A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.     

A proposal for using the ensemble approach to understand genetic regulatory networks

Understanding the genetic regulatory network comprising genes, RNA, proteins and the network connections and dynamical control rules among them, is a major task of contemporary systems biology. I focus here on the use of the ensemble approach to find one or more well-defined ensembles of model networks whose statistical features match those of real cells and organisms. Such ensembles should help explain and predict features of real cells and organisms. More precisely, an ensemble of model networks is defined by constraints on the "wiring diagram" of regulatory interactions, and the "rules" governing the dynamical behavior of regulated components of the network. The ensemble consists of all networks consistent with those constraints. Here I discuss ensembles of random Boolean networks, scale free Boolean networks, "medusa" Boolean networks, continuous variable networks, and others. For each ensemble, M statistical features, such as the size distribution of avalanches in gene activity changes unleashed by transiently altering the activity of a single gene, the distribution in distances between gene activities on different cell types, and others, are measured. This creates an M-dimensional space, where each ensemble corresponds to a cluster of points or distributions. Using current and future experimental techniques, such as gene arrays, these M properties are to be measured for real cells and organisms, again yielding a cluster of points or distributions in the M-dimensional space. The procedure then finds ensembles close to those of real cells and organisms, and hill climbs to attempt to match the observed M features. Thus obtains one or more ensembles that should predict and explain many features of the regulatory networks in cells and organisms.     

A systems biology approach to analyse leaf carbohydrate metabolism in Arabidopsis thaliana

Plant carbohydrate metabolism comprises numerous metabolite interconversions, some of which form cycles of metabolite degradation and re-synthesis and are thus referred to as futile cycles. In this study, we present a systems biology approach to analyse any possible regulatory principle that operates such futile cycles based on experimental data for sucrose (Scr) cycling in photosynthetically active leaves of the model plant Arabidopsis thaliana. Kinetic parameters of enzymatic steps in Scr cycling were identified by fitting model simulations to experimental data. A statistical analysis of the kinetic parameters and calculated flux rates allowed for estimation of the variability and supported the predictability of the model. A principal component analysis of the parameter results revealed the identifiability of the model parameters. We investigated the stability properties of Scr cycling and found that feedback inhibition of enzymes catalysing metabolite interconversions at different steps of the cycle have differential influence on stability. Applying this observation to futile cycling of Scr in leaf cells points to the enzyme hexokinase as an important regulator, while the step of Scr degradation by invertases appears subordinate.     

Metabolic systems biology

The first full genome sequences were established in the mid-1990s. Shortly thereafter, genome-scale metabolic network reconstructions appeared. Since that time, we have witnessed an exponential growth in their number and uses. Here I discuss, from a personal point of view, four topics: (1) the placement of metabolic systems biology in the context of broader scientific developments, (2) its foundational concepts, (3) some of its current uses, and (4) some of the expected future developments in the field.     

A systems biology approach to nutritional immunology - focus on innate immunity

Innate immunity and nutrient metabolism are complex biological systems that must work in concert to sustain and preserve life. The effector cells of the innate immune system rely on essential nutrients to generate energy, produce metabolic precursors for macromolecule biosynthesis and tune their responses to infectious agents. Thus disruptions to nutritional status have a substantial impact on immune competence and can result in increased susceptibility to infection in the case of nutrient deficiency, or chronic inflammation in the case of over-nutrition. The traditional, reductionist methods used in the study of nutritional immunology are incapable of exploring the extremely complex interactions between nutrient metabolism and innate immunity. Here, we review a relatively new analytical approach, systems biology, and highlight how it can be applied to nutritional immunology to provide a comprehensive view of the mechanisms behind nutritional regulation of the innate immune system.     

Quantitative microscopy and systems biology: seeing the whole picture

Understanding cellular function requires studying the spatially resolved dynamics of protein networks. From the isolated proteins we can only learn about their individual properties, but by investigating their behavior in their natural environment, the cell, we obtain information about the overall response properties of the network module in which they operate. Fluorescence microscopy methods provide currently the only tools to study the dynamics of molecular processes in living cells with high temporal and spatial resolution. Combined with computational approaches they allow us to obtain insights in the reaction-diffusion processes that determine biological function on the scale of cells.     

A systems biology view of cancer

In order to understand how a cancer cell is functionally different from a normal cell it is necessary to assess the complex network of pathways involving gene regulation, signaling, and cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Since the network is typically complex, with multiple connections between pathways and important feedback loops, it is crucial to represent it in the form of a computational model that can be used for a rigorous analysis. This is the approach of systems biology, made possible by new -omics data generation technologies. The goal of this review is to illustrate this approach and its utility for our understanding of cancer. After a discussion of recent progress using a network-centric approach, three case studies related to diagnostics, therapy, and drug development are presented in detail. They focus on breast cancer, B-cell lymphomas, and colorectal cancer. The discussion is centered on key mathematical and computational tools common to a systems biology approach.     

Complex systems biology approach to understanding coordination of JAK-STAT signaling

In this work, we search for coordination as an organizing principle in a complex signaling system using a multilevel hierarchical paradigm. The objective is to explain the underlying mechanism of Interferon (IFN_γ) induced JAK-STAT (specifically JAK1/JAK2-STAT1) pathway behavior. Starting with a mathematical model of the pathway from the literature, we modularize the system using biological knowledge via principles of biochemical cohesion, biological significance, and functionality. The modularized system is then used as a basis for in silico inhibition, knockdown/deletion and perturbation experiments to discover a coordination mechanism. Our analysis shows that a module representing the SOCS1 complex can be identified as the coordinator. Analysis of the coordinator can then be used for the selection of biological experiments for the discovery of ‘soft’ molecular drug targets, that could lead to the development of improved therapeutics. The coordinator identified is also being investigated to determine its relationship to pathological conditions.     

Gastric cancer biomarkers; A systems biology approach

Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways.In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.     

Toward computational systems biology

The development and successful application of high-throughput technologies are transforming biological research. The large quantities of data being generated by these technologies have led to the emergence of systems biology, which emphasizes large-scale, parallel characterization of biological systems and integration of fragmentary information into a coherent whole. Complementing the reductionist approach that has dominated biology for the last century, mathematical modeling is becoming a powerful tool to achieve an integrated understanding of complex biological systems and to guide experimental efforts of engineering biological systems for practical applications. Here I give an overview of current mainstream approaches in modeling biological systems, highlight specific applications of modeling in various settings, and point out future research opportunities and challenges.     

Lipopolysaccharide-stimulated responses in rat aortic endothelial cells by a systems biology approach

The vascular endothelium plays an important role in regulating immune and inflammatory responses to resist pathogens infection. Although it has been known that lipopolysaccharide (LPS) is a critical inducer of sepsis or endotoxemia, the systematic responses of LPS-stimulation in endothelial cells (ECs) are still unclear. The present study aims to analyze the late-phase responses of LPS-induced rat aortic ECs by using systematic biology approaches, including rat cDNA microarray, 2-DE and MALDI-TOF MS/MS, and cytokine protein array. Furthermore, to improve the efficiency of analysis of the bulk systematic data of rat, we designed a set of bioinformatic tools to convert and integrate these rat data into the corresponding human genes or proteins IDs based on BioCarta, KEGG, and Gene Ontology databases. Using the systematic analysis, it was shown that LPS could promote some signaling or metabolic pathways as well as pathophysiologic phenomena of proliferation, atherogenesis, inflammation, and apoptosis through activated nuclear factor-kappaB pathway in ECs. Interestingly, ECs also activated the mediators of anti-inflammation, antiapoptosis, and antioxidation to protect themselves. Moreover, the expressions of altered genes, proteins, and their involvement in the hypothetical signaling pathway can provide further understanding of inflammation associated responses in ECs.     

A systems biology-led insight into the role of the proteome in neurodegenerative diseases

Introduction: Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases.

Areas covered: Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer’s disease, Parkinson’s disease and other neurodegenerative disorders, as well as of affected human tissues.

Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.     

Building momentum for systems and synthetic biology in India

Biological systems are inherently noisy. Predicting the outcome of a perturbation is extremely challenging. Traditional reductionist approach of describing properties of parts, vis-a-vis higher level behaviour has led to enormous understanding of fundamental molecular level biology. This approach typically consists of converting genes into junk (knock-down) and garbage (knock-out) and observe how a system responds. To enable broader understanding of biological dynamics, an integrated computational and experimental strategy was formally proposed in mid 1990s leading to the re-emergence of Systems Biology. However, soon it became clear that natural systems were far more complex than expected. A new strategy to address biological complexity was proposed at MIT (Massachusetts Institute of Technology) in June 2004, when the first meeting of synthetic biology was held. Though the term ‘synthetic biology’ was proposed during 1970s (Szybalski in Control of gene expression, Plenum Press, New York, 1974), the usage of the original concept found an experimental proof in 2000 with the demonstration of a three-gene circuit called repressilator (Elowitz and Leibler in Nature, 403:335–338, 2000). This encouraged people to think of forward engineering biology from a set of well described parts.     

系统生物学对医学的影响

系统生物学是21世纪最前沿的科学之一,它是随着生命科学飞速发展而产生的一门新兴生物学分支[1],它综合数学、信息科学和生物学的各种工具来阐明和理解大量的数据所包含的生物医学意义,从而使人们能够从整体上理解生物医学系统并精确、量化地预测生物医学系统的行为。随着系统生物学的发展及其理论的突破,将在疾病诊治、新药开发、预防医学方面发挥重要的作用,有助于弥补传统医学缺陷并促进其发展。     

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex–complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery‐potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.     

A systems biology analysis of metastatic melanoma using in-depth three-dimensional protein profiling

Melanoma is an excellent model to study molecular mechanisms of tumor progression because melanoma usually develops through a series of architecturally and phenotypically distinct stages that are progressively more aggressive, culminating in highly metastatic cells. In this study, we used an in-depth, 3-D protein level, comparative proteome analysis of two genetically, very closely related melanoma cell lines with low- and high-metastatic potentials to identify proteins and key pathways involved in tumor progression. This proteome comparison utilized fluorescent tagging of cell lysates followed by microscale solution IEF prefractionation and subsequent analysis of each fraction on narrow-range 2-D gels. LC-MS/MS analysis of gel spots exhibiting significant abundance changes identified 110 unique proteins. The majority of observed abundance changes closely correlate with biological processes central to cancer progression, such as cell death and growth and tumorigenesis. In addition, the vast majority of protein changes mapped to six cellular networks, which included known oncogenes (JNK, c-myc, and N-myc) and tumor suppressor genes (p53 and transforming growth factor-β) as critical components. These six networks showed substantial connectivity, and most of the major biological functions associated with these pathways are involved in tumor progression. These results provide novel insights into cellular pathways implicated in melanoma metastasis.     

Signaling networks in Leishmania macrophages deciphered through integrated systems biology: a mathematical modeling approach

Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines.     

Maps, books and other metaphors for systems biology

We briefly review the use of metaphors in science and progressively focus on fields from biology and molecular biology to genomics and bioinformatics. We discuss how metaphors are both a tool for scientific exploration and a medium for public communication of complex subjects, by various short examples. Finally, we propose a metaphor for systems biology that provides an illuminating perspective for the ambitious goals of this field and delimits its current agenda.     

In vitro regulatory models for systems biology

The reductionist approach has revolutionized biology in the past 50 years. Yet its limits are being felt as the complexity of cellular interactions is gradually revealed by high-throughput technology. In order to make sense of the deluge of “omic data”, a hypothesis-driven view is needed to understand how biomolecular interactions shape cellular networks. We review recent efforts aimed at building in vitro biochemical networks that reproduce the flow of genetic regulation. We highlight how those efforts have culminated in the rational construction of biochemical oscillators and bistable memories in test tubes. We also recapitulate the lessons learned about in vivo biochemical circuits such as the importance of delays and competition, the links between topology and kinetics, as well as the intriguing resemblance between cellular reaction networks and ecosystems.     

A systems biology approach to the blood-aluminium problem: the application and testing of a computational model

Transport and distribution of systemic aluminium are influenced by its interaction with blood. Current understanding is centred upon the role played by the iron transport protein transferrin which has been shown to bind up to 90% of serum total aluminium. We have coined what we have called the blood-aluminium problem which states that the proportion of serum aluminium which, at any one moment in time, is bound by transferrin is more heavily influenced by kinetic constraints than thermodynamic equilibria with the result that the role played by transferrin in the transport and distribution of aluminium is likely to have been over estimated. To begin to solve the blood-aluminium problem and therewith provide a numerical solution to the aforementioned kinetic constraints we have applied and tested a simple computational model of the time-dependency of a putative transferrin ligand (L) binding aluminium to form an Al-L complex with a probability of existence, K(E), between 0% (no complex) and 100% (complex will not dissociate). The model is based upon the principles of a lattice-gas automaton which when ran for K(E) in the range 0.1-98.0% demonstrated the emergence of complex behaviour which could be defined in the terms of a set of parameters (equilibrium value, E(V), equilibrium time, E(T), peak value, P(V), peak time, P(T), area under curve, AUC) the values of which varied in a predictable way with K(E). When K(E) was set to 98% the model predicted that ca. 90% of the total aluminium would be bound by transferrin within ca. 350 simulation timesteps. We have used a systems biology approach to develop a simple model of the time-dependency of the binding of aluminium by transferrin. To use this approach to begin to solve the blood-aluminium problem we shall need to increase the complexity of the model to better reflect the heterogeneity of a biological system such as the blood.