Namrata Tomar

Immunoinformatics: an integrated scenario

Genome sequencing of humans and other organisms has led to the accumulation of huge amounts of data, which include immunologically relevant data. A large volume of clinical data has been deposited in several immunological databases and as a result immunoinformatics has emerged as an important field which acts as an intersection between experimental immunology and computational approaches. It not only helps in dealing with the huge amount of data but also plays a role in defining new hypotheses related to immune responses. This article reviews classical immunology, different databases and prediction tools. It also describes applications of immunoinformatics in designing in silico vaccination and immune system modelling. All these efforts save time and reduce cost.

Comparing methods for metabolic network analysis and an application to metabolic engineering.

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.

Immunoinformatics: A Brief Review

A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

An integrated pathway system modeling of Saccharomyces cerevisiae HOG pathway: a Petri net based approach.

Biochemical networks comprise many diverse components and interactions between them. It has intracellular signaling, metabolic and gene regulatory pathways which are highly integrated and whose responses are elicited by extracellular actions. Previous modeling techniques mostly consider each pathway independently without focusing on the interrelation of these which actually functions as a single system. In this paper, we propose an approach of modeling an integrated pathway using an event-driven modeling tool, i.e., Petri nets (PNs). PNs have the ability to simulate the dynamics of the system with high levels of accuracy. The integrated set of signaling, regulatory and metabolic reactions involved in Saccharomyces cerevisiae’s HOG pathway has been collected from the literature. The kinetic parameter values have been used for transition firings. The dynamics of the system has been simulated and the concentrations of major biological species over time have been observed. The phenotypic characteristics of the integrated system have been investigated under two conditions, viz., under the absence and presence of osmotic pressure. The results have been validated favorably with the existing experimental results. We have also compared our study with the study of idFBA (Lee et al., PLoS Comput Biol 4:e1000–e1086, 2008) and pointed out the differences between both studies. We have simulated and monitored concentrations of multiple biological entities over time and also incorporated feedback inhibition by Ptp2 which has not been included in the idFBA study. We have concluded that our study is the first to the best of our knowledge to model signaling, metabolic and regulatory events in an integrated form through PN model framework. This study is useful in computational simulation of system dynamics for integrated pathways as there are growing evidences that the malfunctioning of the interplay among these pathways is associated with disease.

Cross Talk Between the Metabolic and Immune Systems

Understanding the interplay between metabolic and cellular signaling systems has emerged as a focus in the study of metabolic disorders, cancer, and immune responses. Immune system is active in the regulation of metabolism. Lymphocyte activation initiates a program of cell growth, proliferation, and differentiation that increase metabolic demand. Activated lymphocytes must alter their metabolism to support these increased synthetic activities. In this chapter, we describe how signaling via the immune system integrates with metabolic functions to control immune response and vice versa. It has been explained mainly in the context of T lymphocyte activation and, to a lesser detail, in other immune cell types.

Modeling the optimal central carbon metabolic pathways under feedback inhibition using flux balance analysis.

Metabolism is a complex process for energy production for cellular activity. It consists of a cascade of reactions that form a highly branched network in which the product of one reaction is the reactant of the next reaction. Metabolic pathways efficiently produce maximal amount of biomass while maintaining a steady-state behavior. The steady-state activity of such biochemical pathways necessarily incorporates feedback inhibition of the enzymes. This observation motivates us to incorporate feedback inhibition for modeling the optimal activity of metabolic pathways using flux balance analysis (FBA). We demonstrate the effectiveness of the methodology on a synthetic pathway with and without feedback inhibition. Similarly, for the first time, the Central Carbon Metabolic (CCM) pathways of Saccharomyces cerevisiae and Homo sapiens have been modeled and compared based on the above understanding. The optimal pathway, which maximizes the amount of the target product(s), is selected from all those obtained by the proposed method. For this, we have observed the concentration of the product inhibited enzymes of CCM pathway and its influence on its corresponding metabolite/substrate. We have also studied the concentration of the enzymes which are responsible for the synthesis of target products. We further hypothesize that an optimal pathway would opt for higher flux rate reactions. In light of these observations, we can say that an optimal pathway should have lower enzyme concentration and higher flux rates. Finally, we demonstrate the superiority of the proposed method by comparing it with the extreme pathway analysis.

A Brief Outline of the Immune System

The various cells and proteins responsible for immunity constitute the immune system, and their orchestrated response to defend foreign/non-self substances (antigen) is known as the immune response. When an antigen attacks the host system, two distinct, yet interrelated, branches of the immune system are active-the nonspecific/innate and specific/adaptive immune response. Both of these systems have certain physiological mechanisms, which enable the host to recognize foreign materials to itself and to neutralize, eliminate, or metabolize them. Innate immunity represents the earliest development of protection against antigens. Adaptive immunity has again two branches-humoral and cell mediated. It should be noted that both innate and adaptive immunities do not work independently. Moreover, most of the immune responses involve the activity and interplay of both the humoral and the cell-mediated immune branches of the immune system. We have described these branches in detail along with the mechanism of antigen recognition. This chapter also describes the disorders of immune system in brief.

A Model of an Integrated Immune System Pathway in Homo sapiens and Its Interaction with Superantigen Producing Expression Regulatory Pathway in Staphylococcus aureus : Comparing Behavior of Pathogen Perturbed and Unperturbed Pathway

Response of an immune system to a pathogen attack depends on the balance between the host immune defense and the virulence of the pathogen. Investigation of molecular interactions between the proteins of a host and a pathogen helps in identifying the pathogenic proteins. It is necessary to understand the dynamics of a normally behaved host system to evaluate the capacity of its immune system upon pathogen attack. In this study, we have compared the behavior of an unperturbed and pathogen perturbed host system. Moreover, we have developed a formalism under Flux Balance Analysis (FBA) for the optimization of conflicting objective functions. We have constructed an integrated pathway system, which includes Staphylococcal Superantigen (SAg) expression regulatory pathway and TCR signaling pathway of Homo sapiens. We have implemented the method on this pathway system and observed the behavior of host signaling molecules upon pathogen attack. The entire study has been divided into six different cases, based on the perturbed/unperturbed conditions. In other words, we have investigated unperturbed and pathogen perturbed human TCR signaling pathway, with different combinations of optimization of concentrations of regulatory and signaling molecules. One of these cases has aimed at finding out whether minimization of the toxin production in a pathogen leads to the change in the concentration levels of the proteins coded by TCR signaling pathway genes in the infected host. Based on the computed results, we have hypothesized that the balance between TCR signaling inhibitory and stimulatory molecules can keep TCR signaling system into resting/stimulating state, depending upon the perturbation. The proposed integrated host-pathogen interaction pathway model has accurately reflected the experimental evidences, which we have used for validation purpose. The significance of this kind of investigation lies in revealing the susceptible interaction points that can take back the Staphylococcal Enterotoxin (SE)-challenged system within the range of normal behavior.

Modeling host-pathogen interactions: H. sapiens as a host and C. difficile as a pathogen.

Many complex mechanisms in immunological studies cannot be measured by experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of host–pathogen system interactions can be explored and clinical treatment schedules can be optimized to lower the microbial toxin burden and side effects in the host system. In this study, we use a computational modeling technique that aims to explain the host–pathogen interactions and suggests how the host system tries to survive from the pathogen attack. The method generates data on reaction fluxes in a pathway at steady state. A set of constraints is incorporated and an objective function for the minimization of toxin expression, with respect to some parameters such as concentration of signaling molecules, is formulated. We have integrated the toxin expression regulatory pathway in Clostridium difficile, apoptosis and mitogen‐activated protein kinase pathways in an infected host (Homo sapiens). We have found that due to the minimization of the toxin expression, the signal flow values for most of the survival genes are at the higher side, whereas it is the reverse for most of the proapoptotic genes. We have observed increased signal flow values of the molecules for extracellular regulated kinase as compared with the molecules present in c‐Jun NH2‐terminal kinase/p38 pathways. In light of these observations, we can hypothesize that lower toxin level in a pathogen implies higher chance of host survival. Copyright