Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

REGULATION OF SHAPE DYNAMICS AND ACTIN POLYMERIZATION DURING COLLECTIVE CELL MIGRATION

This thesis aims to understand how cells coordinate their motion during collective migration. As previously shown, the motion of individually migrating cells is governed by wave-like cell shape dynamics. The mechanisms that regulate these dynamic behaviors in response to extracellular environment remain largely unclear. I applied shape dynamics analysis to Dictyostelium cells migrating in pairs and in multicellular streams and found that wave-like membrane protrusions are highly coupled between touching cells. I further characterized cell motion by using principle component analysis (PCA) to decompose complex cell shape changes into a serial shape change modes, from which I found that streaming cells exhibit localized anterior protrusion, termed front narrowing, to facilitate cell-cell coupling. I next explored cytoskeleton-based mechanisms of cell-cell coupling by measuring the dynamics of actin polymerization. Actin polymerization waves observed in individual cells were significantly suppressed in multicellular streams. Streaming cells exclusively produced F-actin at cell-cell contact regions, especially at cell fronts. I demonstrated that such restricted actin polymerization is associated with cell-cell coupling, as reducing actin polymerization with Latrunculin A leads to the assembly of F-actin at the side of streams, the decrease of front narrowing, and the decoupling of protrusion waves. My studies also suggest that collective migration is guided by cell-surface interactions. I examined the aggregation of Dictyostelim cells under distinct conditions and found that both chemical compositions of surfaces and surface-adhesion defects in cells result in altered collective migration patterns. I also investigated the shape dynamics of cells suspended on PEG-coated surfaces, which showed that coupling of protrusion waves disappears on touching suspended cells. These observations indicate that collective migration requires a balance between cell-cell and cell-surface adhesions. I hypothesized such a balance is reached via the regulation of cytoskeleton. Indeed, I found cells actively regulate cytoskeleton to retain optimal cell-surface adhesions on varying surfaces, and cells lacking the link between actin and surfaces (talin A) could not retain the optimal adhesions. On the other hand, suspended cells exhibited enhanced actin filament assembly on the periphery of cell groups instead of in cell-cell contact regions, which facilitates their aggregation in a clumping fashion.

Synthetic Circuits for Feedback and Detection in Bacteria

Synthetic biology, by co-opting molecular machinery from existing organisms, can be used as a tool for building new genetic systems from scratch, for understanding natural networks through perturbation, or for hybrid circuits that piggy-back on existing cellular infrastructure. Although the toolbox for genetic circuits has greatly expanded in recent years, it is still difficult to separate the circuit function from its specific molecular implementation. In this thesis, we discuss the function-driven design of two synthetic circuit modules, and use mathematical models to understand the fundamental limits of circuit topology versus operating regimes as determined by the specific molecular implementation. First, we describe a protein concentration tracker circuit that sets the concentration of an output protein relative to the concentration of a reference protein. The functionality of this circuit relies on a single negative feedback loop that is implemented via small programmable protein scaffold domains. We build a mass-action model to understand the relevant timescales of the tracking behavior and how the input/output ratios and circuit gain might be tuned with circuit components. Second, we design an event detector circuit with permanent genetic memory that can record order and timing between two chemical events. This circuit was implemented using bacteriophage integrases that recombine specific segments of DNA in response to chemical inputs. We simulate expected population-level outcomes using a stochastic Markov-chain model, and investigate how inferences on past events can be made from differences between single-cell and population-level responses. Additionally, we present some preliminary investigations on spatial patterning using the event detector circuit as well as the design of stationary phase promoters for growth-phase dependent activation. These results advance our understanding of synthetic gene circuits, and contribute towards the use of circuit modules as building blocks for larger and more complex synthetic networks.

Crystal structure of Bn IV in complex with myristic acid: A Lys49 myotoxic phospholipase A(2) from Bothrops neuwiedi venom

The LY549-PLA(2)s myotoxins have attracted attention as models for the induction of myonecrosis by a catalytically independent mechanism of action. Structural studies and biological activities have demonstrated that the myotoxic activity of LYS49-PLA(2) is independent of the catalytic activity site. The myotoxic effect is conventionally thought to be to due to the C-terminal region 111-121, which plays an effective role in membrane damage. In the present study, Bn IV LYS49-PLA(2) was isolated from Bothrops neuwiedi snake venom in complex with myristic acid (CH3(CH2)(12)COOH) and its overall structure was refined at 2.2 angstrom resolution. The Bn IV crystals belong to monoclinic space group P2(1) and contain a dimer in the asymmetric unit. The unit cell parameters are a = 38.8, b = 70.4, c = 44.0 angstrom. The biological assembly is a "conventional dimer" and the results confirm that dimer formation is not relevant to the myotoxic activity. Electron density map analysis of the Bn IV structure shows clearly the presence of myristic acid in catalytic site. The relevant structural features for myotoxic activity are located in the C-terminal region and the Bn IV C-terminal residues NKKYRY are a probable heparin binding domain. These findings indicate that the mechanism of interaction between Bn IV and muscle cell membranes is through some kind of cell signal transduction mediated by heparin complexes. (C) 2010 Elsevier Masson SAS. All rights reserved.

WATER TRANSPORT THROUGH POROUS MEDIA AND IN FLOW CHANNELS OF PROTON EXCHANGE MEMBRANE FUEL CELL

Proton exchange membrane (PEM) fuel cell has been known as a promising power source for different applications such as automotive, residential and stationary. During the operation of a PEM fuel cell, hydrogen is oxidized in anode and oxygen is reduced in the cathode to produce the intended power. Water and heat are inevitable byproducts of these reactions. The water produced in the cathode should be properly removed from inside the cell. Otherwise, it may block the path of reactants passing through the gas channels and/or gas diffusion layer (GDL). This deteriorates the performance of the cell and eventually can cease the operation of the cell. Water transport in PEM fuel cell has been the subject of this PhD study. Water transport on the surface of the GDL, through the gas flow channels, and through GDL has been studied in details. For water transport on the surface of the GDL, droplet detachment has been measured for different GDL conditions and for anode and cathode gas flow channels. Water transport through gas flow channels has been investigated by measuring the two-phase flow pressure drop along the gas flow channels. As accumulated liquid water within gas flow channels resists the gas flow, the pressure drop increases along the flow channels. The two-phase flow pressure drop can reveal useful information about the amount of liquid water accumulated within gas flow channels. Liquid water transport though GDL has also been investigated by measuring the liquid water breakthrough pressure for the region between the capillary fingering and the stable displacement on the drainage phase diagram. The breakthrough pressure has been measured for different variables such as GDL thickness, PTFE/Nafion content within the GDL, GDL compression, the inclusion of a micro-porous layer (MPL), and different water flow rates through the GDL. Prior to all these studies, GDL microstructural properties have been studied. GDL microstructural properties such as mean pore diameter, pore diameter distribution, and pore roundness distribution have been investigated by analyzing SEM images of GDL samples.

Understanding ten-eleven translocation-2 in hematological and nervous systems

I proposed the study of two distinct aspects of Ten-Eleven Translocation 2 (TET2) protein for understanding specific functions in different body systems. ^ In Part I, I characterized the molecular mechanisms of Tet2 in the hematological system. As the second member of Ten-Eleven Translocation protein family, TET2 is frequently mutated in leukemic patients. Previous studies have shown that the TET2 mutations frequently occur in 20% myelodysplastic syndrome/myeloproliferative neoplasm (MDS/MPN), 10% T-cell lymphoma leukemia and 2% B-cell lymphoma leukemia. Genetic mouse models also display distinct phenotypes of various types of hematological malignancies. I performed 5-hydroxymethylcytosine (5hmC) chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq) of hematopoietic stem/progenitor cells to determine whether the deletion of Tet2 can affect the abundance of 5hmC at myeloid, T-cell and B-cell specific gene transcription start sites, which ultimately result in various hematological malignancies. Subsequent Exome sequencing (Exome-Seq) showed that disease-specific genes are mutated in different types of tumors, which suggests that TET2 may protect the genome from being mutated. The direct interaction between TET2 and Mutator S Homolog 6 (MSH6) protein suggests TET2 is involved in DNA mismatch repair. Finally, in vivo mismatch repair studies show that the loss of Tet2 causes a mutator phenotype. Taken together, my data indicate that TET2 binds to MSH6 to protect genome integrity. ^ In Part II, I intended to better understand the role of Tet2 in the nervous system. 5-hydroxymethylcytosine regulates epigenetic modification during neurodevelopment and aging. Thus, Tet2 may play a critical role in regulating adult neurogenesis. To examine the physiological significance of Tet2 in the nervous system, I first showed that the deletion of Tet2 reduces the 5hmC levels in neural stem cells. Mice lacking Tet2 show abnormal hippocampal neurogenesis along with 5hmC alternations at different gene promoters and corresponding gene expression downregulation. Through the luciferase reporter assay, two neural factors Neurogenic differentiation 1 (NeuroD1) and Glial fibrillary acidic protein (Gfap) were down-regulated in Tet2 knockout cells. My results suggest that Tet2 regulates neural stem/progenitor cell proliferation and differentiation in adult brain.^

An empirical study of the recreationalexpressive and referential roles of the cell phone among young students and their potential applications to advertising

The cell phone has become a means of finding persuasive content, making friends and accessing information. For this reason it is of great interest in analyzing is use by young people during their formative years. Regarding this point, the objectives of the research are: to obtain percentage data on the distraction call phones cause among students; to define their habits re fun activities and their study responsibility in their personal and academic lives; and to identify leisure-expressive and referential trends. It was decided to analyze the Spanish discourse found on Twitter. To do so, a quantitative and qualitative methodology was used, with a linguistic pattern extraction tool which allows us to obtain categories of meaning. The sample was 10,000 tweets in Spanish, with key words such as “cell” and “study” and all possible derivatives. The data were gathered between 30 May and the 6 June 2014, which coincides with the start of the exam period in Spain. Finally, the potential applications of the research to the specific field of publicity and advertising is discussed as a possible solution to the current needs of the brands, which have to find participative formats taking into account the students’ leisure trends.

Progesterone attenuates microglial-driven retinal degeneration and stimulates protective Fractalkine-CX3CR1 signaling

Retinitis pigmentosa (RP) is a degenerative disease leading to photoreceptor cell loss. Mouse models of RP, such as the rd10 mouse (B6.CXBl-Pde6brd10/J), have enhanced our understanding of the disease, allowing for development of potential therapeutics. In 2011, our group first demonstrated that the synthetic progesterone analogue ‘Norgestrel’ is neuroprotective in two mouse models of retinal degeneration, including the rd10 mouse. We have since elucidated several mechanisms by which Norgestrel protects stressed photoreceptors, such as upregulating growth factors. This study consequently aimed to further characterize Norgestrel’s neuroprotective effects. Specifically, we sought to investigate the role that microglia might play; for microglial-derived inflammation has been shown to potentiate neurodegeneration. Dams of post-natal day (P) 10 rd10 pups were given a Norgestrel-supplemented diet (80mg/kg). Upon weaning, pups remained on Norgestrel. Tissue was harvested from P15-P50 rd10 mice on control or Norgestrel-supplemented diet. Norgestrel-diet administration provided significant retinal protection out to P40 in rd10 mice. Alterations in microglial activity coincided with significant protection, implicating microglial changes in Norgestrel-induced neuroprotection. Utilizing primary cultures of retinal microglia and 661W photoreceptor-like cells, we show that rd10 microglia drive neuronal cell death. We reveal a novel role of Norgestrel, acting directly on microglia to reduce pro-inflammatory activation and prevent neuronal cell death. Norgestrel effectively suppresses cytokine, chemokine and danger-associated molecular pattern molecule (DAMP) expression in the rd10 retina. Remarkably, Norgestrel upregulates fractalkine-CX3CR1 signaling 1 000-fold at the RNA level, in the rd10 mouse. Fractalkine-CX3CR1 signaling has been shown to protect neurons by regulating retinal microglial activation and migration. Ultimately, these results present Norgestrel as a promising treatment for RP, with dual actions as a neuroprotective and anti-inflammatory agent in the retina.

Hilbert Space Valued Signal Plus Noise Models: Analysis of Structural Breaks under High Dimensionality and Temporal Dependence

This thesis is concerned with change point analysis for time series, i.e. with detection of structural breaks in time-ordered, random data. This long-standing research field regained popularity over the last few years and is still undergoing, as statistical analysis in general, a transformation to high-dimensional problems. We focus on the fundamental »change in the mean« problem and provide extensions of the classical non-parametric Darling-Erdős-type cumulative sum (CUSUM) testing and estimation theory within highdimensional Hilbert space settings. In the first part we contribute to (long run) principal component based testing methods for Hilbert space valued time series under a rather broad (abrupt, epidemic, gradual, multiple) change setting and under dependence. For the dependence structure we consider either traditional m-dependence assumptions or more recently developed m-approximability conditions which cover, e.g., MA, AR and ARCH models. We derive Gumbel and Brownian bridge type approximations of the distribution of the test statistic under the null hypothesis of no change and consistency conditions under the alternative. A new formulation of the test statistic using projections on subspaces allows us to simplify the standard proof techniques and to weaken common assumptions on the covariance structure. Furthermore, we propose to adjust the principal components by an implicit estimation of a (possible) change direction. This approach adds flexibility to projection based methods, weakens typical technical conditions and provides better consistency properties under the alternative. In the second part we contribute to estimation methods for common changes in the means of panels of Hilbert space valued time series. We analyze weighted CUSUM estimates within a recently proposed »high-dimensional low sample size (HDLSS)« framework, where the sample size is fixed but the number of panels increases. We derive sharp conditions on »pointwise asymptotic accuracy« or »uniform asymptotic accuracy« of those estimates in terms of the weighting function. Particularly, we prove that a covariance-based correction of Darling-Erdős-type CUSUM estimates is required to guarantee uniform asymptotic accuracy under moderate dependence conditions within panels and that these conditions are fulfilled, e.g., by any MA(1) time series. As a counterexample we show that for AR(1) time series, close to the non-stationary case, the dependence is too strong and uniform asymptotic accuracy cannot be ensured. Finally, we conduct simulations to demonstrate that our results are practically applicable and that our methodological suggestions are advantageous.

The impact of phosphoinositide metabolic enzymes in glioblastoma: a tale of phosphoinositide-specific phospholipase C PLCβ1 and 5-phosphatase SKIP

Background: Glioblastoma multiforme (GBM) is one of the deadliest and most aggressive form of primary brain tumor. Unfortunately, current GBM treatment therapies are not effective in treating GBM patients. They usually experience very poor prognosis with a median survival of approximately 12 months. Only 3-5% survive up to 3 years or more. A large-scale gene profile study revealed that several genes involved in essential cellular processes are altered in GBM, thus, explaining why existing therapies are not effective. The survival of GBM patients depends on understanding the molecular and key signaling events associated with these altered physiological processes in GBM. Phosphoinositides (PI) form just a tiny fraction of the total lipid content in humans, however they are implicated in almost all essential biological processes, such as acting as second messengers in spatio-temporal regulation of cell signaling, cytoskeletal reorganization, cell adhesion, migration, apoptosis, vesicular trafficking, differentiation, cell cycle and post-translational modifications. Interestingly, these essential processes are altered in GBM. More importantly, incoming reports have associated PI metabolism, which is mediated by several PI phosphatases such as SKIP, lipases such as PLCβ1, and other kinases, to regulate GBM associated cellular processes. Even as PLCβ1 and SKIP are involved in regulating aberrant cellular processes in several other cancers, very few studies, of which majority are in-silico-based, have focused on the impact of PLCβ1 and SKIP in GBM. Hence, it is important to employ clinical, in vitro, and in vivo GBM models to define the actual impact of PLCβ1 and SKIP in GBM. AIM: Since studies of PLCβ1 and SKIP in GBM are limited, this study aimed at determining the pathological impact of PI metabolic enzymes, PLCB1 and SKIP, in GBM patient samples, GBM cell line models, and xenograft models for SKIP. Results: For the first time, this study confirmed through qPCR that PLCβ1 gene expression is lower in human GBM patient samples. Moreover, PLCβ1 gene expression inversely correlates with pathological grades of glioma; it decreases as glioma grades increases or worsens. Silencing PLCβ1 in U87MG GBM cells produces a dual impact in GBM by participating in both pro-tumoral and anti-tumoral roles. PLCβ1 knockdown cells were observed to have more migratory abilities, increased cell to extracellular matrix (ECM) adhesion, transition from epithelial phenotype to mesenchymal phenotype through the upregulation of EMT transcription factors Twist1 and Slug, and mesenchymal marker, vimentin. On the other hand, p-Akt and p-mTOR protein expression were downregulated in PLCβ1 knockdown cells. Thus, the oncogenic pathway PI3K/Akt/mTOR pathway is inhibited during PLCβ1 knockdown. Consistently, cell viability in PLCβ1 knockdown cells were significantly decreased compared to controls. As for SKIP, this study demonstrated that about 48% of SKIP colocalizes with nuclear PtdIns(4,5)P2 to nuclear speckles and that SKIP knockdown alters nuclear PtdIns(4,5)P2 in a cell-type dependent manner. In addition, SKIP silencing increased tumor volume and weight in xenografts than controls by reducing apoptosis and increasing viability. All in all, these data confirm that PLCβ1 and SKIP are involved in GBM pathology and a complete understanding of their roles in GBM may be beneficial.

Virtual Calibration of CO2 and Pollutant Emissions of a High-Performance PHEV using Model-in-the-Loop Methodology

The study analyses the calibration process of a newly developed high-performance plug-in hybrid electric passenger car powertrain. The complexity of modern powertrains and the more and more restrictive regulations regarding pollutant emissions are the primary challenges for the calibration of a vehicle’s powertrain. In addition, the managers of OEM need to know as earlier as possible if the vehicle under development will meet the target technical features (emission included). This leads to the necessity for advanced calibration methodologies, in order to keep the development of the powertrain robust, time and cost effective. The suggested solution is the virtual calibration, that allows the tuning of control functions of a powertrain before having it built. The aim of this study is to calibrate virtually the hybrid control unit functions in order to optimize the pollutant emissions and the fuel consumption. Starting from the model of the conventional vehicle, the powertrain is then hybridized and integrated with emissions and aftertreatments models. After its validation, the hybrid control unit strategies are optimized using the Model-in-the-Loop testing methodology. The calibration activities will proceed thanks to the implementation of a Hardware-in-the-Loop environment, that will allow to test and calibrate the Engine and Transmission control units effectively, besides in a time and cost saving manner.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.

Life cycle assessment (LCA) of a bio-fuel cell fed with waste biomass: potential for scale-up and process optimization

In this Thesis, a life cycle analysis (LCA) of a biofuel cell designed by a team from the University of Bologna was done. The purpose of this study is to investigate the possible environmental impacts of the production and use of the cell and a possible optimization for an industrial scale-up. To do so, a first part of the paper was devoted to studying the present literature on biomass, and fuel cell treatments and then LCA studies on them. The experimental part presents the work done to create the Life Cycle Inventory and Life Cycle Impact Assessment. Several alternative scenarios were created to study process optimization. Reagents and energy supply were changed. To examine whether this technology can be competitive, a comparison was made with some biofuel cell use scenarios with traditional biomass treatment technologies. The result of this study is that this technology is promising from an environmental point of view in case it is possible to recover nutrients in output, without excessive energy consumption, and to minimize the use of energy used to prepare the solution.

Obesity And Inflammation And The Effect On The Hematopoietic System.

Bone marrow is organized in specialized microenvironments known as 'marrow niches'. These are important for the maintenance of stem cells and their hematopoietic progenitors whose homeostasis also depends on other cell types present in the tissue. Extrinsic factors, such as infection and inflammatory states, may affect this system by causing cytokine dysregulation (imbalance in cytokine production) and changes in cell proliferation and self-renewal rates, and may also induce changes in the metabolism and cell cycle. Known to relate to chronic inflammation, obesity is responsible for systemic changes that are best studied in the cardiovascular system. Little is known regarding the changes in the hematopoietic system induced by the inflammatory state carried by obesity or the cell and molecular mechanisms involved. The understanding of the biological behavior of hematopoietic stem cells under obesity-induced chronic inflammation could help elucidate the pathophysiological mechanisms involved in other inflammatory processes, such as neoplastic diseases and bone marrow failure syndromes.

Carbon Nanoparticles For Gene Transfection In Eukaryotic Cell Lines.

For the first time, oxygen terminated cellulose carbon nanoparticles (CCN) was synthesised and applied in gene transfection of pIRES plasmid. The CCN was prepared from catalytic of polyaniline by chemical vapour deposition techniques. This plasmid contains one gene that encodes the green fluorescent protein (GFP) in eukaryotic cells, making them fluorescent. This new nanomaterial and pIRES plasmid formed π-stacking when dispersed in water by magnetic stirring. The frequencies shift in zeta potential confirmed the plasmid strongly connects to the nanomaterial. In vitro tests found that this conjugation was phagocytised by NG97, NIH-3T3 and A549 cell lines making them fluorescent, which was visualised by fluorescent microscopy. Before the transfection test, we studied CCN in cell viability. Both MTT and Neutral Red uptake tests were carried out using NG97, NIH-3T3 and A549 cell lines. Further, we use metabolomics to verify if small amounts of nanomaterial would be enough to cause some cellular damage in NG97 cells. We showed two mechanisms of action by CCN-DNA complex, producing an exogenous protein by the transfected cell and metabolomic changes that contributed by better understanding of glioblastoma, being the major finding of this work. Our results suggested that this nanomaterial has great potential as a gene carrier agent in non-viral based therapy, with low cytotoxicity, good transfection efficiency, and low cell damage in small amounts of nanomaterials in metabolomic tests.