Publications by authors named "Anirikh Chakrabarti"

21 Publications

  • Page 1 of 1

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions.

Adv Nutr 2022 Jul 1. Epub 2022 Jul 1.

International Life Sciences Institute, European Branch, Brussels, Belgium.

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing inter-individual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from non-human animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.
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http://dx.doi.org/10.1093/advances/nmac075DOI Listing
July 2022

The microbiota-gut-brain axis: pathways to better brain health. Perspectives on what we know, what we need to investigate and how to put knowledge into practice.

Cell Mol Life Sci 2022 Jan 19;79(2):80. Epub 2022 Jan 19.

Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, UK.

The gut and brain link via various metabolic and signalling pathways, each with the potential to influence mental, brain and cognitive health. Over the past decade, the involvement of the gut microbiota in gut-brain communication has become the focus of increased scientific interest, establishing the microbiota-gut-brain axis as a field of research. There is a growing number of association studies exploring the gut microbiota's possible role in memory, learning, anxiety, stress, neurodevelopmental and neurodegenerative disorders. Consequently, attention is now turning to how the microbiota can become the target of nutritional and therapeutic strategies for improved brain health and well-being. However, while such strategies that target the gut microbiota to influence brain health and function are currently under development with varying levels of success, still very little is yet known about the triggers and mechanisms underlying the gut microbiota's apparent influence on cognitive or brain function and most evidence comes from pre-clinical studies rather than well controlled clinical trials/investigations. Filling the knowledge gaps requires establishing a standardised methodology for human studies, including strong guidance for specific focus areas of the microbiota-gut-brain axis, the need for more extensive biological sample analyses, and identification of relevant biomarkers. Other urgent requirements are new advanced models for in vitro and in vivo studies of relevant mechanisms, and a greater focus on omics technologies with supporting bioinformatics resources (training, tools) to efficiently translate study findings, as well as the identification of relevant targets in study populations. The key to building a validated evidence base rely on increasing knowledge sharing and multi-disciplinary collaborations, along with continued public-private funding support. This will allow microbiota-gut-brain axis research to move to its next phase so we can identify realistic opportunities to modulate the microbiota for better brain health.
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http://dx.doi.org/10.1007/s00018-021-04060-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770392PMC
January 2022

Effect of erythritol and xylitol on Streptococcus pyogenes causing peritonsillar abscesses.

Sci Rep 2021 08 4;11(1):15855. Epub 2021 Aug 4.

Department of Microbiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.

Polyols are effective against caries-causing streptococci but the effect on oropharynx-derived pyogenic streptococci is not well characterised. We aimed to study the effect of erythritol (ERY) and xylitol (XYL) against Streptococcus pyogenes isolated from peritonsillar abscesses (PTA). We used 31 clinical isolates and 5 throat culture collection strains. Inhibition of bacterial growth by polyols at 2.5%, 5% and 10% concentrations was studied and the results were scored. Amylase levels in PTA pus were compared to polyol effectivity scores (PES). Growth curves of four S. pyogenes isolates were analysed. Our study showed that XYL was more effective than ERY inhibiting 71-97% and 48-84% of isolates, respectively, depending of concentrations. 48% of clinical and all throat strains were inhibited by polyols in all concentrations (PES 3). PES was negative or zero in 26% of the isolates in the presence of ERY and in 19% of XYL. ERY enhanced the growth of S. pyogenes isolated from pus with high amylase levels. Polyols in all concentrations inhibited the growth in exponential phase. In conclusion, ERY and XYL are potent growth inhibitors of S. pyogenes isolated from PTA. Therefore, ERY and XYL may have potential in preventing PTA in the patients with frequent tonsillitis episodes.
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http://dx.doi.org/10.1038/s41598-021-95367-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339055PMC
August 2021

Exploring Valine Metabolism in Astrocytic and Liver Cells: Lesson from Clinical Observation in TBI Patients for Nutritional Intervention.

Biomedicines 2020 Nov 10;8(11). Epub 2020 Nov 10.

Lipid metabolism, Nestlé Research, Nestlé Institute of Health Sciences,1015 Lausanne, Switzerland.

The utilization of alternative energy substrates to glucose could be beneficial in traumatic brain injury (TBI). Recent clinical data obtained in TBI patients reported valine, β-hydroxyisobutyrate (ibHB) and 2-ketoisovaleric acid (2-KIV) as three of the main predictors of TBI outcome. In particular, higher levels of ibHB, 2-KIV, and valine in cerebral microdialysis (CMD) were associated with better clinical outcome. In this study, we investigate the correlations between circulating and CMD levels of these metabolites. We hypothesized that the liver can metabolize valine and provide a significant amount of intermediate metabolites, which can be further metabolized in the brain. We aimed to assess the metabolism of valine in human-induced pluripotent stem cell (iPSC)-derived astrocytes and HepG2 cells using C-labeled substrate to investigate potential avenues for increasing the levels of downstream metabolites of valine via valine supplementation. We observed that 94 ± 12% and 84 ± 16% of ibHB, and 94 ± 12% and 87 ± 15% of 2-KIV, in the medium of HepG2 cells and in iPSC-derived astrocytes, respectively, came directly from valine. Overall, these findings suggest that both ibHB and 2-KIV are produced from valine to a large extent in both cell types, which could be of interest in the design of optimal nutritional interventions aiming at stimulating valine metabolism.
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http://dx.doi.org/10.3390/biomedicines8110487DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697144PMC
November 2020

Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics.

Sci Rep 2020 06 8;10(1):9236. Epub 2020 Jun 8.

Nestlé Institute of Health Sciences, Lausanne, Switzerland.

Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.
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http://dx.doi.org/10.1038/s41598-020-65936-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280519PMC
June 2020

Exploration of singular and synergistic effect of xylitol and erythritol on causative agents of dental caries.

Sci Rep 2020 04 14;10(1):6297. Epub 2020 Apr 14.

Department of Microbiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.

Non-cariogenic sweet substances, like sugar alcohols, are used to decrease the risk of caries by reducing the growth of dental plaque. The aim of our study was to reveal the impact of xylitol and erythritol on the growth and biofilm formation of cariogenic bacteria including as a novelty, set of clinical mutans streptococci and Scardovia wiggsiae and to assess the possible synergistic influence of these polyols. We found both xylitol and erythritol to express high growth inhibition effect on cariogenic bacteria. In synergistic effect experiments, 10% polyol combination with excess of erythritol was found to be more effective against growth of Streptococcus mutans and the combination with excess of xylitol more effective against growth of Streptococcus sobrinus and S. wiggsiae. In biofilm inhibition experiments, solutions of 10% polyols in different combinations and 15% single polyols were equally effective against mutans streptococci. At the same time, higher biofilm formation of S. wiggsiae compared to experiments without polyols was detected in different polyol concentrations for up to 34%. In conclusion, both erythritol and xylitol as well as their combinations inhibit the growth of different cariogenic bacteria. Biofilm formation of mutans streptococci is also strongly inhibited. When applying polyols in caries prophylaxis, it is relevant to consider that the profile of pathogens in a particular patient may influence the effect of polyols used.
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http://dx.doi.org/10.1038/s41598-020-63153-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156733PMC
April 2020

Impact of multi-micronutrient supplementation on lipidemia of children and adolescents.

Clin Nutr 2020 07 18;39(7):2211-2219. Epub 2019 Oct 18.

Lipid Metabolism, Nestlé Research, EPFL Innovation Park, 1015, Switzerland; Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland. Electronic address:

Background: Micronutrient supplementation has been extensively explored as a strategy to improve health and reduce risk of chronic diseases. Fat-soluble vitamins like A and E with their antioxidant properties and mechanistic interactions with lipoproteins, have potentially a key impact on lipid metabolism and lipidemia.

Objective: The impact of micronutrients on lipid metabolism requires further investigation including characterization of plasma lipidome following supplementation and any cause-effect on circulating lipids.

Design: In this study, we elucidate the effect and associations of a multi-micronutrient intervention in Brazilian children and teens with lipoprotein alterations and lipid metabolism.

Results: Our analysis suggests a combination of short and long-term impact of supplementation on lipid metabolism, potentially mediated primarily by α-tocopherol (vitamin E) and retinol (vitamin A). Among the lipid classes, levels of phospholipids, lysophospholipids, and cholesterol esters were impacted the most along with differential incorporation of stearic, palmitic, oleic and arachidonic acids. Integrated analysis with proteomic data suggested potential links to supplementation-mediated alterations in protein levels of phospholipases and pyruvate dehydrogenase kinase 1 (PDK1).

Conclusions: Associations between the observed differences in lipidemia, total triglyceride, and VLDL-cholesterol levels suggest that micronutrients may play a role in reducing these risk factors for cardiovascular disease in children. This would require further investigation.
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http://dx.doi.org/10.1016/j.clnu.2019.09.010DOI Listing
July 2020

Differential Metabolism of Medium-Chain Fatty Acids in Differentiated Human-Induced Pluripotent Stem Cell-Derived Astrocytes.

Front Physiol 2019 4;10:657. Epub 2019 Jun 4.

Lipid Metabolism, Nestlé Institute of Health Sciences, Lausanne, Switzerland.

Medium-chain triglyceride (MCT) ketogenic diets increase ketone bodies, which are believed to act as alternative energy substrates in the injured brain. Octanoic (C8:0) and decanoic (C10:0) acids, which produce ketone bodies through β-oxidation, are used as part of MCT ketogenic diets. Although the ketogenic role of MCT is well-established, it remains unclear how the network metabolism underlying β-oxidation of these medium-chain fatty acids (MCFA) differ. We aim to elucidate basal β-oxidation of these commonly used MCFA at the cellular level. Human-induced pluripotent stem cell-derived (iPSC) astrocytes were incubated with [U-C]-C8:0 or [U-C]-C10:0, and the fractional enrichments (FE) of the derivatives were used for metabolic flux analysis. Data indicate higher extracellular concentrations and faster secretion rates of β-hydroxybutyrate (βHB) and acetoacetate (AcAc) with C8:0 than C10:0, and an important contribution from unlabeled substrates. Flux analysis indicates opposite direction of metabolic flux between the MCFA intermediates C6:0 and C8:0, with an important contribution of unlabeled sources to the elongation in the C10:0 condition, suggesting different β-oxidation pathways. Finally, larger intracellular glutathione concentrations and secretions of 3-OH-C10:0 and C6:0 were measured in C10:0-treated astrocytes. These findings reveal MCFA-specific ketogenic properties. Our results provide insights into designing different MCT-based ketogenic diets to target specific health benefits.
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http://dx.doi.org/10.3389/fphys.2019.00657DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558201PMC
June 2019

Discovery and validation of temporal patterns involved in human brain ketometabolism in cerebral microdialysis fluids of traumatic brain injury patients.

EBioMedicine 2019 Jun 13;44:607-617. Epub 2019 Jun 13.

Lipid metabolism, Nestlé Research, EPFL Innovation Park, Lausanne, Switzerland; Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland. Electronic address:

Background: Traumatic brain injury (TBI) is recognized as a metabolic disease, characterized by acute cerebral glucose hypo-metabolism. Adaptive metabolic responses to TBI involve the utilization of alternative energy substrates, such as ketone bodies. Cerebral microdialysis (CMD) has evolved as an accurate technique allowing continuous sampling of brain extracellular fluid and assessment of regional cerebral metabolism. We present the successful application of a combined hypothesis- and data-driven metabolomics approach using repeated CMD sampling obtained routinely at patient bedside. Investigating two patient cohorts (n = 26 and n = 12), we identified clinically relevant metabolic patterns at the acute post-TBI critical care phase.

Methods: Clinical and CMD metabolomics data were integrated and analysed using in silico and data modelling approaches. We used both unsupervised and supervised multivariate analysis techniques to investigate structures within the time series and associations with patient outcome.

Findings: The multivariate metabolite time series exhibited two characteristic brain metabolic states that were attributed to changes in key metabolites: valine, 4-methyl-2-oxovaleric acid (4-MOV), isobeta-hydroxybutyrate (iso-bHB), tyrosyine, and 2-ketoisovaleric acid (2-KIV). These identified cerebral metabolic states differed significantly with respect to standard clinical values. We validated our findings in a second cohort using a classification model trained on the cerebral metabolic states. We demonstrated that short-term (therapeutic intensity level (TIL)) and mid-term patient outcome (6-month Glasgow Outcome Score (GOS)) can be predicted from the time series characteristics.

Interpretation: We identified two specific cerebral metabolic patterns that are closely linked to ketometabolism and were associated with both TIL and GOS. Our findings support the view that advanced metabolomics approaches combined with CMD may be applied in real-time to predict short-term treatment intensity and long-term patient outcome.
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http://dx.doi.org/10.1016/j.ebiom.2019.05.054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6606955PMC
June 2019

Transcriptomics-driven lipidomics (TDL) identifies the microbiome-regulated targets of ileal lipid metabolism.

NPJ Syst Biol Appl 2017 7;3:33. Epub 2017 Nov 7.

Nestlé Institute of Health Sciences SA, Campus EPFL, Quartier de l'Innovation, Bâtiment H, 1015 Lausanne, Switzerland.

The gut microbiome and lipid metabolism are both recognized as essential components in the maintenance of metabolic health. The mechanisms involved are multifactorial and (especially for microbiome) poorly defined. A strategic approach to investigate the complexity of the microbial influence on lipid metabolism would facilitate determination of relevant molecular mechanisms for microbiome-targeted therapeutics. is associated with obesity and metabolic syndrome and we used this association in conjunction with gnotobiotic models to investigate the impact of on lipid metabolism. To address the complexities of the integration of the microbiome and lipid metabolism, we developed transcriptomics-driven lipidomics (TDL) to predict the impact of colonization on lipid metabolism and established mediators of inflammation and insulin resistance including arachidonic acid metabolism, alterations in bile acids and dietary lipid absorption. A microbiome-related therapeutic approach targeting these mechanisms may therefore provide a therapeutic avenue supporting maintenance of metabolic health.
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http://dx.doi.org/10.1038/s41540-017-0033-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5676686PMC
November 2017

Aging and sarcopenia associate with specific interactions between gut microbes, serum biomarkers and host physiology in rats.

Aging (Albany NY) 2017 07;9(7):1698-1720

Nestlé Institute of Health Sciences SA, EPFL Innovation Park, Lausanne 1015, Switzerland.

The microbiome has been demonstrated to play an integral role in the maintenance of many aspects of health that are also associated with aging. In order to identify areas of potential exploration and intervention, we simultaneously characterized age-related alterations in gut microbiome, muscle physiology and serum proteomic and lipidomic profiles in aged rats to define an integrated signature of the aging phenotype. We demonstrate that aging skews the composition of the gut microbiome, in particular by altering the Sutterella to Barneseilla ratio, and alters the metabolic potential of intestinal bacteria. Age-related changes of the gut microbiome were associated with the physiological decline of musculoskeletal function, and with molecular markers of nutrient processing/availability, and inflammatory/immune status in aged versus adult rats. Altogether, our study highlights that aging leads to a complex interplay between the microbiome and host physiology, and provides candidate microbial species to target physical and metabolic decline during aging by modulating gut microbial ecology.
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http://dx.doi.org/10.18632/aging.101262DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559170PMC
July 2017

Complete Genome Sequence of Strain M8, Isolated from Mice.

Genome Announc 2017 Jun 1;5(22). Epub 2017 Jun 1.

Gastrointestinal Health and Microbiome, Nestlé Institute of Health Sciences, Lausanne, Vaud, Switzerland

is one of the common inhabitants of the mammalian gastrointestinal track. We isolated a strain from an mouse and performed whole-genome sequencing, which yielded a chromosome of ~5.1 Mb and three plasmids of ~160 kb, ~6 kb, and ~4 kb.
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http://dx.doi.org/10.1128/genomeA.00449-17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5454205PMC
June 2017

Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1.

PLoS Comput Biol 2016 Dec 27;12(12):e1005251. Epub 2016 Dec 27.

Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.

Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor β (TGF-β) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-β induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-β exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-β and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-β and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-β treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.
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http://dx.doi.org/10.1371/journal.pcbi.1005251DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5189931PMC
December 2016

Resolving microbial membership using Abundance and Variability In Taxonomy ('AVIT ).

Sci Rep 2016 08 17;6:31655. Epub 2016 Aug 17.

Nestlé Institute of Health Sciences, Lausanne, Switzerland.

Development of NGS has revolutionized the analysis in microbial ecology contributing to our deeper understanding of microbiota in health and disease. However, the quality, quantity and confidence of summarized taxonomic abundances are in need of further scrutiny due to sample dependent and independent effects. In this article we introduce 'AVIT (Abundance and Variability In Taxonomy), an unbiased method to enrich for assigned members of microbial communities. As opposed to using a priori thresholds, 'AVIT uses inherent abundance and variability of taxa in a dataset to determine the inclusion or rejection of each taxa for further downstream analysis. Using in-vitro and in-vivo studies, we benchmarked performance and parameterized 'AVIT to establish a framework for investigating the dynamic range of microbial community membership in clinically relevant scenarios.
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http://dx.doi.org/10.1038/srep31655DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987704PMC
August 2016

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models.

Metab Eng 2016 May 5;35:148-159. Epub 2016 Feb 5.

Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. Electronic address:

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.
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http://dx.doi.org/10.1016/j.ymben.2016.01.009DOI Listing
May 2016

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints.

Biotechnol J 2013 Sep 20;8(9):1043-57. Epub 2013 Aug 20.

Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Swiss Institute of Bioinformatics, Switzerland.

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.
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http://dx.doi.org/10.1002/biot.201300091DOI Listing
September 2013

Physicochemical regulation of endothelial sprouting in a 3D microfluidic angiogenesis model.

J Biomed Mater Res A 2013 Oct 5;101(10):2948-56. Epub 2013 Apr 5.

Department of Biomedical Engineering, Cornell University, Ithaca, New York.

Both physiological and pathological tissue remodeling (e.g., during wound healing and cancer, respectively) require new blood vessel formation via angiogenesis, but the underlying microenvironmental mechanisms remain poorly defined due in part to the lack of biologically relevant in vitro models. Here, we present a biomaterials-based microfluidic 3D platform for analysis of endothelial sprouting in response to morphogen gradients. This system consists of three lithographically defined channels embedded in type I collagen hydrogels. A central channel is coated with endothelial cells, and two parallel side channels serve as a source and a sink for the steady-state generation of biochemical gradients. Gradients of vascular endothelial growth factor (VEGF) promoted sprouting, whereby endothelial cell responsiveness was markedly dependent on cell density and vessel geometry regardless of treatment conditions. These results point toward mechanical and/or autocrine mechanisms that may overwhelm pro-angiogenic paracrine signaling under certain conditions. To date, neither geometrical effects nor cell density have been considered critical determinants of angiogenesis in health and disease. This biomimetic vessel platform demonstrated utility for delineating hitherto underappreciated contributors of angiogenesis, and future studies may enable important new mechanistic insights that will inform anti-angiogenic cancer therapy.
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http://dx.doi.org/10.1002/jbm.a.34587DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3776016PMC
October 2013

Multiscale models of breast cancer progression.

Ann Biomed Eng 2012 Nov 25;40(11):2488-500. Epub 2012 Sep 25.

School of Chemical and Biomolecular Engineering, 244 Olin Hall, Cornell University, Ithaca, NY 14853, USA.

Breast cancer initiation, invasion and metastasis span multiple length and time scales. Molecular events at short length scales lead to an initial tumorigenic population, which left unchecked by immune action, acts at increasingly longer length scales until eventually the cancer cells escape from the primary tumor site. This series of events is highly complex, involving multiple cell types interacting with (and shaping) the microenvironment. Multiscale mathematical models have emerged as a powerful tool to quantitatively integrate the convective-diffusion-reaction processes occurring on the systemic scale, with the molecular signaling processes occurring on the cellular and subcellular scales. In this study, we reviewed the current state of the art in cancer modeling across multiple length scales, with an emphasis on the integration of intracellular signal transduction models with pro-tumorigenic chemical and mechanical microenvironmental cues. First, we reviewed the underlying biomolecular origin of breast cancer, with a special emphasis on angiogenesis. Then, we summarized the development of tissue engineering platforms which could provide high-fidelity ex vivo experimental models to identify and validate multiscale simulations. Lastly, we reviewed top-down and bottom-up multiscale strategies that integrate subcellular networks with the microenvironment. We present models of a variety of cancers, in addition to breast cancer specific models. Taken together, we expect as the sophistication of the simulations increase, that multiscale modeling and bottom-up agent-based models in particular will become an increasingly important platform technology for basic scientific discovery, as well as the identification and validation of potentially novel therapeutic targets.
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http://dx.doi.org/10.1007/s10439-012-0655-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3868441PMC
November 2012

Computational modeling and analysis of insulin induced eukaryotic translation initiation.

PLoS Comput Biol 2011 Nov 10;7(11):e1002263. Epub 2011 Nov 10.

School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America.

Insulin, the primary hormone regulating the level of glucose in the bloodstream, modulates a variety of cellular and enzymatic processes in normal and diseased cells. Insulin signals are processed by a complex network of biochemical interactions which ultimately induce gene expression programs or other processes such as translation initiation. Surprisingly, despite the wealth of literature on insulin signaling, the relative importance of the components linking insulin with translation initiation remains unclear. We addressed this question by developing and interrogating a family of mathematical models of insulin induced translation initiation. The insulin network was modeled using mass-action kinetics within an ordinary differential equation (ODE) framework. A family of model parameters was estimated, starting from an initial best fit parameter set, using 24 experimental data sets taken from literature. The residual between model simulations and each of the experimental constraints were simultaneously minimized using multiobjective optimization. Interrogation of the model population, using sensitivity and robustness analysis, identified an insulin-dependent switch that controlled translation initiation. Our analysis suggested that without insulin, a balance between the pro-initiation activity of the GTP-binding protein Rheb and anti-initiation activity of PTEN controlled basal initiation. On the other hand, in the presence of insulin a combination of PI3K and Rheb activity controlled inducible initiation, where PI3K was only critical in the presence of insulin. Other well known regulatory mechanisms governing insulin action, for example IRS-1 negative feedback, modulated the relative importance of PI3K and Rheb but did not fundamentally change the signal flow.
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http://dx.doi.org/10.1371/journal.pcbi.1002263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3213178PMC
November 2011

A review of the mammalian unfolded protein response.

Biotechnol Bioeng 2011 Dec 9;108(12):2777-93. Epub 2011 Aug 9.

School of Chemical and Biomolecular Engineering, Cornell University, 244 Olin Hall, Ithaca, New York 14853, USA.

Proteins requiring post-translational modifications such as N-linked glycosylation are processed in the endoplasmic reticulum (ER). A diverse array of cellular stresses can lead to dysfunction of the ER and ultimately to an imbalance between protein-folding capacity and protein-folding load. Cells monitor protein folding by an inbuilt quality control system involving both the ER and the Golgi apparatus. Unfolded or misfolded proteins are tagged for degradation via ER-associated degradation (ERAD) or sent back through the folding cycle. Continued accumulation of incorrectly folded proteins can also trigger the unfolded protein response (UPR). In mammalian cells, UPR is a complex signaling program mediated by three ER transmembrane receptors: activating transcription factor 6 (ATF6), inositol requiring kinase 1 (IRE1) and double-stranded RNA-activated protein kinase (PKR)-like endoplasmic reticulum kinase (PERK). UPR performs three functions, adaptation, alarm, and apoptosis. During adaptation, the UPR tries to reestablish folding homeostasis by inducing the expression of chaperones that enhance protein folding. Simultaneously, global translation is attenuated to reduce the ER folding load while the degradation rate of unfolded proteins is increased. If these steps fail, the UPR induces a cellular alarm and mitochondrial mediated apoptosis program. UPR malfunctions have been associated with a wide range of disease states including tumor progression, diabetes, as well as immune and inflammatory disorders. This review describes recent advances in understanding the molecular structure of UPR in mammalian cells, its functional role in cellular stress, and its pathophysiology.
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http://dx.doi.org/10.1002/bit.23282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3193940PMC
December 2011

Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).

Biotechnol J 2010 Jul;5(7):768-80

School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA.

Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.
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http://dx.doi.org/10.1002/biot.201000059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3021968PMC
July 2010
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