Mathematical study of complex networks : brain, internet, and power grid

The dissertation is concerned with the mathematical study of various network problems. First, three real-world networks are considered: (i) the human brain network (ii) communication networks, (iii) electric power networks. Although these networks perform very different tasks, they share similar mathematical foundations. The high-level goal is to analyze and/or synthesis each of these systems from a “control and optimization” point of view. After studying these three real-world networks, two abstract network problems are also explored, which are motivated by power systems. The first one is “flow optimization over a flow network” and the second one is “nonlinear optimization over a generalized weighted graph”. The results derived in this dissertation are summarized below.

Brain Networks: Neuroimaging data reveals the coordinated activity of spatially distinct brain regions, which may be represented mathematically as a network of nodes (brain regions) and links (interdependencies). To obtain the brain connectivity network, the graphs associated with the correlation matrix and the inverse covariance matrix—describing marginal and conditional dependencies between brain regions—have been proposed in the literature. A question arises as to whether any of these graphs provides useful information about the brain connectivity. Due to the electrical properties of the brain, this problem will be investigated in the context of electrical circuits. First, we consider an electric circuit model and show that the inverse covariance matrix of the node voltages reveals the topology of the circuit. Second, we study the problem of finding the topology of the circuit based on only measurement. In this case, by assuming that the circuit is hidden inside a black box and only the nodal signals are available for measurement, the aim is to find the topology of the circuit when a limited number of samples are available. For this purpose, we deploy the graphical lasso technique to estimate a sparse inverse covariance matrix. It is shown that the graphical lasso may find most of the circuit topology if the exact covariance matrix is well-conditioned. However, it may fail to work well when this matrix is ill-conditioned. To deal with ill-conditioned matrices, we propose a small modification to the graphical lasso algorithm and demonstrate its performance. Finally, the technique developed in this work will be applied to the resting-state fMRI data of a number of healthy subjects.

Communication Networks: Congestion control techniques aim to adjust the transmission rates of competing users in the Internet in such a way that the network resources are shared efficiently. Despite the progress in the analysis and synthesis of the Internet congestion control, almost all existing fluid models of congestion control assume that every link in the path of a flow observes the original source rate. To address this issue, a more accurate model is derived in this work for the behavior of the network under an arbitrary congestion controller, which takes into account of the effect of buffering (queueing) on data flows. Using this model, it is proved that the well-known Internet congestion control algorithms may no longer be stable for the common pricing schemes, unless a sufficient condition is satisfied. It is also shown that these algorithms are guaranteed to be stable if a new pricing mechanism is used.

Electrical Power Networks: Optimal power flow (OPF) has been one of the most studied problems for power systems since its introduction by Carpentier in 1962. This problem is concerned with finding an optimal operating point of a power network minimizing the total power generation cost subject to network and physical constraints. It is well known that OPF is computationally hard to solve due to the nonlinear interrelation among the optimization variables. The objective is to identify a large class of networks over which every OPF problem can be solved in polynomial time. To this end, a convex relaxation is proposed, which solves the OPF problem exactly for every radial network and every meshed network with a sufficient number of phase shifters, provided power over-delivery is allowed. The concept of “power over-delivery” is equivalent to relaxing the power balance equations to inequality constraints.

Flow Networks: In this part of the dissertation, the minimum-cost flow problem over an arbitrary flow network is considered. In this problem, each node is associated with some possibly unknown injection, each line has two unknown flows at its ends related to each other via a nonlinear function, and all injections and flows need to satisfy certain box constraints. This problem, named generalized network flow (GNF), is highly non-convex due to its nonlinear equality constraints. Under the assumption of monotonicity and convexity of the flow and cost functions, a convex relaxation is proposed, which always finds the optimal injections. A primary application of this work is in the OPF problem. The results of this work on GNF prove that the relaxation on power balance equations (i.e., load over-delivery) is not needed in practice under a very mild angle assumption.

Generalized Weighted Graphs: Motivated by power optimizations, this part aims to find a global optimization technique for a nonlinear optimization defined over a generalized weighted graph. Every edge of this type of graph is associated with a weight set corresponding to the known parameters of the optimization (e.g., the coefficients). The motivation behind this problem is to investigate how the (hidden) structure of a given real/complex valued optimization makes the problem easy to solve, and indeed the generalized weighted graph is introduced to capture the structure of an optimization. Various sufficient conditions are derived, which relate the polynomial-time solvability of different classes of optimization problems to weak properties of the generalized weighted graph such as its topology and the sign definiteness of its weight sets. As an application, it is proved that a broad class of real and complex optimizations over power networks are polynomial-time solvable due to the passivity of transmission lines and transformers.

Structural Characterization of Pro-inflammatory and Anti-inflammatory Immunoglobulin G Fc Proteins

Immunoglobulin G (IgG) is central in mediating host defense due to its ability to target and eliminate invading pathogens. The fragment antigen binding (Fab) regions are responsible for antigen recognition; however the effector responses are encoded on the Fc region of IgG. IgG Fc displays considerable glycan heterogeneity, accounting for its complex effector functions of inflammation, modulation and immune suppression. Intravenous immunoglobulin G (IVIG) is pooled serum IgG from multiple donors and is used to treat individuals with autoimmune and inflammatory disorders such as rheumatoid arthritis and Kawasaki’s disease, respectively. It contains all the subtypes of IgG (IgG1-4) and over 120 glycovariants due to variation of an Asparagine 297-linked glycan on the Fc. The species identified as the activating component of IVIG is sialylated IgG Fc. Comparisons of wild type Fc and sialylated Fc X-ray crystal structures suggests that sialylation causes an increase in conformational flexibility, which may be important for its anti-inflammatory properties.

Although glycan modifications can promote the anti-inflammatory properties of the Fc, there are amino acid substitutions that cause Fcs to initiate an enhanced immune response. Mutations in the Fc can cause up to a 100-fold increase in binding affinity to activating Fc gamma receptors located on immune cells, and have been shown to enhance antibody dependent cell-mediated cytotoxicity. This is important in developing therapeutic antibodies against cancer and infectious diseases. Structural studies of mutant Fcs in complex with activating receptors gave insight into new protein-protein interactions that lead to an enhanced binding affinity.

Together these studies show how dynamic and diverse the Fc region is and how both protein and carbohydrate modifications can alter structure, leading to IgG Fc’s switch from a pro-inflammatory to an anti-inflammatory protein.

Model systems for the study of drug hypersensitivity

I. It was not possible to produce anti-tetracycline antibody in laboratory animals by any of the methods tried. Tetracycline protein conjugates were prepared and characterized. It was shown that previous reports of the detection of anti-tetracycline antibody by in vitro-methods were in error. Tetracycline precipitates non-specifically with serum proteins. The anaphylactic reaction reported was the result of misinterpretation, since the observations were inconsistent with the known mechanism of anaphylaxis and the supposed antibody would not sensitize guinea pig skin. The hemagglutination reaction was not reproducible and was extremely sensitive to minute amounts of microbial contamination. Both free tetracyclines and the conjugates were found to be poor antigens.

II. Anti-aspiryl antibodies were produced in rabbits using 3 protein carriers. The method of inhibition of precipitation was used to determine the specificity of the antibody produced. ε-Aminocaproate was found to be the most effective inhibitor of the haptens tested, indicating that the combining hapten of the protein is ε-aspiryl-lysyl. Free aspirin and salicylates were poor inhibitors and did not combine with the antibody to a significant extent. The ortho group was found to participate in the binding to antibody. The average binding constants were measured.

Normal rabbit serum was acetylated by aspirin under in vitro conditions, which are similar to physiological conditions. The extent of acetylation was determined by immunochemical tests. The acetylated serum proteins were shown to be potent antigens in rabbits. It was also shown that aspiryl proteins were partially acetylated. The relation of these results to human aspirin intolerance is discussed.

III. Aspirin did not induce contact sensitivity in guinea pigs when they were immunized by techniques that induce sensitivity with other reactive compounds. The acetylation mechanism is not relevant to this type of hypersensitivity, since sensitivity is not produced by potent acetylating agents like acetyl chloride and acetic anhydride. Aspiryl chloride, a totally artificial system, is a good sensitizer. Its specificity was examined.

IV. Protein conjugates were prepared with p-aminosalicylic acid and various carriers using azo, carbodiimide and mixed anhydride coupling. These antigens were injected into rabbits and guinea pigs and no anti-hapten IgG or IgM response was obtained. Delayed hypersensitivity was produced in guinea pigs by immunization with the conjugates, and its specificity was determined. Guinea pigs were not sensitized by either injections or topical application of p-amino-salicylic acid or p-aminosalicylate.

A nonlinear systems model for the control mechanism of free fatty acid-glucose metabolosm in normal humans

A mathematical model is proposed in this thesis for the control mechanism of free fatty acid-glucose metabolism in healthy individuals under resting conditions. The objective is to explain in a consistent manner some clinical laboratory observations such as glucose, insulin and free fatty acid responses to intravenous injection of glucose, insulin, etc. Responses up to only about two hours from the beginning of infusion are considered. The model is an extension of the one for glucose homeostasis proposed by Charette, Kadish and Sridhar (Modeling and Control Aspects of Glucose Homeostasis. Mathematical Biosciences, 1969). It is based upon a systems approach and agrees with the current theories of glucose and free fatty acid metabolism. The description is in terms of ordinary differential equations. Validation of the model is based on clinical laboratory data available at the present time. Finally procedures are suggested for systematically identifying the parameters associated with the free fatty acid portion of the model.

Development of a Cationic Mucic Acid Polymer-Based Nanoparticle siRNA Delivery System

Cancer chemotherapy has advanced from highly toxic drugs to more targeted treatments in the last 70 years. Chapter 1 opens with an introduction to targeted therapy for cancer. The benefits of using a nanoparticle to deliver therapeutics are discussed. We move on to siRNA in particular, and why it would be advantageous as a therapy. Specific to siRNA delivery are some challenges, such as nuclease degradation, quick clearance from circulation, needing to enter cells, and getting to the cytosol. We propose the development of a nanoparticle delivery system to tackle these challenges so that siRNA can be effective.

Chapter 2 of this thesis discusses the synthesis and analysis of a cationic mucic acid polymer (cMAP) which condenses siRNA to form a nanoparticle. Various methods to add polyethylene glycol (PEG) for stabilizing the nanoparticle in physiologic solutions, including using a boronic acid binding to diols on mucic acid, forming a copolymer of cMAP with PEG, and creating a triblock with mPEG on both ends of cMAP. The goal of these various pegylation strategies was to increase the circulation time of the siRNA nanoparticle in the bloodstream to allow more of the nanoparticle to reach tumor tissue by the enhanced permeation and retention effect. We found that the triblock mPEG-cMAP-PEGm polymer condensed siRNA to form very stable 30-40 nm particles that circulated for the longest time – almost 10% of the formulation remained in the bloodstream of mice 1 h after intravenous injection.

Chapter 3 explores the use of an antibody as a targeting agent for nanoparticles. Some antibodies of the IgG1 subtype are able to recruit natural killer cells that effect antibody dependent cellular cytotoxicity (ADCC) to kill the targeted cell to which the antibody is bound. There is evidence that the ADCC effect remains in antibody-drug conjugates, so we wanted to know whether the ADCC effect is preserved when the antibody is bound to a nanoparticle, which is a much larger and complex entity. We utilized antibodies against epidermal growth factor receptor with similar binding and pharmacokinetics, cetuximab and panitumumab, which differ in that cetuximab is an IgG1 and panitumumab is an IgG2 (which does not cause ADCC). Although a natural killer cell culture model showed that gold nanoparticles with a full antibody targeting agent can elicit target cell lysis, we found that this effect was not preserved in vivo. Whether this is due to the antibody not being accessible to immune cells or whether the natural killer cells are inactivated in a tumor xenograft remains unknown. It is possible that using a full antibody still has value if there are immune functions which are altered in a complex in vivo environment that are intact in an in vitro system, so the value of using a full antibody as a targeting agent versus using an antibody fragment or a protein such as transferrin is still open to further exploration.

In chapter 4, nanoparticle targeting and endosomal escape are further discussed with respect to the cMAP nanoparticle system. A diboronic acid entity, which gives an order of magnitude greater binding (than boronic acid) to cMAP due to the vicinal diols in mucic acid, was synthesized, attached to 5kD or 10kD PEG, and conjugated to either transferrin or cetuximab. A histidine was incorporated into the triblock polymer between cMAP and the PEG blocks to allow for siRNA endosomal escape. Nanoparticle size remained 30-40 nm with a slightly negative ca. -3 mV zeta potential with the triblock polymer containing histidine and when targeting agents were added. Greater mRNA knockdown was seen with the endosomal escape mechanism than without. The nanoparticle formulations were able to knock down the targeted mRNA in vitro. Mixed effects suggesting function were seen in vivo.

Chapter 5 summarizes the project and provides an outlook on siRNA delivery as well as targeted combination therapies for the future of personalized medicine in cancer treatment.