patel NB, Zak DE, Aderem A, Dong T, Del Giudice G, Rappuoli R, Cerundolo V, Pollard AJ, Pulendran B, Siegrist CA

Abstract
The dynamics and molecular mechanisms underlying vaccine immunity in early childhood remain poorly understood. Here we applied systems approaches to investigate the innate and adaptive responses to trivalent inactivated influenza vaccine (TIV) and MF59-adjuvanted TIV (ATIV) in 90 14- to 24-mo-old healthy children. MF59 enhanced the magnitude and kinetics of serum antibody titers following vaccination, and induced a greater frequency of vaccine specific, multicytokine-producing CD4(+) T cells. Compared with transcriptional responses to TIV vaccination previously reported in adults, responses to TIV in infants were markedly attenuated, limited to genes regulating antiviral and antigen presentation pathways, and observed only in a subset of vaccinees. In contrast, transcriptional responses to ATIV boost were more homogenous and robust. Interestingly, a day 1 gene signature characteristic of the innate response (antiviral IFN genes, dendritic cell, and monocyte responses) correlated with hemagglutination at day 28. These findings demonstrate that MF59 enhances the magnitude, kinetics, and consistency of the innate and adaptive response to vaccination with the seasonal influenza vaccine during early childhood, and identify potential molecular correlates of antibody responses.

PMID: 26755593 [PubMed - indexed for MEDLINE]

Synthetic Ecology of Microbes: Mathematical Models and Applications.

J Mol Biol. 2016 Feb 27;428(5 Pt B):837-61

Authors: Zomorrodi AR, Segrè D

Abstract
As the indispensable role of natural microbial communities in many aspects of life on Earth is uncovered, the bottom-up engineering of synthetic microbial consortia with novel functions is becoming an attractive alternative to engineering single-species systems. Here, we summarize recent work on synthetic microbial communities with a particular emphasis on open challenges and opportunities in environmental sustainability and human health. We next provide a critical overview of mathematical approaches, ranging from phenomenological to mechanistic, to decipher the principles that govern the function, dynamics and evolution of microbial ecosystems. Finally, we present our outlook on key aspects of microbial ecosystems and synthetic ecology that require further developments, including the need for more efficient computational algorithms, a better integration of empirical methods and model-driven analysis, the importance of improving gene function annotation, and the value of a standardized library of well-characterized organisms to be used as building blocks of synthetic communities.

PMID: 26522937 [PubMed - indexed for MEDLINE]