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Whole-Exome Sequencing (WES) is a valuable tool for the molecular diagnosis of patients with a suspected genetic condition. In complex and heterogeneous diseases, the interpretation of WES variants is more challenging given the absence of diagnostic handles and other reported cases with overlapping clinical presentations.To describe candidate variants emerging from trio-WES and possibly associated with the clinical phenotype in clinically heterogeneous conditions.We performed WES in ten patients from eight families, selected because of the lack of a clear clinical diagnosis or suspicion, the presence of multiple clinical signs, and the negative results of traditional genetic tests.Although we identified ten candidate variants, reaching the diagnosis of these cases is challenging, given the complexity and the rarity of these syndromes and because affected genes are already associated with known genetic diseases only partially recapitulating patients' phenotypes. However, the identification of these variants could shed light into the definition of new genotype-phenotype correlations. Here, we describe the clinical and molecular data of these cases with the aim of favoring the match with other similar cases and, hopefully, confirm our diagnostic hypotheses.This study emphasizes the major limitations associated with WES data interpretation, but also highlights its clinical utility in unraveling novel genotype-phenotype correlations in complex and heterogeneous undefined clinical conditions with a suspected genetic etiology.

Glioblastoma (GBM) is the most malignant human brain tumour, characterized by rapid progression, invasion, intense angiogenesis, high genomic instability, and resistance to therapies. Despite countless experimental researches for new therapeutic strategies and promising clinical trials, the prognosis remains extremely poor, with a mean survival of less than 14 months. GBM aggressive behaviour is due to a subpopulation of tumourigenic stem-like cells, GBM stem cells (GSCs), which hierarchically drive onset, proliferation, and tumour recurrence. The morbidity and mortality of this disease strongly encourage exploring genetic characteristics of GSCs. Here, using array-CGH platform, we investigated genetic and genomic aberration profiles of GBM parent tumour (n=10) and their primarily derived GSCs. Statistical analysis was performed by using R software and complex heatmap and corrplot packages. Pearson correlation and K-means algorithm were exploited to compare genetic alterations and to group similar genetic profiles in matched pairs of GBM and derived GSCs. We identified, in both GBM and matched GSCs, recurrent copy number alterations, as chromosome 7 polysomy, chromosome 10 monosomy, and chromosome 9p21deletions, which are typical features of primary GBM, essential for gliomagenesis. These observations suggest a condition of strong genomic instability both in GBM as GSCs. Our findings showed the robust similarity between GBM mass and GSCs (Pearson corr.≥0.65) but also highlighted a marked variability among different patients. Indeed, the heatmap reporting Gain/Loss State for 21022 coding/noncoding genes demonstrated high interpatient divergence. Furthermore, K-means algorithm identified an impairment of pathways related to the development and progression of cancer, such as angiogenesis, as well as pathways related to the immune system regulation, such as T cell activation. Our data confirmed the preservation of the genomic landscape from tumour tissue to GSCs, supporting the relevance of this cellular model to test in vitro new target therapies for GBM.

Background De novo pathogenic variants in the DDX3X gene are reported to account for 1–3% of unexplained intellectual disability (ID) in females, leading to the rare disease known as DDX3X syndrome (MRXSSB, OMIM #300958). Besides ID, these patients manifest a variable clinical presentation, which includes neurological and behavioral defects, and abnormal brain MRIs. Case presentation We report a 10-year-old girl affected by delayed psychomotor development, delayed myelination, and polymicrogyria (PMG). We identified a novel de novo missense mutation in the DDX3X gene (c.625C > G) by whole exome sequencing (WES). The DDX3X gene encodes a DEAD-box ATP-dependent RNA-helicase broadly implicated in gene expression through regulation of mRNA metabolism. The identified mutation is located just upstream the helicase domain and is suggested to impair the protein activity, thus resulting in the altered translation of DDX3X-dependent mRNAs. The proband, presenting with the typical PMG phenotype related to the syndrome, does not show other clinical signs frequently reported in presence of missense DDX3X mutations that are associated with a most severe clinical presentation. In addition, she has brachycephaly, never described in female DDX3X patients, and macroglossia, that has never been associated with the syndrome. Conclusions This case expands the knowledge of DDX3X pathogenic variants and the associated DDX3X syndrome phenotypic spectrum.

The placental methylation pattern is crucial for the regulation of genes involved in trophoblast invasion and placental development, both key events for fetal growth. We investigated LINE-1 methylation and methylome profiling using a methylation EPIC array and the targeted methylation sequencing of 154 normal, full-term pregnancies, stratified by birth weight percentiles. LINE-1 methylation showed evidence of a more pronounced hypomethylation in small neonates compared with normal and large for gestational age. Genome-wide methylation, performed in two subsets of pregnancies, showed very similar methylation profiles among cord blood samples while placentae from different pregnancies appeared very variable. A unique methylation profile emerged in each placenta, which could represent the sum of adjustments that the placenta made during the pregnancy to preserve the epigenetic homeostasis of the fetus. Investigations into the 1000 most variable sites between cord blood and the placenta showed that promoters and gene bodies that are hypermethylated in the placenta are associated with blood-specific functions, whereas those that are hypomethylated belong mainly to pathways involved in cancer. These features support the functional analogies between a placenta and cancer. Our results, which provide a comprehensive analysis of DNA methylation profiling in the human placenta, suggest that its peculiar dynamicity can be relevant for understanding placental plasticity in response to the environment.

Background&Aims: Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease and has a strong heritable component. The aim of this study was to identify new genes involved in NAFLD pathogenesis. Methods: We examined rare variants captured by whole-exome sequencing in individuals with severe fibrosis or hepatocellular carcinoma due to NAFLD (severe NAFLD, n=301) after variant prioritization. We replicated the results in the UK Biobank and the Liver biopsy cohort (n=2268). Results: We observed an enrichment of the p.P426L variant (rs143545741 C>T; OR=7.2, 2.3-17.3; p C; MAF=0.060 vs. 0.035; OR=1.7, 1.2-2.5; p=0.003). In the UK Biobank cohort, the p.V471A variant was associated with NAFLD (p=0.009) and liver disorders (p=0.004). In the Liver biopsy cohort, the p.V471A variant was linked with severe fibrosis, particularly in those with severe steatosis (p=0.002). Moreover, p.V471A was an independent predictor of hepatocellular ballooning (p=0.007). Hepatic ATG7 expression correlated with suppression of the TNFα pathway, which was conversely upregulated in p.V471A carriers (n=125 with available transcriptomic). ATG7 protein localized to non-parenchymal cells and predominantly in periportal hepatocytes, more so in the presence of ballooning. Finally, we confirmed that in vitro the p.P426L and p.V471A variants result in a protein loss-of-function. Conclusion: We identified novel rare and low-frequency ATG7 variants contributing to severe NAFLD. The underlying mechanism may involve impairment of autophagy and facilitation of hepatocellular ballooning degeneration.

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Background Maternal dietary habits are contributors of maternal and fetal health; however, available data are heterogeneous and not conclusive. Methods Nutrient intake during pregnancy was assessed in 503 women with uncomplicated pregnancies, using the validated Food Frequency Questionnaire developed by the European Prospective Investigation into Cancer and Nutrition (EPIC-FFQ). Results In all, 68% of women had a normal body mass index at the beginning of pregnancy, and 83% of newborns had an appropriate weight for gestational age. Maternal pre-pregnancy body mass index (BMI), gestational weight gain (GWG), and placental weight were independently correlated with birth weight. GWG was not related to the pre-pregnancy BMI. EPIC-FFQ evaluation showed that 30% of women adhered to the European Food Safety Authority (EFSA) ranges for macronutrient intake. In most pregnant women (98.1%), consumption of water was below recommendations. Comparing women with intakes within EFSA ranges for macronutrients with those who did not, no differences were found in BMI, GWG, and neonatal or placental weight. Neither maternal nor neonatal parameters were associated with the maternal dietary profiles. Conclusions In our population, maternal pre-pregnancy BMI, GWG, and placental weight are determinants of birth weight percentile, while no association was found with maternal nutrition. Future studies should explore associations through all infancy. Impact Maternal anthropometrics and nutrition status may affect offspring birth weight. In 503 healthy women, maternal pre-pregnancy body mass index (BMI), gestational weight gain (GWG), and placental weight were independently correlated to neonatal birth weight. GWG was not related to the pre-pregnancy BMI. In all, 30% of women respected the EFSA ranges for macronutrients. Neither maternal nor neonatal parameters were associated with maternal dietary profiles considered in this study. Maternal pre-pregnancy BMI, GWG, and placental weight are determinants of neonatal birth weight percentile, while a connection with maternal nutrition profiles was not found.

Background: De novo pathogenic variants in the DDX3X gene are reported to account for 1–3% of unexplained intellectual disability (ID) in females, leading to the rare disease known as DDX3X syndrome (MRXSSB, OMIM #300958). Besides ID, these patients manifest a variable clinical presentation, which includes neurological and behavioral defects, and abnormal brain MRIs. Case presentation: We report a 10-year-old girl affected by delayed psychomotor development, delayed myelination, and polymicrogyria (PMG). We identified a novel de novo missense mutation in the DDX3X gene (c.C625G) by exome sequencing. The DDX3X gene encodes a DEAD-box ATP-dependent RNA-helicase broadly implicated in gene expression through regulation of mRNA metabolism. The identified mutation is located just upstream the helicase domain and is suggested to impair the protein activity, thus resulting in the altered translation of DDX3X-dependent mRNAs. The proband, presenting with the typical PMG phenotype related to the syndrome, does not show other clinical signs frequently reported in presence of missense DDX3X mutations that are associated with a most severe clinical presentation. In addition, she has brachycephaly, never described in female DDX3X patients, and macroglossia, that has never been associated with the syndrome.Conclusions: This case expands the knowledge of DDX3X pathogenic variants and the associated DDX3X syndrome phenotypic spectrum.

Background Beckwith–Wiedemann syndrome (BWS) is an overgrowth disorder caused by defects at the 11p15.5 imprinted region. Many cases of female monozygotic (MZ) twins discordant for BWS have been reported, but no definitive conclusions have been drawn regarding the link between epigenetic defects, twinning process, and gender. Here, we report a comprehensive characterization and follow‐up of female MZ twins discordant for BWS. Methods Methylation pattern at 11p15.5 and multilocus methylation disturbance (MLID) profiling were performed by pyrosequencing and MassARRAY in placental/umbilical cord samples and postnatal tissues. Whole‐exome sequencing was carried out to identify MLID causative mutations. X‐chromosome inactivation (XCI) was determined by HUMARA test. Results Both twins share KCNQ1OT1:TSS‐DMR loss of methylation (LOM) and MLID in blood and the epigenetic defect remained stable in the healthy twin over time. KCNQ1OT1:TSS‐DMRLOM was nonhomogeneously distributed in placental samples and the twins showed the same severely skewed XCI pattern. No MLID‐causative mutations were identified. Conclusion This is the first report on BWS‐discordant twins with methylation analyses extended to extraembryonic tissues. The results suggest that caution is required when attempting prenatal diagnosis in similar cases. Although the causative mechanism underlying LOM remains undiscovered, the XCI pattern and mosaic LOM suggest that both twinning and LOM/MLID occurred after XCI commitment.

Previous studies on breast and ovarian carcinoma (BC and OC) revealed constitutional BRCA1 and RAD51C promoter hypermethylation as epigenetic alterations leading to tumor predisposition. Nevertheless, the impact of epimutations at these genes is still debated. One hundred and eight women affected by BC, OC, or both and considered at very high risk of carrying BRCA1 germline mutations were studied. All samples were negative for pathogenic variants or variants of uncertain significance at BRCA testing. Quantitative BRCA1 and RAD51C promoter methylation analyses were performed by Epityper mass spectrometry on peripheral blood samples and results were compared with those in controls. All the 108 analyzed cases showed methylation levels at the BRCA1/RAD51C promoter comparable with controls. Mean methylation levels (&plusmn, stdev) at the BRCA1 promoter were 4.3% (&plusmn, 1.4%) and 4.4% (&plusmn, 1.4%) in controls and patients, respectively (p &gt, 0.05, t-test), mean methylation levels (&plusmn, stdev) at the RAD51C promoter were 4.3% (&plusmn, 0.9%) and 3.7% (&plusmn, 0.9%) in controls and patients, respectively (p &gt, t-test). Based on these observations, the analysis of constitutional methylation at promoters of these genes does not seem to substantially improve the definition of cancer risks in patients. These data support the idea that epimutations represent a very rare event in high-risk BC/OC populations.

X-linked intellectual disability (XLID) refers to a clinically and genetically heterogeneous neurodevelopmental disorder, in which males are more heavily affected than females. Among the syndromic forms of XLID, identified by additional clinical signs as part of the disease spectrum, the association between XLID and severe myopia has been poorly characterized. We used whole exome sequencing (WES) to study two Italian male twins presenting impaired intellectual function and adaptive behavior, in association with severe myopia and mild facial dysmorphisms. WES analysis detected the novel, maternally inherited, mutation c.916G > C (G306R) in the X-linked heparan sulfate 6-O-sulfotransferase 2 (HS6ST2) gene. HS6ST2 transfers sulfate from adenosine 3'-phosphate, 5'-phosphosulfate to the sixth position of the N-sulphoglucosamine residue in heparan sulfate (HS) proteoglycans. Low HS sulfation levels are associated with defective optic disc and stalk morphogenesis during mammalian visual system development. The c.916G>C variant affects the HS6ST2 substrate binding site, and its effect was considered "deleterious" by in-silico tools. An in-vitro enzymatic assay showed that the HS6ST2 mutant isoform had significantly reduced sulphotransferase activity. Taken together, the results suggest that mutant HS6ST2 is possibly involved in the development of myopia and cognitive impairment, characteristics of the probands reported here.

Ethanol is among the largest fermentation product used worldwide, accounting for more than $$90\%$$ of all biofuel produced in the last decade. However current production methods of ethanol are unable to meet the requirements of increasing global demand, because of low yields on glucose sources. In this work, we present an in silico multi-objective optimization and analyses of eight genome-scale metabolic networks for the overproduction of ethanol within the engineered cell. We introduce MOME (multi-objective metabolic engineering) algorithm, that models both gene knockouts and enzymes up and down regulation using the Redirector framework. In a multi-step approach, MOME tackles the multi-objective optimization of biomass and ethanol production in the engineered strain; and performs genetic design and clustering analyses on the optimization results. We find in silico E. coli Pareto optimal strains with a knockout cost of 14 characterized by an ethanol production up to $$ 19.74 \, \hbox {mmol} \, \hbox {gDW}^{-1} \, \hbox {h}^{-1} $$ ( $$+\,832.88 \% $$ with respect to wild-type) and biomass production of $$0.02 \, \hbox {h}^{-1}$$ ( $$-\,98.06 \% $$ ). The analyses on E. coli highlighted a single knockout strategy producing $$16.49 \, \hbox {mmol} \, \hbox {gDW}^{-1} \, \hbox {h}^{-1} $$ ( $$+\,679.29 \%$$ ) ethanol, with biomass equals to $$0.23 \, \hbox {h}^{-1}$$ ( $$-\,77.45 \% $$ ). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica. We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. In the same conditions the maximum improvement in ethanol production is $$+\,195.24\%$$ .

Estrogen receptor (ER)-positive progesterone receptor (PR)-negative breast cancers are infrequent but clinically challenging. Despite the volume of genomic data available on these tumors, their biology remains poorly understood. Here, we aimed to identify clinically relevant subclasses of ER+/PR&minus, breast cancers based on their mutational landscape. The Cancer Genomics Data Server was interrogated for mutational and clinical data of all ER+ breast cancers with information on PR status from The Cancer Genome Atlas (TCGA), Memorial Sloan Kettering (MSK), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) projects. Clustering analysis was performed using gplots, ggplot2, and ComplexHeatmap packages. Comparisons between groups were performed using the Student&rsquo, s t-test and the test of Equal or Given Proportions. Survival curves were built according to the Kaplan&ndash, Meier method, differences in survival were assessed with the log-rank test. A total of 3570 ER+ breast cancers (PR&minus, n = 959, 27%, PR+ n = 2611, 73%) were analyzed. Mutations in well-known cancer genes such as TP53, GATA3, CDH1, HER2, CDH1, and BRAF were private to or enriched for in PR&minus, tumors. Mutual exclusivity analysis revealed the presence of four molecular clusters with significantly different prognosis on the basis of PIK3CA and TP53 status. ER+/PR&minus, breast cancers are genetically heterogeneous and encompass a variety of distinct entities in terms of prognostic and predictive information.

Treatment of metastatic melanoma has been recently revolutionized by the use of targeted and immune inhibitors, though efficiently working only in a fraction of patients. Adverse effects still represent major challenges, as well as the rapid development of drug resistance upon treatment, thus suggesting that it is of utmost importance to elucidate additional molecular pathways and targets to better understand the biology of this tumor. In vivo RNAi screens done in patient-derived xenografts (PDXs) that fully phenocopy the original tumor of the patient represent a powerful tool to identify new putative essential and druggable candidates, and to repurpose drugs already approved for other indications. To this end we have recently generated a platform of melanoma PDXs with which we can recapitulate both melanoma growth and metastasization. We have performed in vivo shRNA screens, where we have found numerous and patient-specific epigenetic genes essential for tumor growth in vivo. With this platform, we have isolated novel frailties whose depletion favors cell senescence. Preliminary data using shRNA libraries of actionable genes for which drugs are already available suggest that specific drugs can be repurposed for melanoma treatment. We have proved that this approach can work in vivo, and we propose to convert it in an in vitro assay to rapidly translate new discoveries to the patient. Note: This abstract was not presented at the conference. Citation Format: Federica Marocchi, Letizia Granieri, Fiorenza Lotti, Fernando Palluzzi, Jole Costanza, Laura Riva, Daniela Bossi, Luisa Lanfrancone. New treatment opportunities by in vivo and in vitro screening approaches in melanoma [abstract]. In: Proceedings of the AACR Special Conference on Melanoma: From Biology to Target; 2019 Jan 15-18; Houston, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(19 Suppl):Abstract nr A15.

If Turing were a first-year graduate student interested in computers,he would probably migrate into the field of computational biology. During his studies, he presenteda work about a mathematical and computational model of the morphogenesis process, in which chemical substancesreact together. Moreover, a protein can be thought of as a computational element, i.e. a processing unit, able totransform an input into an output signal. Thus, in a biochemical pathway, an enzyme reads the amount of reactants (substrates)and converts them in products. In this work, we consider the biochemical pathway in unicellular organisms (e.g. bacteria) as a living computer, and we are able to program it in order to obtain desired outputs.The genome sequence is thought of as an executable code specified by a set of commands in a sort of ad-hoc low-level programming language. Each combination of genes is coded as a string of bits $y \in \left \{ 0 , 1 \right \}^L$, each of which represents a gene set. By turning off a gene set, we turn off the chemical reaction associated with it. Through an optimal executable code stored in the ``memory'' of bacteria, we are able to simultaneously maximise the concentration of two or more metabolites of interest.Finally, we use the Robustness Analysis and a new Sensitivity Analysis method to investigate both the fragility of the computation carried out by bacteria and the most important entities in the mathematical relations used to model them.

Background The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots. Results We analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family. Conclusions LowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis. LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0935-7) contains supplementary material, which is available to authorized users.

Recent advances in synthetic biology call for robust, flexible and efficient in silico optimization methodologies. We present a Pareto design approach for the bi-level optimization problem associated to the overproduction of specific metabolites in Escherichia coli. Our method efficiently explores the high dimensional genetic manipulation space, finding a number of trade-offs between synthetic and biological objectives, hence furnishing a deeper biological insight to the addressed problem and important results for industrial purposes. We demonstrate the computational capabilities of our Pareto-oriented approach comparing it with state-of-the-art heuristics in the overproduction problems of i) 1,4-butanediol, ii) myristoyl-CoA, i ii) malonyl-CoA , iv) acetate and v) succinate. We show that our algorithms are able to gracefully adapt and scale to more complex models and more biologically-relevant simulations of the genetic manipulations allowed. The Results obtained for 1,4-butanediol overproduction significantly outperform results previously obtained, in terms of 1,4-butanediol to biomass formation ratio and knock-out costs. In particular overproduction percentage is of $+662.7\%$ , from $1.425~{\rm mmolh}^{-1}{\rm gDW}^{-1}$ (wild type) to $10.869~{\rm mmolh}^{-1}{\rm gDW}^{-1}$ , with a knockout cost of 6. Whereas, Pareto-optimal designs we have found in fatty acid optimizations strictly dominate the ones obtained by the other methodologies, e.g., biomass and myristoyl-CoA exportation improvement of $+21.43\%~(0.17~{\rm h}^{-1})$ and $+5.19\%~(1.62~{\rm mmolh}^{-1}{\rm gDW}^{-1})$ , respectively. Furthermore CPU time required by our heuristic approach is more than halved. Finally we implement pathway oriented sensitivity analysis, epsilon-dominance analysis and robustness analysis to enhance our biological understanding of the problem and to improve the optimization algorithm capabilities.

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.

In this paper, we discuss data and methodological challenges for building bacterial communication networks using two examples: E. coli as a flagellate bacterium and G. sulfurreducens as a biofilm forming bacterium. We first highlight the link between the bacterial network communication design with respect to metabolic information processing design. The potentialities of designing routing network schemes described previously in the literature and based on bacteria motility and genetic message exchanges will depend on the genes coding for the intracellular and intercellular signalling pathways. In bacteria, the “mobilome” is related to horizontal gene transfer. Bacteria trade-off the acquisition of new genes which could improve their survival (and often their communication bandwidth), keeping their genome small enough to ensure quick DNA replication and fast increase of the biomass to speed up cell division. First, by using a multi-objective optimisation procedure, we search for the optimal trade-off between energy production, which is a requirement for the motility, and biomass growth, which is related to the overall survival and fitness of the bacterium. We use flux balance analysis of the genome-scale biochemical network of E. coli k-13 MG1655. Then, as a second case study, we analyse the electric properties and biomass trade-off of the bacterium G. sulfurreducens , which constructs an electric biofilm where electrons move across the nanowires. Here we discuss the potentialities of optimisation methodologies to design and select bacterial strains with desiderata properties. The optimisation methodologies establish also a relation between metabolism, network communication and computing. Moreover, we point to genetic design and synthetic biology as key areas to develop bacterial nano communication networks.

Inverse modeling of geophysical observations is becoming an important topic in volcanology. The advantage of exploiting innovative inverse methods in volcanology is twofold by providing: a robust tool for the interpretation of the observations and a quantitative model-based assessment of volcanic hazard. This paper re-interprets the data collected during the 1981 eruption of Mt Etna, which offers a good case study to explore and validate new inversion algorithms. Single-objective optimization and multi-objective optimization are here applied in order to improve the fitting of the geophysical observations and better constrain the model parameters. We explore the genetic algorithm NSGA2 and the differential evolution DE method. The inverse results provide a better fitting of the model to the geophysical observations with respect to previously published results. In particular, NSGA2 shows low fitting error in electro-optical distance measurements EDM, leveling and micro-gravity measurements; while the DE algorithm provides a set of solutions that combine low leveling error with low EDM error but that are characterized by a poor capability of minimizing all measures at the same time. The sensitivity of the model to variations of its parameters are investigated by means of the Morris technique and the Sobol' indices with the aim of identifying the parameters that have higher impact on the model. In particular, the model parameters, which define the sources position, their dip and the porosity of the infiltration zones, are found to be the more sensitive. In addition, being the robustness a good indicator of the quality of a solution, a subset of solutions with good characteristics is selected and their robustness is evaluated in order to identify the more suitable model.

In low and high eukaryotes, energy is collected or transformed in compartments, the organelles. The rich variety of size, characteristics, and density of the organelles makes it difficult to build a general picture. In this paper, we make use of the Pareto-front analysis to investigate the optimization of energy metabolism in mitochondria and chloroplasts. Using the Pareto optimality principle, we compare models of organelle metabolism on the basis of single- and multiobjective optimization, approximation techniques (the Bayesian Automatic Relevance Determination), robustness, and pathway sensitivity analysis. Finally, we report the first analysis of the metabolic model for the hydrogenosome of Trichomonas vaginalis, which is found in several protozoan parasites. Our analysis has shown the importance of the Pareto optimality for such comparison and for insights into the evolution of the metabolism from cytoplasmic to organelle bound, involving a model order reduction. We report that Pareto fronts represent an asymptotic analysis useful to describe the metabolism of an organism aimed at maximizing concurrently two or more metabolite concentrations.

The bioenergetic activity of mitochondria can be thoroughly investigated by using computational methods. In particular, in our work we focus on ATP and NADH, namely the metabolites representing the production of energy in the cell. We develop a computational framework to perform an exhaustive investigation at the level of species, reactions, genes and metabolic pathways. The framework integrates several methods implementing the state-of-the-art algorithms for many-objective optimization, sensitivity, and identifiability analysis applied to biological systems. We use this computational framework to analyze three case studies related to the human mitochondria and the algal metabolism of Chlamydomonas reinhardtii, formally described with algebraic differential equations or flux balance analysis. Integrating the results of our framework applied to interacting organelles would provide a general-purpose method for assessing the production of energy in a biological network.