L-lactate production in engineered Saccharomyces cerevisiae using a multistage multiobjective automated design framework.

The design of alternative biodegradable polymers has the potential of severely reducing the environmental impact, cost and production time currently associated with the petrochemical industry. In fact, growing demand for renewable feedstock has recently brought to the fore synthetic biology and metabolic engineering. These two interdependent research areas focus on the study of microbial conversion of organic acids, with the aim of replacing their petrochemical-derived equivalents with more sustainable and efficient processes. The particular case of Lactic acid (LA) production has been the subject of extensive research because of its role as an essential component for developing an eco-friendly biodegradable plastic-widely used in industrial biotechnological applications. Because of its resistance to acidic environments, among the many LA-producing microbes, Saccharomyces cerevisiae has been the main focus of research into related biocatalysts. In this study, we present an extensive in silico investigation of S. cerevisiae cell metabolism (modeled with Flux Balance Analysis) with the overall aim of maximizing its LA production yield. We focus on the yeast 8.3 steady-state metabolic model and analyze it under the impact of different engineering strategies including: gene knock-in, gene knock-out, gene regulation and medium optimization; as well as a comparison between results in aerobic and anaerobic conditions. We designed ad-hoc constrained multiobjective evolutionary algorithms to automate the engineering process and developed a specific postprocessing methodology to analyze the genetic manipulation results obtained. The in silico results reported in this paper empirically show that our method is able to automatically select a small number of promising genetic and metabolic manipulations, deriving competitive strains that promise to impact microorganisms design in the production of sustainable chemicals.

Pareto optimal metabolic engineering for the growth-coupled overproduction of sustainable chemicals.

Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta-carotene (ß-carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of ß-carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution space to highlight near-optimal strains on Pareto Fronts, balancing the above-cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this study produced robust results: seven significant strains of Y. lipolytica and seven strains of S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. This study offers solutions for developing an efficient in vitro mutagenesis for microorganisms and demonstrates the efficiency of multiobjective optimization in automatizing metabolic engineering processes.

Assessment of pregnancy dietary intake and association with maternal and neonatal outcomes.

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.

Correction: Rondinone et al. Extensive Placental Methylation Profiling in Normal Pregnancies. Int. J. Mol. Sci. 2021, 22, 2136.

In the original publication [...].

Extensive Placental Methylation Profiling in Normal Pregnancies.

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.

PTEN Expression as a Complementary Biomarker for Mismatch Repair Testing in Breast Cancer.

Mismatch repair (MMR) analysis in breast cancer may help to inform immunotherapy decisions but it lacks breast-specific guidelines. Unlike in other neoplasms, MMR protein loss shows intra-tumor heterogeneity and it is not mirrored by microsatellite instability in the breast. Additional biomarkers can improve MMR clinical testing. Phosphatase and tensin homolog (PTEN) inactivation is an early oncogenic event that is associated with MMR deficiency (dMMR) in several tumors. Here, we sought to characterize the diagnostic utility of PTEN expression analysis for MMR status assessment in breast cancer. A total of 608 breast cancers were profiled for their MMR and PTEN status. Proteins expression and distribution were analyzed by immunohistochemistry (IHC) on tissue microarrays and confirmed on full sections; PTEN copy number alterations were detected using a real-time PCR assay. Overall, 78 (12.8%) cases were MMR-heterogeneous (hMMR), while all patterns of PTEN expression showed no intra-tumor heterogeneity. Wild-type PTEN expression was observed in 15 (18.5%) dMMR tumors (p < 0.0001). Survival analyses revealed significant correlations between MMR-proficient (pMMR), PTEN expression, and a better outcome. The positive predictive value of PTEN-retained status for pMMR ranged from 94.6% in estrogen receptor (ER)+/HER2- tumors to 100% in HER2-amplified and ER-/HER2- cases. We propose a novel diagnostic algorithm where PTEN expression analysis can be employed to identify pMMR breast cancers.

A HS6ST2 gene variant associated with X-linked intellectual disability and severe myopia in two male twins.

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.

Molecular Insights into the Classification of Luminal Breast Cancers: The Genomic Heterogeneity of Progesterone-Negative Tumors.

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- 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's t-test and the test of Equal or Given Proportions. Survival curves were built according to the Kaplan⁻Meier method; differences in survival were assessed with the log-rank test. A total of 3570 ER+ breast cancers (PR- 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- 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- breast cancers are genetically heterogeneous and encompass a variety of distinct entities in terms of prognostic and predictive information.

LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer.

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.

Robust design of microbial strains.

MOTIVATION: Metabolic engineering algorithms provide means to optimize a biological process leading to the improvement of a biotechnological interesting molecule. Therefore, it is important to understand how to act in a metabolic pathway in order to have the best results in terms of productions. In this work, we present a computational framework that searches for optimal and robust microbial strains that are able to produce target molecules. Our framework performs three tasks: it evaluates the parameter sensitivity of the microbial model, searches for the optimal genetic or fluxes design and finally calculates the robustness of the microbial strains. We are capable to combine the exploration of species, reactions, pathways and knockout parameter spaces with the Pareto-optimality principle. RESULTS: Our framework provides also theoretical and practical guidelines for design automation. The statistical cross comparison of our new optimization procedures, performed with respect to currently widely used algorithms for bacteria (e.g. Escherichia coli) over different multiple functions, reveals good performances over a variety of biotechnological products. AVAILABILITY: http://www.dmi.unict.it/nicosia/pathDesign.html. CONTACT: nicosia@dmi.unict.it or pl219@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pitfalls of whole exome sequencing in undefined clinical conditions with a suspected genetic etiology.

BACKGROUND: 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. OBJECTIVE: To describe candidate variants emerging from trio-WES and possibly associated with the clinical phenotype in clinically heterogeneous conditions. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Identification of sensitive enzymes in the photosynthetic carbon metabolism.

Understanding and optimizing the CO(2) fixation process would allow human beings to address better current energy and biotechnology issues. We focused on modeling the C(3) photosynthetic Carbon metabolism pathway with the aim of identifying the minimal set of enzymes whose biotechnological alteration could allow a functional re-engineering of the pathway. To achieve this result we merged in a single powerful pipe-line Sensitivity Analysis (SA), Single- (SO) and Multi-Objective Optimization (MO), and Robustness Analysis (RA). By using our recently developed multipurpose optimization algorithms (PAO and PMO2) here we extend our work exploring a large combinatorial solution space and most importantly, here we present an important reduction of the problem search space. From the initial number of 23 enzymes we have identified 11 enzymes whose targeting in the C(3) photosynthetic Carbon metabolism would provide about 90% of the overall functional optimization. Both in terms of maximal CO(2) Uptake and minimal Nitrogen consumption, these 11 sensitive enzymes are confirmed to play a key role. Finally we present a RA to confirm our findings.

Novel Pathogenetic Variants in PTHLH and TRPS1 Genes Causing Syndromic Brachydactyly.

Skeletal disorders, including both isolated and syndromic brachydactyly type E, derive from genetic defects affecting the fine tuning of the network of pathways involved in skeletogenesis and growth-plate development. Alterations of different genes of this network may result in overlapping phenotypes, as exemplified by disorders due to the impairment of the parathyroid hormone/parathyroid hormone-related protein pathway, and obtaining a correct diagnosis is sometimes challenging without a genetic confirmation. Five patients with Albright's hereditary osteodystrophy (AHO)-like skeletal malformations without a clear clinical diagnosis were analyzed by whole-exome sequencing (WES) and novel potentially pathogenic variants in parathyroid hormone like hormone (PTHLH) (BDE with short stature [BDE2]) and TRPS1 (tricho-rhino-phalangeal syndrome [TRPS]) were discovered. The pathogenic impact of these variants was confirmed by in vitro functional studies. This study expands the spectrum of genetic defects associated with BDE2 and TRPS and demonstrates the pathogenicity of TRPS1 missense variants located outside both the nuclear localization signal and the GATA ((A/T)GATA(A/G)-binding zinc-containing domain) and Ikaros-like binding domains. Unfortunately, we could not find distinctive phenotypic features that might have led to an earlier clinical diagnosis, further highlighting the high degree of overlap among skeletal syndromes associated with brachydactyly and AHO-like features, and the need for a close interdisciplinary workout in these rare patients. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

A novel de novo DDX3X missense variant in a female with brachycephaly and intellectual disability: a case report.

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.

Analysis of BRCA1 and RAD51C Promoter Methylation in Italian Families at High-Risk of Breast and Ovarian Cancer.

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 (± stdev) at the BRCA1 promoter were 4.3% (± 1.4%) and 4.4% (± 1.4%) in controls and patients, respectively (p > 0.05; t-test); mean methylation levels (± stdev) at the RAD51C promoter were 4.3% (± 0.9%) and 3.7% (± 0.9%) in controls and patients, respectively (p > 0.05; 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.

(Epi)genetic profiling of extraembryonic and postnatal tissues from female monozygotic twins discordant for Beckwith-Wiedemann syndrome.

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.

The Genetic Landscape of Human Glioblastoma and Matched Primary Cancer Stem Cells Reveals Intratumour Similarity and Intertumour Heterogeneity.

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.

Multi-Target Analysis and Design of Mitochondrial Metabolism.

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.

Pareto Optimal Design for Synthetic Biology.

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 mmolh⁻¹gDW⁻¹ (wild type) to 10.869 mmolh⁻¹gDW⁻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 h⁻¹) and +5.19% (1.62 mmolh⁻¹gDW⁻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.

A design automation framework for computational bioenergetics in biological networks.

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.