In the last decades the rapid progress of the molecular biology and the achievements in this scientific area is directly related to the advances in the computer sciences and the development of new software tools.

Systems Biology and Omics Technologies: The Big Picture

In the last decades the rapid progress of the molecular biology and the achievements in this scientific area is directly related to the advances in the computer sciences and the development of new software tools. The application of different omics-related technologies (genomics, epigenomics, transcriptomic, proteomics, metabolomics, etc.) leads to the accumulation of versatile biological data and their combined interpretation open new possibilities for the scientists to move from studying isolated biological molecules towards a broad analysis of large sets of biological molecules. The biological sciences have become big-data sciences which are of growing importance for solving problems concerning human health and environment. The big data challenges are not only their size but also their increasing complexity.

Systems biology has emerged as a new interdisciplinary study field which has gained a tremendous attention in the last few years. Although the term “Systems biology” has been used in various ways, it is generally understood to describe research that combines biology with seemingly disparate disciplines such as physics, biochemistry, engineering, biostatistics, mathematics, computer science, bioinformatics and others. The main purpose of this cross-disciplinary field is to obtain a highly parallel view of the biological systems at the molecular level and understand how they function as a whole instead as a sum of parts. By clarifying the molecular mechanisms and processes at different system levels in the organism or a cell, it could be predicted how these systems change over time and under varying conditions. The great amount of information obtained on molecular bases of cell physiology and organization could provide solutions concerning diseases, toxicities, therapies, drug discovery, etc.

Systems biology has launched as a distinct discipline in 1950s with the work of the systems theorists Mihajlo Mesarovic and Ludwig von Bertalanffy on the general systems theory. The main concept of this theory is that the dynamics of any system is a result of the relationships between its separate units which determining its function. At that time, by thoroughly studying enzymes and kinetics of enzyme reactions, biochemists, followers of the system theory, tried to examine the behavior of the biochemical pathways as a network instead as a sum of its parts. Moving away from the biochemical area, Reinhart Heinrich developed theoretical approaches for the description and quantitative investigation of signaling pathways and developed a metabolic control theory. The need for an integrated approach for a more detailed study and understanding of the complex biological processes became evident.

Systems biology is highly dependent on the biological information obtained by molecular biology and / or individual omics approaches, but often they are largely hypothesis-driven or reductionist. In the reductionist approach the functional properties of the individual molecular components in the complex biological systems are studied. After the completion of the Human Genome Project the modern science has developed beyond the gene-centered view of the earlier genomic era. This period designated as a postgenomic era is characterized by major changes in the scientific research conduction and results interpretation and according to the Bloom this is the end of the “naïve reductionism”. Nevertheless, that these classical methods alone cannot provide a complete understanding of living organisms, they will continue to be an essential element of all biological research. In this context, Systems biology has caused the fundamental change in traditional approaches. It uses a new hypothesis-generating holistic approach, focuses on the study of subsystems, which allows deeper understanding of the whole process. The galactose utilization (GAL) pathway of yeast Saccharomyces cerevisiae is one example of the application of Systems biology approach for rereading and interpretation of the results obtained by the reductionist approach (one gene/one protein at a time). On the basis of experimental data obtained from protein and RNA levels analyses as well as protein–protein and protein–DNA interactions and their integration into a single model, new hypothesis was provided on the regulation of GAL pathway, which was experimentally verified afterward.

Through the use of computational and mathematical tools a large amount of experimental data is collected and integrated by Systems biology for revealing unknown patterns and hypothesis generation. This is an important strategy to gain new insights of the biological systems and also help in the experimental design.

The systems-level approach of the biological systems is aimed at clarifying the following three issues in the context of molecular network: i) what are the individual components of the system; ii) how do they work separately? and iii) how do these components work together to accomplish a task? (3). In the context of molecular networks, the basic purpose of the Systems biology can be summarized as follows: (i) an understanding of the structure of all the components of a cell / organism up to molecular level, (ii) the ability to predict the future state of the cell / organism under a normal environment, iii) the ability to predict the output responses for a given input stimulus, and iv) the ability to estimate the changes in system behavior upon perturbation of the components or the environment.

The term “systems” in Systems biology defines different span of complexity ranging from two macromolecules that interact to perform a particular task to whole organisms (Fig.1).

Thus, Systems biology shares a common scientific goal with the discipline of physiology which is dedicated to study the integrated function of the entire complex biological systems. Organisms are much more than the sum of their parts and the complexity of physiological processes cannot be understood simply by studying how the components work in isolation. For example, genes encode the primary structure of the cellular proteins that in turn carry out specific functions supporting cell metabolism and physiology and organism development. At the same time, the proteins in each cell do not act in isolation but in a complex network whose are important to specify phenotypes. Most biological processes are highly dynamic and often involve more than one type of molecules. At the same time one phenotype may be conditioned by several different molecular and epigenetic mechanisms and one type of molecule may be involved in different phenotypes. Actually, even in the same organism, a protein may play different roles in different cells; or signal pathway effectors may induce various differentiation programs in different cell lineages. Moreover, in multicellular organisms, single cells do not have an existence independent from the whole organism, they are ontogenetically linked.

Regardless of their common goal, Systems biology and physiology utilize different tools and experimental approaches, which leads to obtaining a different set of experimental data. A cross-disciplinary Systems biology is a modern rapidly evolving discipline due to the fact that it uses a variety of methods and tools including large-scale functional genomics and other omics technologies, bioinformatics and computer modeling, which are not exploited by the physiologists. The role of computational biology in Systems biology is to process and analyze massive amounts of empirical data produced by different omics levels which in turn leads to biological knowledge discovery and generation of research hypothesis. The application of in silico and simulation-based analyses give opportunities to make predictions which further are confirmed by experimental assays. The rapid advance of information technologies including also enhancement of public web-databases of biological information also support the development of omics studies and respectively lead to the progress of systems biology.
In order to comprehensively study biological processes, it is critical to understand how the separate biological layers (genome, epigenome, transcriptome, proteome, metabolome, and ionome) interconnect with one another in a cellular system and how the flow of biological information takes place. The combination of different omics analyses employing multi-omics approach is required to design a precise picture of living organisms. Transcriptomics, proteomics, and metabolomics data can answer key biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration can comprehensively assimilate, annotate, and model these large data sets.

The transfer of genetic information in biological systems is realized from DNA to mRNA to protein and this is stated as the Central dogma in molecular biology. The three main processes in each cell are replication, transcription and translation. Their constant flow ensures the maintenance and conversion of the genetic information, encoded in DNA into gene products, which are either RNAs or proteins, depending on the gene. Replication is a process of a cell`s DNA duplication and it is the basis for biological inheritance. It is carried out by the enzyme DNA polymerase that copies a single parental double-stranded DNA molecule into two daughter double-stranded DNA molecules. The enzyme RNA polymerase creates an RNA molecule from DNA and that process is known as transcription. The newly synthesized RNA molecule is complementary to a gene-encoding stretch of DNA. Translation makes protein from mRNA. The ribosome generates a polypeptide chain of amino acids using mRNA as a template. The polypeptide chain folds up to become a protein. In eukaryotic cells, or those cells that have a nucleus, replication and transcription take place within the nucleus while translation takes place outside of the nucleus in cytoplasm. In prokaryotic cells, or those cells that do not have a nucleus, all three processes occur in the cytoplasm. The organisms` phenotype is determined by that information transfer paradigm. Biologists have studied these “omes” for years in the form of genomics, transcriptomics and proteomics. The data from these experimental approaches are complemented by epigenomics and metabolomics that have recently been used to solve specific problems concerning many functions of an organism. The rapid development and advance in “omics” technologies determine the progressive expand of the volume of information that can be gathered in individual studies. Moreover, the current high throughput nature of these techniques has increased accessibility to this information in terms of time and cost. Many researchers are placed in a situation where they can collect several omics data sets on the same experimental samples. In aim to obtain more comprehensive conclusions on biological processes these data sets must be integrated by multi-omics approach and analyzed as a holistic system (Fig. 2).

The term “Omics” derived from a Greek word and the addition of thе suffix -ome to cellular molecules, such as gene, transcript, protein, metabolite, gives meaning to “whole,” “all,” or “complete.” The different layers of the cell, consisting of DNA and modifications (Genome, Epigenome), RNA and protein content (Transcriptome, Proteome), small molecules (Metabolome, Lipidome) and elemental composition (measured as ‘Ionome’), can be analysed by omic technologies. The combination of omic layers in a multi-ome dataset are integrated through robust systems biology which is able to reveal inter-layer mechanisms and interactions as well as the function of cell populations’ tissues, organs, and the whole organism. Omics approaches comprise a larger number of measurements per endpoint and although the number of parameters measured per analysis is increased, the number of replicates is decreased. This on one hand is due to the super estimation of methods since it is considered that more measurements would compensate a small number of samples and on the other hand is due to the cost and time of omics experiments.

The single-omic disciplines aimed at studying specific biological issues without requiring a prior understanding of the biological bases involved. Depending on the type of biomolecule they primarily focus on in a specific biological sample, omics technologies are divided into: genomics (genome / gene), metagenomics (genomes recovered directly from environmental samples), epigenomics (supporting structure of genome, including protein and RNA binders, alternative DNA structures, and chemical modifications on DNA), transcriptomics (mRNA), proteomics (peptides / proteins), metabolomics (metabolites), lipidomics (lipids) glycomes (carbohydrate and sugars), ionomics (ions).

Although none of the current omic technologies is perfect, some of them succeed to provide more comprehensive picture of the biological layer they aim to study than others. This fact is due not only to differences in the state of technological developments, but rather of the differences in the in chemical and physical complexity of each biological level.

Genomics

Genomics is the systematic study of all of an organism’s genes (the genome), including interactions of those genes with each other and with the organism`s environment. The genome is the basal biological layer in the cell and represents the total DNA of a cell or organism. Deoxyribonucleic acid (DNA) is the chemical compound that contains all the necessary genetic information needed to develop and direct the activities of nearly all living organisms. DNA molecules are made of two twisting, paired strands, often referred to as a double helix. Each DNA strand is made of four nucleotide bases – adenine (A), thymine (T), guanine (G), and cytosine (C) that pair specifically on opposite strands: A always pairs with a T; a C always pairs with a G. A sequence of three adjacent nucleotides (codon) encodes for a specific amino acid during protein synthesis or translation. The nucleotides` order along the DNA molecules determines the genetic code. The genetic code is universal because it is the same among all organisms and it is also degenerate because 64 codons encode only 22 amino acids. With its four-letter language, DNA contains the information needed to build an entire organism. A gene represents the unit of DNA that carries codes for making a specific protein or a set of proteins. On the basis of the intrinsic complementarity of the nucleotide bases in DNA, it has become possible to rapidly sequencing a huge number of genomes at a relatively low cost. From the efficiently sequenced genomes predictions about RNA and protein sequences could be made which is combined in the multi-omic approaches. The digital form of the DNA sequences as a kind of biological omic information could be easily stored in biological databases and shared between scientists all over the world. The sequencing of the first whole genome of the bacterium Haemophilus influenza in 1995 made a revolution in molecular biology. The big volume of sequences data was produced which was beyond being completely interpreted. To decipher relevant genetic information from the background genetic material was almost an impossible task. To overcome this, more detailed biological information was required. Information on the transcription of the genetic material and subsequent production of proteins was necessary.

An essential research direction in the field of Systems biology is the functional genomics. This discipline develops and exploit large-scale and high-throughput methodologies in aim to define and analyze gene function at a global level. It is very important for developing a systems level understanding of a biological process to identify the genes, and the proteins they encode which work together to give rise to that process. Functional genomics is an integrative scientific field which combines multiple large-scale datasets in attempts to generate insights into gene function. At first, genes were analyzed individually, but the advance of technologies in recent years make possible the expression of thousands of genes to be analyzed simultaneously. This large-scale analysis of gene function is called DNA microarrays technology (Fig. 3).

DNA microarrays measure differences in DNA sequence between individuals and this technology can provide information for the function of uncharacterized genes and also can reveal clusters of interacting genes that give rise to a biological process of interest. Microarray analyses can also provide insights into mechanisms of gene regulation, evolution and the etiology of disease. For example, the microarray data analysis could reveal abnormalities such as chromosomal insertions and deletions or abnormal chromosomal numbers in a process called comparative genomic hybridization. The most common variations in DNA sequences between people are single nucleotide polymorphisms (SNPs), in which one nucleotide is substituted for another; this may have functional significance if the change results in a codon for a different amino acid. They are of particular interest when linked with diseases with a genetic determination. Single nucleotide polymorphism profiling also has a role in pharmacogenomics in exploring individual patient responses to drugs.

Epigenomics

The epigenetic modifications such as DNA methylation, histone modifications, 2D and 3D analysis of chromatin structure and non-coding RNA are studied by the methods of epigenomics. This -omics direction focuses on the analysis of overall epigenetic changes which provides important information regarding mechanisms and function of gene regulation across many genes in a cell or organism. Scientists have understood that the individual`s phenotype is not controlled only by the genome but also by the changes in regulation of gene activities. Genetic experiments in humans and animals have proved that, in addition to the DNA sequence, epigenetic marks may be transmitted from parent to offspring via the gametes and influence the phenotype of offspring.
Epigenomics defines the modifications in the regulation of gene activities that act without, or independently of, changes in gene sequences. Some definitions confine epigenetics/epigenomics to modifications of the phenotype without changes of the DNA sequence that are transmitted to the next generations. The epigenetic modifications are chemical modifications, which are not coded by the genome and they coordinate how and when genes are expressed. The epigenomics explores heritable, reversible modifications of DNA and chromatin that do not influence the primary nucleotide sequences. While the term epigenomics would describe the analysis of epigenetic changes across many genes in a cell or throughout an entire organism, epigenetics centers on processes that regulate how and when specific genes are turned on and turned off. Several factors are known to affect the epigenetic regulation: 1) Nutrition (dietary factors); 2) Environmental factors; 3) Radiation exposure; 4) Infectious agents; 5) Immunological factors; 6) Genetic factors; 7) Toxic agents; 8) Mutagens.
Versatile detection methods are applied for analysis of epigenetics modifications in the cell. DNA methylation are analyzed by digestion assays and bisulfite sequencing of DNA. In digestion assays, the genomic DNA is fragmented with methylation-sensitive and methylation-insensitive endonucleases. Methylation-sensitive restriction enzymes cleave only unmethylated DNA and leave methylated DNA fragments undigested. The fragmented DNA can be analyzed by sequencing or microarray, and thus the methylation sites are mapped. The disadvantage of this method is that it only studies the DNA sequences near the targets of chosen restriction enzymes, and is usually used to characterize only global levels of DNA methylation instead of identifying methylated DNA at the single residue level. Resolution is improved by using the method of bisulfite sequencing. This strand-specific method is used to convert unmethylated cytosine to uracil, whereas methylated cytosine residues remain unaffected. The resulting DNA is amplified in PCR and can be analyzed by sequencing of the regions of interest. The analysis can also be done using MALDI-TOF mass spectrometry or microarrays. DNA methylation can also be studied through methods based on the principle of affinity chromatography. The sample containing fragmented DNA is loaded to a column with bound methyl-binding domain (MBD) of MeCP2, specific for methylated DNA. The methylated DNA fractions are eluted out and analyzed with genome-wide techniques such as MBDCap-seq/MethylCap-seq. Another approach is immunoprecipitation of methylated DNA (MeDIP), which is based on the specific binding of antibodies to the methylated cytosine (5mC) in DNA. This method is also used for the analysis of hydroxylated methylcytosine (5hmC). Purified fragments are analyzed by PCR, sequencing or microarray.
A widely used technique for detection of histone modifications is the immunoprecipitation of chromatin (ChIP). It is used for identifying local posttranslational modifications of the histone tails and for monitoring changes in the modifications in response to different stimuli. Antibodies against specific histone modifications (such as trimethylation of histone H3 lysine K27) are used for immunoprecipitation of that chromatin regions that have these modifications. ChIP can also be applied to study the binding of transcription factors and enzymes to the chromatin, using a specific antibody against the factor of interest. The associated chromatin regions can be analyzed either by PCR to detect specific loci or deep sequencing for a more global view, as well as by using microarray (ChIP-chip), but in the last case the resolution of data is weaker compared to sequencing. During the last decade the basic ChIP method has been developed which leads to the emergence of other diverse ChIP methods such as μChIP-seq for low, micro-scale sample amount analysis, and the modern single-molecule real-time sequencing (SMRT; “third generation sequencing”) of ChIP-samples.

Epigenetic modifications can also be studied by methods identifying the active regions of chromatin. In the DNase-seq method, the enzyme DNase I is utilized to digest the DNA that is not protected by nucleosome structure, so the regions that are sensitive to DNase are associated with active genes. Sequencing, microarray or Southern Blot techniques can be used for the results analysis and interpretation. The open chromatin can be detected also by the formaldehyde-assisted isolation of regulatory elements (FAIRE), which result in the isolation of open, nucleosome-depleted chromatin regions. The FAIRE method is based on the cross-linking between formaldehyde and DNA, histones and other proteins associated with it. The DNA is sonicated to be fragmented and after that isolated with phenol-chloroform extraction. Only DNA that is not bound by nucleosomes and associated proteins remains in the aqueous phase in the extraction, thus resulting in the isolation of the open and active regions of the genome. The isolated fragments can be analyzed again with different methods, such as PCR, microarray, and sequencing. Chromatin can also be studied by chromatin conformation capture (3C) technique that identifies the chromatin regions that are physically associated together, such as promoters with enhancers. 3C is often analyzed by PCR, but nowadays also by deep sequencing of the interactions globally (Hi-C).

Noncoding RNAs (ncRNAs) act as epigenetic modifiers to strongly regulate gene expression. Their aberrant expression mainly in the form of microRNAs (miRNAs) and long noncoding RNAs, may modify gene expression and trigger complicated immune disorders. The analysis of the RNA component of epigenetics most often is done on a genome-wide scale using next-generation sequencing methods. The changes in ncRNA and mRNA can be characterized by deep sequencing. The expression of RNAs can be analyzed also by quantitative Polymerase Chain Reaction (qPCR). Quantitative PCR also known as real-time PCR is a laboratory technique in molecular biology, by which the amount of the PCR product can be determined in real-time, and is very useful for investigating gene expression.

Transcriptomics

When genes are expressed, the genetic information stored in DNA is transferred to RNA (ribonucleic acid). RNAs are important macromolecules, composed of linear chains of nucleotides (Fig. 4), which are produced by the cellular process of transcription. RNAs perform diverse cellular and biological functions as either serve as templates for protein synthesis, or play critical catalytic and regulatory roles. In transcription the genetic information is transferred from DNA to mRNA. This process is carried out by an enzyme RNA polymerase. Several classes of RNAs exist in cells (messenger RNAs (mRNA), transfer RNAs (tRNA), ribosomal RNA (rRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), short interfering RNA (siRNAs), micro RNA (miRNAs), long non-coding RNA and pseudogenes), but those which take part in protein synthesis are messenger RNA (mRNA), transfer RNA (tRNA) and ribosomal RNA. Messenger RNA (mRNA) is a single-stranded molecule that mediate the transfer of genetic information to the ribosomes where the proteins are synthesized. In eukaryotes, each gene is transcribed to yield a single mRNA, whereas in prokaryotes, a single mRNA molecule may carry the genetic information from several genes; that is, several protein coding regions. A linear correspondence exists between the base sequence of a gene and the amino acid sequence of a polypeptide. Each group of three consecutive nucleotides encode the location of a particular amino acid in a protein molecule and each such triplet of bases is called a codon. Codons are translated into sequences of amino acids by ribosomes (which themselves consist of proteins and rRNA), tRNA, and helper proteins called translation factors.

The term “transcriptome” is widely used to designate the complete set of all the ribonucleic acid (RNA) molecules in a cell, tissue, or organism. The transcriptome reflects the molecular activity in cells, the genes that are actively expressed at any given moment. It encompasses all forms of RNAs molecules including protein coding, non-protein coding, alternatively spliced, alternatively polyadenylated, alternatively initiated, sense, antisense, and RNA-edited transcripts. Respectively, transcriptomics studies all types of transcripts within a cell or an organism, including mRNAs, miRNAs and different types of long noncoding RNAs (lncRNAs). Transcriptomics covers everything relating to RNAs such as their transcription and expression levels, functions, locations, trafficking, and degradation. It also includes the structures of transcripts and their parent genes with regard to start sites, 5′ and 3′ end sequences, splicing patterns, and posttranscriptional modifications.

The major studies of transcriptomics are aimed at:

• characterization of different states of cells (i.e. development stages), tissues or cell cycle phases by expression patterns;
• investigation of the molecular mechanisms underlying a phenotype;
• identifications of biomarkers differently expressed between the diseased state and healthy state;
• differentiation of disease stages or subtypes (e.g. cancer stages);
• setting up the causative relationship between genetic variants and gene expression patterns to illuminate the etiology of diseases.

During the last three decades the technological advance has revolutionized transcriptome profiling and redefined what is possible to investigate. Integration of transcriptomic data with other omics is giving an increasingly integrated view of cellular complexities facilitating holistic approaches to biomedical research.

The major techniques applied for transcriptome study are:

• Expressed sequence tag (EST)-based methods
• SAGE
• Hybridization-based microarray
• Real-time PCR
• NGS-based RNA-sequencing (RNA-seq) methods,
• RNA interference
• Bioinformatics tools for transcriptomes analysis.

The selection of the technique is dependent on cost effectiveness, sensitivity, high throughput, and minimal concentration of starting RNA. The methodology involves RNA isolation, purification, quantification, cDNA library construction, and high-throughput sequencing.

• Expressed sequence tag (EST)-based methods – Expressed sequence tags (ESTs) are relatively short DNA sequences (usually 200–300 nucleotides) usually generated from the 3′ ends of cDNA clones from which PCR primers can be derived and used to detect the presence of the specific coding sequence in genomic DNA. The sequencing of the ESTs gives an overview on the expression level of the gene. As more and more EST data have become publicly available, the usage of ESTs has expanded to other areas, such as in silico genetic marker discovery, in silico gene discovery, construction of gene models, alternative splicing prediction, genome annotation, expression profiling, and comparative genomics. In comparison with whole genome sequencing, EST technology is simpler and less costly, especially in the case of large genomes. There are EST databases such as dbEST (NCBI EST) (https://www.ncbi.nlm.nih.gov/genbank/dbest/) that contain sequence data and other information on “single-pass” cDNA sequences, or “Expressed Sequence Tags”, from a number of organisms and thus serve as a reference for the expression profile of an organism.
• Serial Analysis of Gene Expression (SAGE) is a transcriptomic technique used by scientists to produce a snapshot of the messenger RNA population in a sample of interest in the form of small tags that correspond to fragments of those transcripts. This technique is advantageous over EST because only short “tags” of about only 15 bases are sequenced. The short fragments generated are then joined together and sequenced. A pool of cDNA can be subjected to high-throughput NGS known as RNA-seq for quantification, discovery of novel ESTs, and profiling of RNAs.
• DNA chip, gene chip, biochip, or microarray is a collection of DNA, cDNA, oligonucleotides spots attached to a solid support such as glass or silicon chip. Through this hybridization-based method the expression levels of thousands of genes are monitored simultaneously. The major restriction of this technology is that genome sequence information is a prerequisite and also higher background inherent of hybridization technique.
• Quantitative real-time PCR (qRT-PCR) is a type of PCR for reliable quantification of low-abundance mRNA or low-copy transcripts. The major advantages of this technique are its high sensitivity, better reproducibility, and wide dynamic quantification range. It facilitates gene expressions and regulation studies even in a single cell based on its exponential amplification ability. The availability of diverse types of fluorescence monitoring system attached with the PCR resulted in its popularity for gene-expression studies. The problems of nonspecific amplification, formation of primer-dimers are some of the limitations of qRT-PCR.
• RNA-sequencing is one of the advanced high-throughput technology for transcriptomics. RNA-seq, also known as whole-transcriptome shotgun sequencing, utilizes NGS tools. The advantages of RNA-seq are that it does not rely on the availability of the genome sequence, has no upper quantification limits, shows high reproducibility, and possesses a large dynamic detection range.

The progress of transcriptomics led to a number of biological discoveries. This omic technology laid the foundation of the first real multi-omic studies, based on the comparisons between DNA sequence and mRNA expression. Nowadays, transcriptional analysis remains more frequently employed by most biologists since the data obtained are still more easily analyzed and shared than the more ‘downstream omics’ such as proteomics and metabolomics. More recently, transcriptomics is enjoying a second revival, as it is in many cases applicable to single cells.

Proteomics

Proteins command cellular structure and activity, provide the mechanisms for signaling between cells and tissues, and catalyze chemical reactions that support metabolism. The protein structure dictates the function (or dysfunction). Proteins can be the base cause of diseases (such as Alzheimer’s or Huntington’s disease), and they can be used to cure it (e.g., antibodies are used as therapeutics against viral and bacterial infections). The set of all expressed proteins in a biological system under a specific, defined conditions is known as proteome. The word “proteome” is a combination of protein and genome and was coined by Mark Wilkins in 1994.

The main aim of proteomics is a large-scale experimental analysis of the structure and function of this entire set of proteins produced by a living organism. The term “proteomics” first appeared in 1997 and referring to a core technology in systems biology approaches which studies the proteome. The function of cells is dependent on the proteins that are present in the intra- and intercellular space and their abundance. The synthesis of proteins in the cell is based on mRNA precursors but it is impossible to predict the abundance of specific proteins only based on analysis of gene expression. The reason for this is that native proteins undergo post-translational modifications (PTMs) or alterations in response to changes in the environment. The proteome is a dynamic reflection of both genes and the environment and is a valuable source for biomarker`s discovery because proteins are most likely to be ubiquitously affected in disease and disease response. The complete characterization of all proteins has been the goal of proteomics since its initiation almost 25 years ago. It tends to do more than merely identify proteins potentially present in a sample, but also to assess protein abundance, localization, posttranslational modifications, isoforms, and molecular interactions. Proteomics aims to study the flow of biological information through protein pathways and networks, with the eventual aim of understanding the functional relevance of proteins. This requires the development of technologies that can detect a wide range of proteins in samples from different origins. Various technologies are used in proteomics and most often they are applied in combination, for example one- or two-dimensional gel electrophoresis with mass spectrometry (MS) or liquid chromatography and MS.

The conventional techniques for isolation and purification of proteins are chromatography based such as ion exchange chromatography (IEC), hydroxyapatite, size exclusion chromatography (SEC) and affinity chromatography. For analysis of selective proteins, enzyme-linked immunosorbent assay (ELISA) and western blotting can be used. These techniques may be applied for analysis of individual proteins but they cannot define protein expression level. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), two-dimensional gel electrophoresis (2-DE) and two-dimensional differential gel electrophoresis (2D-DIGE) techniques are used for separation of complex protein samples.

• Mass spectrometry (MS) enables the analysis of proteomes and usually is the preferred method for identifying proteins present in biological systems. The three primary applications of MS to proteomics are: cataloging protein expression, defining protein interactions, and identifying sites of protein modification. Mass spectrometry measures the mass-to-charge ratio (m/z) of gas-phase ions. Mass spectrometers consist of an ion source that converts analyte molecules into gas-phase ions, a mass analyzer that separates ionized analytes based on m/z ratio, and a detector that records the number of ions at each m/z value. The development of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI), the two soft ionization techniques capable of ionizing peptides or proteins, revolutionized protein analysis using MS. The mass analyzer is central to MS technology. For proteomics research, four types of mass analyzers are commonly used: quadrupole (Q), ion trap (quadrupole ion trap, QIT; linear ion trap, LIT or LTQ), time-of-flight (TOF) mass analyzer, and Fourier-transform ion cyclotron resonance (FTICR) mass analyzer. They vary in their physical principles and analytical performance. “Hybrid” instruments have been designed to combine the capabilities of different mass analyzers and include the Q-Q-Q, Q-Q-LIT, Q-TOF, TOF-TOF, and LTQ-FTICR.
• Tandem mass spectrometry is additional experimental procedure which is also known as MS/MS or MS2. This is a key technique for protein or peptide sequencing and PTM analysis where two or more mass analyzer are coupled. The molecules of a given sample are ionized and the first spectrometer (designated MS1) separates these ions by their mass-to-charge ratio (often given as m/z or m/Q). Ions of a particular m/z-ratio coming from MS1 are selected and then made to split into smaller fragment ions, e.g. by collision-induced dissociation (CID), electron-capture dissociation (ECD), electron-transfer dissociation (ETD), ion-molecule reaction, or photo dissociation. These fragments are then introduced into the second mass spectrometer (MS2), which in turn separates the fragments by their m/z-ratio and detects them. The fragmentation step makes it possible to identify and separate ions that have very similar m/z-ratios in regular mass spectrometers. The complexity of the biological systems requires that the proteome be separated before analysis. Both gel chromatography- and liquid chromatography-based separations have proven useful in this regard. Typically, after these extensive separations, proteins are characterized by MS analysis of either intact protein (top–down) or enzymatically digested protein peptides (bottom–up). Protein identifications are made by comparing measured masses of intact proteins (top–down) or digested protein peptides (bottom–up) to calculated masses obtained from genome data.
• Isobaric Taq Labelling (ITL) is a commonly used method for quantification of proteins. In proteomics the quantification of the protein abundance is an important focus. Protein expression levels represent the balance between translation and degradation of proteins in cells. It is therefore assumed that the abundance of a specific protein is related to its role in cell function. ITL provides opportunity for simultaneous identification and quantification of proteins from multiple samples in a single analysis. To measure proteins` quantity in a given sample, peptides are labeled with chemical tags that have the same structure and nominal mass, but vary in the distribution of heavy isotopes in their structure. These tags, commonly referred to as tandem mass tags, are designed so that the mass tag is cleaved at a specific linker region upon higher-energy collisional-induced dissociation (HCD) during tandem mass spectrometry yielding reporter ions of different masses. Protein quantitation is accomplished by comparing the intensities of the reporter ions in the MS/MS spectra. Other quantitative techniques are ICAT labelling and Stable Isotopic Labeling with Amino Acids in Cell Culture (SILAC). The ICAT has also expanded the range of proteins that can be analyzed and permits the accurate quantification and sequence identification of proteins from complex mixtures. SILAC is an MS-based approach for quantitative proteomics that depends on metabolic labeling of whole cellular proteome. The proteomes of different cells grown in cell culture are labeled with “light” or “heavy” form of amino acids and differentiated through MS.
• X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy are two major high-throughput techniques that provide three-dimensional (3D) structure of protein that might be helpful to understand its biological function. With the support of high-throughput technologies, a huge volume of proteomics data is collected. Bioinformatics databases are established to handle enormous quantity of data and its storage. Various bioinformatics tools are developed for 3D structure prediction, protein domain and motif analysis, rapid analysis of protein–protein interaction and data analysis of MS. The alignment tools are helpful for sequence and structure alignment to discover the evolutionary relationship. Proteome analysis provides the complete depiction of structural and functional information of cell as well as the response mechanism of cell against various types of stress and drugs using single or multiple proteomics techniques.

The major techniques used in the proteomics research are given in the Fig. 5.

Metabolomics

The metabolome was first described by Oliver and colleagues in 1998, during their pioneering work on yeast metabolism, as the complete complement of small molecules in a biological system or fluid. It includes all the small molecules such as lipids, amino acids, fatty acids, carbohydrates and vitamins which are known as metabolites and are produced as a result of cellular metabolism. They participate in the metabolic processes in the cells by interacting with other biological molecules following metabolic pathways. The metabolites` status in the biological systems is highly variable and time-dependent and changes in levels of key metabolites are observed due to the genetic, environmental, nutritional and other factors.

Metabolomics (metabolome analysis) is the most recently introduced strategy among the Omics that systematically identify and quantify the metabolites present in a cell, tissue, organ, biofluids, or organism at a specific point of time. Methods used in metabolomics aim to measure low molecular weight compounds (metabolites) with different physical characteristics such as compound polarity, functional groups and structural similarity. On the basis of these properties the metabolome is divided into subsets of various metabolites and for their investigation analytical procedures optimized for each type of molecule is applied in metabolomics.

The metabolites are products of biochemical reactions which are carried out with the participation of proteins of the proteome (Fig. 6). Their concentrations are dependent on the interactions of other processes (transcription, translation, cellular signaling, etc.), they have been investigated as reporters for metabolism in PD. This in turn determines the biological structure and function of the final phenotype of the organism. Therefore, changes in gene expression, proteins` function and the environment directly affect the metabolites` concentration in a biological system. The metabolome is composed of a relatively small number (around 5000), but different types of metabolites which make the metabolome more physically and chemically complex than genome, transcriptome and proteome. Moreover, the genome, transcriptome and proteome consist of compounds which are part of the metabolome. In addition, the components of the metabolome are highly conserved between organisms in comparison to genome, transcriptome and proteome which is why it is considered that the metabolome is evolutionary the oldest part of the cell.

Metabolomics is the most appropriate strategy for determination of disease-associated biomarkers. Comprehensive study of metabolites is a desirable tool for diagnosing disease, identifying new therapeutic targets, and enabling appropriate treatments. The analyses of small numbers of metabolites are used in diseases` diagnostics for decades, for example, the development of blood glucose test strips in the 1950s to test for diabetes or quantifying phenylalanine in newborns to screen for phenylketonuria.

Metabolomics analyses can be grouped into two main approaches: targeted and untargeted metabolomics. In targeted analysis the metabolites which are determined are known and represent specific pathway(s) or class(es) of molecules. On the contrary, the untargeted metabolomics aims to quantify and identify as many metabolites as possible. Targeted metabolomics refers to absolute quantification (nM or mg/ml) and uses an internal standard and semi quantitative or quantitative analysis to detect known compounds related to specific pathways. The untargeted approach measures all the present metabolites in a sample and apply relative quantification (fold change) and comparison between samples. The key role that metabolomics plays in multi-omics integration and system modelling is due to the fact that it can be quantitative. Systems modeling cannot be performed without accurate values or accurate concentrations as inputs and, likewise, systems models cannot be easily verified without accurate, quantitative concentrations as outputs. Metabolomics can deliver both (quantitative input and output data), making it extremely valuable to systems modelers.

Due to the metabolome`s enormous chemical complexity, it cannot be studied comprehensively by a single technology. The earliest application of metabolomics dates back to 1970s when gas chromatography–mass spectrometry was used for metabolite profiling of clinical urine samples. Nicholson and others in the 1980s made a profile of clinical samples by the application of nuclear magnetic resonance spectroscopy. Mass spectrometry coupled with both liquid chromatography and capillary electrophoresis are also applied in metabolomics to complement the analytical techniques available. Some of the analytical instruments utilized for the purposes of metabolomics research are being applied frequently and routinely while others have specific roles and are applied less frequently.

• Gas chromatography – Mass spectrometry (GC-MS) – Among the commonly used methods in metabolomics is GC-MS. As the name implies, GC-MS unifies two techniques to form a single method for analyzing mixtures of chemicals. Gas chromatography separates the components of a mixture and mass spectroscopy characterizes each of the components individually. By combining the two techniques, each metabolite in a sample can be both qualitatively and quantitatively evaluate. This chromatographic technique is used to study metabolites which have a low boiling point and which will be present in the gas phase at the temperature range 50-350°C. These metabolites can have a low boiling point in their biologically native form or the boiling point of a metabolite can be decreased through a chemical alteration, also known as chemical derivatisation. The sample containing metabolites is introduced (injected) into a mobile phase, which in the gas chromatography is an inert gas such as helium. The mobile phase carries the sample mixture through what is referred to as a stationary phase. The metabolites are separated on the basis of their adsorption to the stationary phase. The stationary phase is usually contained in a glass or stainless steel column and represents a chemical that can selectively adsorb components in a sample mixture. By changing characteristics of the mobile phase and the stationary phase, different mixtures of chemicals can be separated. As the individual metabolites elute from the GC column, they enter the electron ionization (mass spec) detector. There, they are bombarded with a stream of high-energy electrons (70 eV) which causing them to break apart into fragments. These fragments can be large or small pieces of the original molecules. The mass spectrum obtained for a given chemical compound is basically the same every time. Therefore, the mass spectrum is a fingerprint for the molecule. This fingerprint can be used to dentify the compound.
• Liquid Chromatography-Mass Spectrometry (LC-MS) – The main difference between GC-MS and LC-MS is that in liquid chromatography (LC), the mobile phase is a solvent. The LC separates mixture of metabolites which are in liquid form, usually contains methanol, acetonitrile and water. By using different packing of columns (different stationary phase) with high efficiency small amount of complex mixture can be separated. The columns used in HPLC are consists of Octadecyl (C18), Octyl (C8), Cyano, Amino, Phenyl packing’s and generally their length is about 50mm to 300mm. The columns are used on the basis of nature of compounds to be separated. This liquid containing mixture of components is transferred into the ion source of mass spectrometer where the process of ionization takes place and droplets carry an excess of positive or negative electric charge are formed. The ionization could be achieved by the use of different types of ionization sources and interfaces. After ionization the ions are transferred into mass Analyser where the separation of ions are done according to their mass to charge (m/z) ratio.
• Capillary Electrophoresis-Mass Spectrometry (CE-MS) – this analytical technique is also called capillary zone electrophoresis-mass spectrometry. CE-MS involves the separation of ionic species in the liquid phase via the application of high voltages and is usually coupled to an electrospray mass spectrometry. Capillary electrophoresis separates metabolites based on their electrophoretic mobility in a liquid electrolyte solution operating in an electric field. Electrophoretic mobility is dependent on the charge and size of the metabolite and so separation of metabolites with different sizes and/or charges is possible. All metabolites have to be charged to allow any mobility to occur. In GC-MS, LC-MS and CE-MS there is a separation of metabolites before their detection with mass spectrometry. This peculiarity provides the ability to detect metabolites at low concentrations, commonly nanomoles/litre (nM/L) or micromoles/litre (µM/L).
• Nuclear magnetic resonance (NMR) spectroscopy is the most widely used analytical instruments in metabolomics research together with Mass spectrometry. NMR spectroscopy applies the magnetic properties of atomic nuclei in a metabolite. Only some atoms are NMR active and include 1H, 13C and 31P; proton (1H). The technique operates by placing a liquid sample in a small internal diameter tube (for example a 5 mm tube), or occasionally a piece of tissue is studied directly using a special sample holder. The sample is pulsed with a range of radio frequencies covering all possible energies required for exciting the selected type of nuclei. The nuclei absorb energy at different radio frequencies depending on their chemical environment and then the release of this energy is measured, forming a free induction decay (FID). This FID is converted from a time domain data set to a frequency domain – using a Fourier transformation – and an NMR spectrum is constructed as the chemical shift (effectively the absorption energy) plotted against peak intensity.
• Vibrational spectroscopy techniques such as Fourier transform–infrared (FT-IR) spectroscopy and Raman spectroscopy are applied for analysis of the metabolic changes in biological samples. The techniques` principle is based on the ultraviolet or infrared light transmission through a sample, or sometimes reflection of the light from the sample, before its detection. The approaches predominantly measure the vibrations and rotations of bonds related to different chemical functional groups resulting from the interaction of the sample with the ultraviolet or infrared light. These techniques usually lack the specificity to detect each metabolite separately but instead specific parts of the molecule absorb the ultraviolet or infrared light at specific wavelengths. For example, in FT-IR, C-H stretching vibrations characteristic of fatty acid chains are observed between the wavenumber range 3100 – 2800 cm-1 and the region between 1800 and 1500 cm-1 is dominated by amide I and amide II bands indicating the predominance of either alpha helix or beta sheet structures.

In the field of metabolomics, many hundreds of samples are routinely analyzed, and a minimum of several hundreds of metabolites are usually detected. The data obtained from the detection of metabolites in a given biological samples is followed by further analysis using statistical multivariate methods (chemometrics) to extract biological, physiological and clinically relevant information.

The implementation of omics technologies and the integration of omics data has been realized for a broad range of research areas, including food and nutrition science, systems microbiology, analysis of microbiomes, genotype–phenotype interactions, systems biology, natural product discovery and disease biology. Recently omics-based approaches have been significantly improved with the addition of novel concepts such as exposome/exposomics (role of the environment in human diseases), adductomics (study of compounds that bind DNA and cause damage and mutations), volatilomics (study of volatile organic compounds to the metabolomics/lipidomics analysis), nutrigenomics (study of how foods affect our genes), etc. However, omics technologies are aimed at primarily four omics research fields – genomics, transcriptomics, proteomics and metabolomics. The traditional research fields like genetics, pharmacogenetics and toxicogenetics which do not apply the omics concept, provide the static sequences of genes and proteins. On the contrary, the omics technologies provide simultaneously measurement of the number of proteins, genes, metabolites and enables to get large scale data in a short time. The collective analysis of the biological processes in a biological system provided by all omics is an advantage to previous traditional methods in better understanding of the whole picture of the biological system. Although large-scale omics data are becoming more accessible, the integration of real multi-omics data is still very challenging. This is due to fact that many of the specific analycal tools and experimental designs that are conventionally used for individual omics disciplines (genomics, transcriptomics, and proteomics) are not sufficiently well-suited to allow reliable comparisons and integration across multiple omics disciplines. For example, methods for sample collection and storage and the quantity and type of biological samples required in genomics studies are often not compatible with metabolomics, proteomics or transcriptomics. During collecting data, there are many problems such as data heterogeneity, small sample size in comparison to many parameters, confirmation and interpretation of data due to many interactions in biological system and deficient information on those systems. Almost in all omics experiments hundreds to thousands of target molecules and variables are measured (metabolites, proteins, genes, transcripts and SNPs). This requires the investigation of sufficient number of biological samples in aim to provide statistically reliable results. Moreover, the multi-omics data must be generated from the same set of samples to permit the direct comparison under the same conditions. However, this is not always possible due to limitations in sample biomass, sample access or financial resources. In single omics experiments, larger sample sizes are often required to overcome this problem. In addition, the integrated multi-omics data must be analyzed as single data sets before being deposited into omics-specific databases in order to make it publicly available. These issues emphasize that for the high-quality large scale-omics studies is necessary to : 1) plan an experiment properly, 2) collect, prepare, and store biological samples attentively, 3) collect quantitative multi-omics data and associated meta-data thoughtfully, 4) utilize proper tools for integration and interpretation of the data.

Some of the advantages and disadvantages of the main omics technologies are summarized in Table 1.

Multi-omics datasets can provide a greater depth of understanding in certain scenarios, but this is not without cost. These studies frequently are based on a large numbers of comparisons, correct data type, relevant statistical analyses, specific equipment and skilled personnel and considerable investment of time and money. When designing an experiment it must be taken into account what types of omics data can and should be integrated to obtain powerful biological insights of the system being studied. The application of high throughput omics platforms is not always necessary to answer the research question. The results obtained from the traditional techniques, such as quantitative polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), sometimes may be enough to provide understanding for biological mechanisms. These techniques often complement the findings from a larger omics study as they are applied to verify the significant molecule identified from omics data is a true positive result.