Chapter 5

Systems Biology and Holistic Concepts

Abstract

Systems biology is an important emerging approach, which considers the interdependence of cells, tissues, and organisms in complex, yet interconnected metabolic networks. The message from systems biology is that one should not consider any biological molecule, process, or phenomenon as parts in isolation but view them as interdependent parts of the whole organism. These concepts have startling similarities with the holistic concepts of Ayurveda and Yoga. This chapter recalls the basic concepts of Ayurveda, which explain the relationships between the microcosm and macrocosm. The authors present a comparative picture of principles of systems biology, and the holistic concepts of Ayurveda. This chapter provides a brief account of a few ongoing research projects studying the basic principles of Ayurveda and biology. The authors discuss how Ayurvedic concepts may open new research avenues for future biomedical research.

Keywords

Biological systems; Complexity; Holistic; Homeostasis; Metabolomics; Metabonomics; Networks; Reductionist; Whole systems

The cure of the part should not be attempted without the cure of the whole.

Plato

The Philosophical Basis to Systems Approach

In its preface, the latest edition of the most popular book, Guyton and Hall Text Book of Medical Physiology states: “Indeed, the human body is much more than the sum of its parts, and life relies upon this total function, not just on the function of individual body parts in isolation from the others.” This clearly indicates how the thinking in mainstream biology is moving toward holistic approaches. The new recognition of a systems approach in biology has significant impact on health, wellness, diseases, therapeutics, and future health care. The systems approach is in consonance with the basic concepts of Ayurveda and Yoga.
The philosophy of systems is inspired by nonlinear approaches, network analysis, cybernetics, and nonequilibrium dynamics of open systems [1]. Actually, the systems biology concept is not new. Erwin Schrödinger’s thermodynamic approach was based on systems thinking. Eminent physiologist, Claude Bernard, and the father of cybernetics, Norbert Wiener, among many others, also propounded or incorporated systems thinking in their work. Systems biology follows Hegel’s dialectical principles of development from thesis, to antithesis, to synthesis. Systems biology, in particular, deals with the higher level analysis of complex, biological systems. Through the reductionist approach, scientists have acquired detailed knowledge of biochemistry, genetics, and molecular biology. But in the process, biology of organisms was reduced to components like tissues, cells, genes, and molecules. Fragmentation of whole systems into parts is actually the antithesis to the thesis of biology. While a detailed understanding of these fragments is absolutely necessary, unlike in physics or mathematics, the sum total of these parts does not make the whole biological organism. This realization has given impetus to systems thinking in biology.
Systems biology as a new approach, emerged in 1966 at the international symposium at the Case Institute of Technology in Cleveland, Ohio; it was hailed as a new way of holism. The successes of molecular biology, functional genomics, and other omics technologies have further strengthened systems approaches. It is possible to design and synthesize proteins, lipids, and carbohydrate complexes as constituent parts of a cell. Yet, scientists and technologists have not been able to create even a single living cell, de novo. Our cells, tissues, organs, and bodies are not mathematical equations. Any living system such as a cell is an integrated, interdependent, and complex ecosystem where the parts and the whole both play important roles in design, sustenance, and growth.
Aristotelian logic followed the path of reductionism, and gradually science and philosophy were separated. During the Renaissance, science and medicine continued on the reductionist path, and attempted analytical evaluation of natural phenomenon based on the assumption that complex problems are solvable by dividing them into smaller, simpler, and manageable units. In this process, big objects are “reduced” to smaller basic units. Undoubtedly, this approach was rewarding, and many path-breaking inventions, discoveries, and technologies emerged to make our life more comfortable. Today, the reductionist approach dominates the biomedical sciences, and governs the way toward diagnosis, treatment, and prevention of diseases (Figure 5.1). As a result, medicine relies more on explanatory models of scientific positivism based on sensory experience as an exclusive source.
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Figure 5.1 Reductionist approach in modern medicine.
However, many scientists are now challenging linear thinking based on reductionism. They argue, based on complexity theory, that analytical and predictive powers can be enhanced with an understanding of the whole system, rather than limiting them as parts to narrow disciplines. This is very important, especially in the process of clinical evaluation when patient, disease, environment, and many other confounding factors need to be considered together. As rightly put by Sweeney and Kervik, “it is better at times to be vaguely right, rather than precisely wrong” [2].
While reductionism was progressing rapidly in the West, a few philosophers were thinking differently. For instance, during the seventeenth century, René Descartes proposed a theory of dualism; this triggered discussions related to the mind and brain. He distinguished the brain as the seat of the intellect, and the mind as having consciousness and self-awareness. The theory of duality made scientists think about the brain and the mind together, as interdependent entities. Such efforts have given some impetus to systems thinking. Of late, several limitations of reductionism have been realized. The systems biology perspective appreciates that holistic problems can be better addressed through the use of computational and mathematical tools.
Any system is constituted by interconnected parts, which form a unified whole. Nature presents an apt example of complex ecosystems with interdependent biotic and abiotic components. Various components of the ecosystem have their own existence, but they are also interdependent in terms of survival and sustenance. Biological systems are often complex, with several, multifunctional elements interacting selectively in a nonlinear fashion.
Chromosomes, genes, proteins, and biochemicals constitute a cell. Many cells together can form tissues, different tissues can form anatomical systems, and many such systems make an organism. The organism is not just an assembly of cells, tissues, and organs—it has a specific structure, hierarchy, and networks. Newer disciplines like omics sciences are making modern biology a “data-rich” science, and systems biology, by making a composite picture, tries to make sense of the whole. Superspecialization and therapeutic precision in modern medicine is target specific, linear, and rigid, and does not consider the dynamic context of time and space. Such reductionist and fragmented thinking have added limitations to modern medicine; a systems biology approach may offer an alternative model.
In simple terms, a complex ecosystem like a forest cannot be explained merely by studying individual trees or insects. At the same time, a butterfly cannot be seen from an airplane without the use of high-resolution telescopes. The morphology of cells cannot be known without the use of a microscope and details of Saturn in the galaxy cannot be seen without the use of a telescope (Figure 5.2). In fact, application of the tool as a microscope or a telescope much depends on the direction in which one is trying to see. Both the views are important.
Chemicals present in cytoplasm are not known without the use of powerful, chromatographic analytical tools. Thus, in reality the reductionist and holistic approaches are complementary, and not mutually exclusive. Systems biology has tried to bridge the gap between these two valuable approaches. In short, systems biology studies microscopic details of cells and the diversity of molecules without losing the sight of the whole organism. Systems biology is a science that allows us to see parts in the whole and whole in the parts.
Reductionism is helpful and useful in understanding the components of any system. It provides a detailed understanding of a part down to the finest details. It is a way of dividing complex problems into solvable smaller, simpler, and thus more tractable units. In the collection of pieces of knowledge, a reductionist approach is required in almost every science. However, the subsequent divisions lead to a loss of important information about the whole. Reductionism is a natural process of gaining knowledge in modern science. Holism is the ability to look at the whole picture. Reductionism and holism are the two sides of a coin. Modern science adopted the reductionist approach, which is based on rigorous experimentation. The earlier notion that considers reductionism as the only way to know any phenomenon is being reconsidered in the light of systems biology.
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Figure 5.2 Telescopic and microscopic approaches for studying ecosystems, both are looking at each other.
Richard Dawkins, famous evolutionary biologist once said, “Reductionism is one of those things, like sin, that is only mentioned by people who are against it.” The systems approach was helpful in overthrowing narrow thinking, and also changed the dogmas of modern medicine. Against the broader backdrop of systems understanding, the limitations of reductionism were more apparent. The debate on the utility and limitations of the reductionist approach has been discussed at various levels. For example, the Stanford Encyclopedia of Philosophy discusses “reductionism in biology.” It discusses reductionism at three levels: ontological, epistemological, and methodological [3]. We feel that reductionism at methodological levels is necessary, but unless it considers epistemological level, there is a danger of losing sight of the whole picture. The systems approaches are now reconnecting science to philosophy!

Biological Systems

Every living thing is made up of cells, which are the smallest part, or building block of any organism. Every cell survives in a particular environment, and performs its independent function. In 1665, English philosopher Robert Hooke published his observations of experiments using the microscope. He observed the cell structure of cork, and called the individual compartments cells. The Latin meaning of this term cell is “small room.” Later, in 1673, Antonie van Leeuwenhoek, the father of microbiology, with an improved microscope, observed living algal cells. These developments provided new insights in biology, and led to the birth of cell theory. Cell theory states that every organism is constituted of cells as its structural and functional units. Living cells reproduce and make similar cells.
The estimated number of total human cells ranges from 37.3 to 100   trillion [4]. Many similar cells can connect and organize to form tissues, which have unique structures and functions. There are four basic tissues: epithelial, connective, muscle, and nervous tissues. The aggregation of cells to form tissues has many advantages. Tissues display more properties than individual cells. A number of tissues may function together as organs, with specific structures and functions. The structures of organs have evolved to perform related functions. For example, the liver is a glandular organ, where all four basic tissues are involved. The liver secretes bile, metabolizes drugs, detoxifies unwanted substances, and controls the metabolism of protein, carbohydrate, and fat. Many organs work together in a coordinated fashion to function as a system. For example, the digestive system consists of the buccal cavity, tongue, esophagus, stomach, duodenum, small intestine, colon, and rectum—which are parts of the alimentary canal. In addition, organs like the liver, pancreas, and gall bladder, which support digestive processes, are also part of this system. Many systems together form an organism. Populations of similar organisms comprise a community and many communities together with animals, plants, and nonliving components make an ecosystem (Figure 5.3).
This is still not a complete story. As per other estimates, the human body is made up of about 10   trillion cells but it carries near 100   trillion microbial cells. Thus in terms of mere numbers microbes are 10 times more than human cells. These microbes are active partners in human body living in symbiotic system mostly in gut. This is known as the gut microbiome, which has become a new target of diagnosis and treatment of many diseases. Microbiome is very much a part of the human body and actively interacts with human cells, tissues, organs, and physiological systems.
Although conventional biological study stops at the organism level, medicine and social sciences consider the community as a unit for epidemiological attributes. The communities, and surrounding ecosystems, consisting of plant, animal, and nonliving geographical components, constitute an ecosystem. There are many ecosystems making up the biosphere [5]. The complex network of interdependent, living, and nonliving forms existing in natural harmony—including a wide range of objects from microscopic cells, to complex ecosystems—is known as the web of life.
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Figure 5.3 Whole organization of ecosystem from parts.

Omics for Studying Cells

During the past few decades, the work of several scientists has advanced the detailed understanding of cell structure, function, and the underlying biological processes. At the microlevel, the cell has amazing machinery. It contains many components, known as cell organelles. Each cell of the multicellular organism has a permeable membrane that surrounds a jellylike substance known as the cytoplasm, which contains various organelles such as the endoplasmic reticulum, Golgi bodies, lysosomes, mitochondria, ribosomes, and vacuoles. Plant cells have chloroplasts that are important in photosynthesis. The cell nucleus is the control center for the cellular functions. It contains deoxyribonucleic acid (DNA), a long, linear polymer arranged in a double helix structure. DNA is comprised of four chemical bases. They are arranged in pairs to form ladder-shaped, twisted DNA molecules. The two pairs of these four molecules are the purines, adenine (A) and thymine (T), and the pyrimidines, cytosine (C) and guanine (G). The threadlike coiled strands of DNA carrying genes are called chromosomes. The genes are units of inheritance that pass the traits to the offspring. Genes have “instructions,” that is, codes, to make specific proteins. The process known as gene expression produces the respective proteins. Proteins are complex macromolecules. The genetic makeup of the cell is the genotype, and its expressed, resultant, observable traits are phenotypes. Each gene has hundreds of base pairs (AT, GC). The maximum number of base pairs is estimated to be 2   million [6]. The exact number of human genes is a subject of debate; estimates range from 20,000 to 23,000 [7,8]. Thus, the DNA of every cell carries a huge cache of coded information. An organism’s full DNA sequence, that describes the order of genes in chromosomal sets, is known as the genome of the organism. Our understanding of information stored in DNA is now reshaping information science. Nature’s way of storing information in such a tiny biological unit is really amazing! Scientists from Harvard’s Wyss Institute have successfully stored 700 terabytes in 1   g of DNA [9]. Now, hard disks are used for storing information, but in the future, DNA-based disk drives will be used for information storage.
From DNA to cell, and from cells to organisms, there are various layers of biological organization. All these expanding levels are interrelated, and become more and more complex. The higher level has newer properties and controls than the lower levels. New properties emerge in expanding systems. For example, a tissue functions differently from the collective properties of its constituent cells. Aggregation of tissue leads to formation of organs, which have specific functions. Biological systems thus have emergent properties, which differentiates them from nonliving, engineered models. In such complex systems, physicochemical understanding of the lower levels may limit comprehension of the larger system. The linear, mathematical rules fail to describe the phenomenon of emergent properties. An understanding of expanding levels of biological organization requires an understanding of the holistic approach. Just looking at components in details is the reductionist view, which may describe the parts, but not necessarily the whole picture.
After the discovery of the microscope, with its ability to look at cells, as demonstrated by Robert Hooke in 1665, biological sciences took a major leap. This millennium has been dominated by several groundbreaking discoveries in cellular and molecular biology. Progress in analytical technology, coupled with computing and informatics, has resulted in generation and high-throughput data analysis. The human genome was decoded at the beginning of this millennium, which resulted in an explosion in genome data. Genomics emerged as a branch of genetics which studies the DNA sequences of various organisms. Ribonucleic acid (RNA) has important functions in coding, decoding, regulation, expression, and signaling. DNA sequences are translated into proteins. Since Robert Hooke first observed cells under the microscope, it took 300   years of rigorous research in cell and molecular biology to reach this level.
The term omics is used to denote powerful, or high-throughput technologies used to study various biological molecules on a large scale. Genomics is the study of whole DNA, and different genes. The DNA is copied into RNA for transcription. The set of all RNA molecules of an organism is the transcriptome, and its study is transcriptomics. Study of the structure and functions of proteins is proteomics—the study of the full set of proteins encoded by a genome. The various molecules produced in the metabolic processes are metabolites. The metabolites are the nutrition, structure, signaling, and regulatory functions of the organism. Metabolomics is a relatively new branch of science, which studies specific patterns of chemicals involved in the cellular processes of an organism. The large data sets emerging from omics sciences have enriched biology, and at the same time, have posed challenges of interpretation of huge amounts of information. Biology today is more than just a descriptive subject—it has become a data-rich science. Even a cursory look at the extent of the information generated in current biological sciences is mind-boggling.
The high-throughput studies of biological systems give rise to omics data of a gigantic scale. Six major omics of human data types constitute millions of components. As of May 2014, The National Center for Biotechnology Information gene database covers 47,758 human genes [10]. The transcriptomics data from protein-encoding, messenger RNA transcripts include 29,000 variants. Small, noncoding RNA molecules, also known as microRNA (miRNA), regulate transcription via gene silencing. The human genome might encode over 1000 miRNA. The proteomics database is composed of around 22,000–39,000 proteins, enzymes, receptors, and structural units of the cell. Phosphoproteomics data has 200,000 proteins, which are involved in cell signaling. The metabolomics database covers all small biomolecules in the cell, including lipids, nucleotides, dipeptides, and hormones. The number of all such metabolites is estimated to be over 40,000. In addition, epigenomic data suggests that nongenomic encoded DNA modifications may be as large as 20–30   million [11]. A typical gene chip, which is hardly the size of a coin, may carry up to 400,000 miniature reaction wells. Each reaction well might carry a DNA fragment with genetic information. The biological data is growing continuously, and developing a deeper understanding of parts poses more and more challenges of interpretation, relevance, and translation. This situation has given further impetus to the study of systems biology.
During the visualization and analysis of this data it is important to create a comprehensive biological insight without losing sense of intrinsic complexity. To interpret protein interaction, gene expression, and metabolic profile data, several visualization tools can be used [12]. High-throughput technologies require powerful computational support and new study models to manage the huge amount of information. In many cases, routine causality analysis might not be sufficient; the cause and effect relation might not be a linear phenomenon. Engineering and physical sciences also deal with complex circuits and networks. However, unlike machines and printed circuit boards, living organisms are not just an assembly of metabolites, proteins, nucleic acids, and genes.

Open Systems
Every biological entity is in a continuous, dynamic state, and one of the important properties of a living system is its ability to maintain homeostasis. Every biological entity continuously responds to changes in the surrounding environment and actively tries to maintain its metabolic equilibrium. The homeostatic mechanisms involve the regulation of several complex, biological processes. For example, temperature regulation involves skin, sweat glands, blood capillaries, thermoreceptors, autonomic nervous system, hypothalamus, water transport, sweating, vasodilatation, muscles, and muscle contraction. This explains how a small stimulus can affect large responses in linear and complex systems.

The Emergence of Systems Biology
Biological systems are subject to the process of evolution. For instance, the immune system as it exists today has evolved over millions of years. This capacity of adaptation, and the race for survival during the process of evolution, has given dynamic robustness to living organisms. Every organism tries to cope with the stress condition. Properties like coherent behavior, capacity of self-regulation, adaptation, and homeostasis make the living system flexible, and support their sustained existence. A physiological understanding, especially of prokaryotic animals, provides interesting insights into the modularity of components. Various components are arranged to perform specific functions. There are similar mechanisms which support each other as backup. The redundant mechanisms provide another advantage which make the systems more robust. Understanding the differences between engineered and living systems is crucial for effective management. In short, the reductionist approach of getting deeper and deeper understanding of parts, has led to rapid advances in the biological sciences. Moreover, the challenges of meaningfully managing the huge quantity of omics data have compelled scientists to rethink the whole picture. Systems biology has emerged as an attempt to strike a scientific balance between the two important and complementary approaches, namely, reductionist and holistic. Previously, these were considered to be alternative or exclusive approaches. The reductionist robustness of modern science and the holistic vision of the ancient sciences both find a place in the new systems biology approach.
The culture of holism in the East and concepts of reductionism in the Western world actually share a similar spirit, according to many experts and philosophers. In 1926, British military leader, and philosopher, Jan Smuts, proposed a concept of holism in his book, Holism and Evolution. He described holism as “the tendency in nature to form wholes that are greater than the sum of the parts through creative evolution.” Interestingly, although Smuts was a believer in holism, he was also a racist who favored discrimination between black and white people [14].
In 1969, Austrian biologist, Ludwig von Bertalanffy described physical systems as closed systems, and biological systems as open systems. According to von Bertalanffy, a closed system does not allow certain types of transfers through the system, while an open system continuously interacts with its environment or surroundings, where laws of thermodynamics might not be applicable. He proposed a systems theory for self-regulating systems. This theory defines systems as a configuration of parts connected together through a web of complex relationships. He further proposed the methodologies and applications for studying open systems, and stressed the importance of illustrating interaction between all related domains, rather than focusing on the stand-alone components. The systems theory was applied to various sectors like engineering, psychology, and biology.
The complex system, and its regulation, has been a curiosity for scientists for many years. In 1948, American mathematician, Norbert Wiener, proposed the concept of cybernetics. He defined it as “the scientific study of control and communication between the animals and the machines.” This concept tried to bridge the gap of micro- and macrolevels of big systems. Actually, systems theory, artificial intelligence, and cybernetics together seem to have led to complexity science during the mid-1960s. Gradually, the concepts of dynamic systems paved the way to new thinking on nonlinear systems, fractal geometry, and chaos theory [15].
The theory of complexity has progressed during the past five decades to encompass an understanding of self-organization, adaptive fuzzy logic, robotics, and network sciences. All these developments are the result of scientists’ efforts to try to understand unexplainable phenomenon from biological and computational sciences. Scientists were trying to connect the dots on a wide canvas of science. Today, many of these disciplines are converging, and new concepts like big data, cloud computing, global network society, and multilevel complex systems are evolving.
Interestingly, because of technological breakthroughs the distance between humans and machines is narrowing. Machines are becoming more and more intelligent, and the human being is becoming more and more mechanical. This realization has triggered a new interest in holistic approaches.
Concepts of holism, cybernetics, and systems theory are intermingled with information, technology, molecular biology, and the omics sciences, in general. This comprehensive approach is now known as systems biology. This approach focuses on an understanding of the whole through connecting the components generated as a result of reductionist approaches. The inputs from systems theory for transdisciplinary investigation, in the context of time and space, are crucial. Systems biology has enabled a nonlinear, dynamic, and holistic study, and has stimulated many new vistas in the biosciences.
The systems theory actually was most relevant for applications in the field of medicine. However, it quickly meshed with biology, and emerged as a new discipline of synthetic biology. These efforts went in dual directions: first, to understand the complexity of natural biological structures and processes and second, to use available technology to create these biological structures artificially.

The Living Cell as System
During the mid-1990s, biodegradable, encapsulated artificial red blood cells were developed and used for the treatment of a diabetic patient in a clinic [17]. Since then, genetically engineered cells are being studied for use in tissue regeneration.
In 2006, the United States National Science Foundation announced a grand challenge to build a mathematical model of the whole cell [18]. Craig Venter, one of the founders of Celera Genomics, which sequenced the human genome, made a successful attempt to use the synthetic genome to transfect a cell. In May 2010, Venter claimed to have created “synthetic life.” He synthesized the DNA of the genome of one bacteria, and introduced it into another cell [19]. In 2011, scientists from Harvard reported the creation of an artificial cell membrane [20].
In 2013, Venter also indicated that “scientists would soon be able to use 3D printers to create synthetic life, possibly even recreating alien genomes.” In 2014, a team of scientists from the United States, France, and India made the first artificial chromosome [21]. This discovery is celebrated as a landmark in synthetic biology, which will revolutionize the field of medical and industrial biotechnology.
These new-generation god-men also happen to be scientists. Their aspirations are sky high. However, in reality, what has been attempted so far cannot be called artificial life or an artificial organism. For an organism to qualify as artificial, all core elements should have been initially constructed from chemically simple, nonliving components. In all these efforts scientists have made some kind of a hybrid, where some part of the structure is natural and some part is synthetic. They have tried to transfect artificial components with natural components. Transfection is the process of introducing a genetic material into eukaryotic cell. Even the Craig Venter Institute admits that “it is not creating life from scratch, but an attempt to create new life from already existing life using synthetic DNA.” So far, scientists may have been able to create some new life structures, but they have no clue as to how to create life itself, making living creatures rather than life structures.
Earlier efforts in genetic engineering have been manipulative. With the advent of synthetic biology, genetic engineering is becoming more creative. Obviously, this has huge ethical and moral significance [22]. For instance, normal, natural DNA has four nucleotides (A, T, G, and C) and two base pairs. This double helix structure of DNA carries genetic code based on 64 codons (a specific sequence of three adjacent nucleotides), each one of which encodes for 1 of the 20 amino acids used in the synthesis of proteins. Now scientists have created cells with six nucleotides—DNA with three base pairs—which can have 216 codons capable of handling up to 172 amino acids [23]. This is like creating more and newer words if, for example, you have 40 alphabets instead of 26. One can only imagine what chaos the English language would suffer in such a scenario; this is rightly feared, and people are raising serious concerns. There is no satisfactory answer to the question: “Are we creating monsters?” In any case, such efforts certainly look overambitious, scary, and unethical.
Artificial cells may be useful as targeted drug delivery systems; liposomes in therapeutics can help the ecosystem by creating better biodegradability, or through their use in new biofuels. However, making any genetic modification in unknown areas must be done with extreme caution. When humanity is already facing so many problems related to ecological and environmental situations, as well as facing lifestyle, behavioral, and mental health challenges, investing so much effort in gray areas like artificial life might call for a better justification. The knowledge, techniques, and technologies could be better used to explore new applications of systems biology in integrative health care. Certainly humanity does not want ninja mutants and species such as those in movies to exist in real life.

Systems Medicine and Holistic Health

Systems biology concepts are relevant to health care and clinical practice. In Chapter 2 we discussed how the prevailing strategies to health care and medical care are more reactive than proactive. Medicine practice starts only when someone is sick or has a disease. Systems medicine has emerged as an interdisciplinary field, which considers the human body as part of an integrated, whole dynamic system involving biochemical, physiological, and environment interactions, which are responsible for health and homeostasis. Systems medicine considers body, mind, and spirit in a way quite similar to holistic health—but in light of their genomics, behavior, and the social environment. Knowing details of disease biology at the molecular level can strengthen the diagnoses, prognoses, and predictive nature of medicine. Alterations in physiological and pathological processes and modulations of metabolic networks can be understood better by studying profiles of proteins and metabolites. With the help of specific markers, which are as yet unknown, the development of diseases like diabetes, asthma, and cancer might be predicted.
Biological networks are dynamic, and constantly changing. Any experiment designed to study a system might just take a snapshot at a specific time. Splicing these snapshots together might allow us to see the dynamic and complex nature of biological networks. Systems behavior requires consideration of three important parameters: context, time, and space. The systems approach has provided a perspective useful in addressing network complexity challenges; it has made scientists rethink the reductionist approach and the linear articulation of “one gene, one risk, one disease.”
A disease can be diagnosed with the help of clinical examination, pathological investigations, diagnostic biomarkers, and imaging techniques. Sometimes, only diagnostic factors are considered, and the clinical picture might not accurately reflect the patient’s whole physical state. In such a case, doctors may treat the symptoms, instead of the patient. This approach is very mechanical. The focus is on regaining the acceptable levels of biomarkers, which becomes the only target. In this process, the underlying biological or pathological environment often takes a backseat. In reality, any abnormal finding needs to be assessed in the context of time and space. The systems approach suggests a totalistic view involving the behavior of the affected system, its linkage with other systems, and the patient as a whole. As a practical matter, consideration of the whole picture becomes challenging due to complexity. The analysis requires nonlinear, sensitive, and probability-based programs.
Systems biology is all set to change our present concepts and definitions. Health is not just a state, or stage of normality. Health is not necessarily obtained by reducing risks. Health is more about the capacity for robustness, and intention to adapt.
The reductionist approach is very powerful, but cannot be considered as the only solution. In the process of reductionism, any problem or object is divided into parts in order to study them in great detail. However, this may lead to a loss of crucial information about the whole. Moreover, it may ignore part-to-part dynamic interactions of system-wide behavior. By using reductionist methods, one can build an airplane or a computer by joining several well-defined parts in a well-defined sequence. This is a linear assembly line operation such as that which occurs in any automobile or appliances factory. Standard operating process and protocols help in doing the same process again and again with great precision. Such processes can be better handled by robots.
It is known that a reductionist approach does not work to build even a simple single cell. Today, cell biologists know most of the parts of a cell. They know how they are connected to each other. They are able to artificially prepare these parts in a laboratory. But still they have not been able to artificially create even a single cell. This is because, unlike physical systems, biological systems cannot be understood only by knowing all the details of the parts.
Contemporary physics and mathematics have limitations in their understanding of biology. Eminent scientists like Einstein and Schrödinger acknowledged these limitations. Today’s physics can address simpler and mechanical systems. However, as rightly pointed out by senior physiologist F.E. Yates from the University of California, Los Angeles, complex systems possess self-organizing capabilities known as homeokinetics. Homeodynamics is the biological application of homeokinetics, which helps in knowing patterns of energy flow, and their transformations in metabolic networks of living systems [24]. The concept of homeostasis is central to modern medicine. However, systems scientists feel that the term stasis needs to be reconsidered. Homeostasis is a dynamic activity, and not a static entity. Therefore, Biologists from Cardiff University Lloyd et al. proposed the term homeodynamics, which seems more appropriate [25]. Homeodynamics is related to regulatory mechanisms, feedback control, structural stability, redundancy, and range of adaptations of the systems in response to stress. Homeodynamics reflects the Ayurveda theory of Dosha much better than the concept of homeostatis.
According to current theory, an illness is the result of an imbalanced homeostatic mechanism. Treatment is aimed at regaining the balance by correcting deviations. This corrective treatment approach is applicable to a range of disorders from hypothyroidism, to hypokalemia, to hyperglycemia and diabetes. Correcting deviations to regain homeostasis is a typical reductionist perspective. Here, the emphasis is mainly on correcting the deviated parameter without a systems view. If one has elevated blood glucose—lower it; if one has high blood pressure—lower it; if one has high fever—reduce it; if one has low hemoglobin—increase it.
Fixing the symptom is required, but not sufficient. Such a reductive approach has limitations, and is even harmful. For example, in some conditions, calcium supplementation can cause adverse effects—even in a hypocalcemic state [26]. Sometimes, lowering elevated blood pressure can be harmful [27]. A selective focus on maintaining normal ranges also undermines the importance of dynamic stability and interactions between parts and systems. Biological systems are not merely collections of static components; rather, they are in dynamic, stable states, such as oscillatory or chaotic behavior. For example, circadian rhythms relate to oscillatory behavior [28], and complex heart rate variability relates to chaotic behavior [29]. Thus, homeostasis, or more appropriately, homeodynamic states should be considered during therapeutics; failing to do so may lead to ineffective, or even detrimental, outcomes. Limits to reductionism in medicine have been well discussed by Drs Russell Phillips and Andrew Ahn of the Harvard Medical School. According to them, present clinical medicine focuses on an understanding of parts and symptoms, while systems medicine considers the whole person in the context of time and space [30].
Against this background, it will be interesting to discuss a few examples from health and disease. By and large, the principles of systems biology can best be applied to multifactorial, chronic diseases such as metabolic syndrome, diabetes, coronary artery disease, arthritis, asthma, dementia, and Alzheimer disease, in which there is not a single causative factor, a single biomarker to diagnose, or a single target to treat. Importantly, in modern medicine these are considered as difficult-to-treat diseases.
Current treatments of such conditions are mainly targeted at disease symptoms, and are attempts to regain the normal status of the patient. Most of the time, the treatment is additive. Various drugs are added, based on various symptoms. For example, a patient with diabetes, hypertension, and dyslipidemia might be prescribed a combination consisting of oral antihyperglycemic drugs, antihypertensive drugs, and statins. Of late, a few companies have launched such commonly used combinations as polypills. However, this cannot be considered a holistic, system, or even a multitargeted approach. A polypill might provide convenience to the patient, but it is not a therapeutic innovation; rather, it is merely a mechanical combination.
The complex interactions between various etiopathological factors, and the dynamics of their interactions, cannot be understood well using only a reductionist approach. However, holistic or systems approaches can help to understand complex interrelationships between multiple factors, and so are better suited for difficult-to-treat chronic diseases. Most of these chronic diseases are polygenic, with lifestyle and psychosomatic etiologies. Most interestingly, traditional, complementary, and alternative medicine is the preferred choice of patients with these conditions.
On the other hand, many acute conditions like simple headache, fever, appendicitis, and infectious diseases like tetanus, diphtheria, diarrhea, and urinary tract infection are driven primarily by specific or single pathology making it easier for medical interventions. Thus, in short, reductionism can work for specific symptoms and problems with known causes, where a quick and effective treatment is possible, whereas a systems approach is suitable for the management of chronic and complex diseases.

The Example of Diabetes

Treatment of a complex disease such as diabetes is challenging because it is a multidimensional disorder. Many factors, including insulin resistance, inflammation, genetics, and lifestyle play a crucial role. Many markers like insulin and glucagon balance, peroxisome proliferator-activated receptor-γ, leptin, and cortisol, are involved, in addition to other parameters such as stress, diet, and obesity, in the pathogenesis of diabetes. The metabolomics of diabetes has become extremely complex.
Type I diabetes is supposedly caused by the inability of body to produce insulin. Type II diabetes (T2D) is considered to be the result of insulin resistance, when beta cells cannot produce enough insulin to counter insulin resistance. This situation leads to an increase in blood sugar, and can cause several complications which are mainly due to inflammatory changes that harm blood vessels, eyes, kidneys, and heart. Genetic predisposition and obesity have been considered as possible factors. However, a new hypothesis suggesting the behavior-mediated, evolutionary origin of insulin resistance has been proposed. The pathophysiology of T2D is hypothesized to originate from the brain and from behavior, rather than from diet and energy imbalance [31]. Recent research on genome-wide association studies indicate that insulin resistance can be triggered due to environmental factors, and genetically programmed beta cell dysfunction can result in diabetes [32].
Metabolic syndrome is known to affect over 24% of adult Americans. It is a premorbid condition which carries the risk of T2D and cardiovascular disease (CVD). The effective control of risk factors such as less physical activity and abdominal obesity can reduce the risk for T2D and CVD and prevent metabolic syndrome. A metabolic syndrome triad of obesity, diabetes, and coronary artery disease needs to be considered from a systems biology perspective. Today, it is known that only glycemic control is not sufficient to prevent all the complications of diabetes. A meta-analysis of 13 randomized controlled trials has shown that glucose-lowering treatment has no significant effect on all-cause mortality or death from cardiovascular causes in patients with T2D [33]. Thus, T2D might not only be about insulin and glucose homeostasis. In fact, a few critics are challenging the scientific rationale of using insulin in T2D, where the main problem is insulin resistance. Moreover, a majority of glucose uptake takes place in the brain, where the glucose transport mechanism is noninsulin dependent. Theoretically, due to the action of insulin-dependent glucose transport, administering more insulin in such a condition can push glucose into skeletal, muscle, liver, and adipose tissue. Obviously, an excess of glucose will lead to increased fat deposition, mainly in adipocytes, leading to obesity [34]. Actually, adipocyte- or fat-cell-specific insulin resistance might be good for health, while hepatic and brain insulin resistance might be deleterious. Different triggers for insulin resistance might affect different tissues in different ways. Insulin sensitivity and insulin secretion respond to a large number of signaling molecules including sex hormones, endorphins, myokines, and many others. A better understanding of the clinical progression of insulin resistance can help better clinical management of diabetes [35].
Thus, prescribing hypoglycemic or secretogogue drugs or insulin just to maintain blood sugar levels within limits might not be universally useful. Management of diabetes requires a comprehensive treatment. Here, the systems approach may be appropriate—it recognizes the presence and interplay of these complex factors in disease management. This approach is indeed very complicated. Yet, because of a deeper knowledge of underlying pathophysiological processes, and the availability of computational tools, it now seems to be possible. The medical community needs to be open to this holistic or systems understanding, and should develop the ability to appreciate the possible application of multitargeted, holistic, and integrative approaches. In attempting this approach it is necessary to keep in mind three crucial variables: time, space, and context.
Present diagnosis of diabetes relies on a measurement of fasting and postprandial glucose levels. However, these measurements are obtained at a single point in time. In such case, diagnosis is done after the beginning of the underlying abnormality.
To get a more accurate diagnosis, it is necessary to know glucose–insulin interdependent variability over a larger time frame. A whole body simulated model of the glucose–insulin–glucagon regulatory system has been developed to understand the pharmacokinetic and pharmacodynamic effects [36]. Still, time-sensitive interplay is not obtained through prevailing methods. It is now known that healthy individuals may show a pattern of periodic oscillations of insulin secretions, which can range from 6 to 10   min. It is also known that people with T2D have abnormal insulin oscillations. Impaired insulin oscillations can also be observed in first-degree relatives who may be metabolically normal [37]. This clearly suggests that time-sensitive, continuous evaluations might be better in detecting beta cell dysfunction. As we know, blood glucose levels are controlled by the interplay of insulin and glucagons, along with growth hormone and epinephrine. The glucose regulatory pathways are closely interconnected, and any dysfunction can affect the glucose/insulin dynamics. Therefore, time-sensitive measures might provide valuable information for diagnosis and treatment of T2D. Consideration of time-sensitive measures is not being done in today’s evidence-based, modern medicine.
The most common method of measuring blood glucose levels is using a glucometer with strips. If a strip reading from the right finger is equivalent to that from the left finger, it leads to the assumption that the distribution of glucose in the body is uniform. However, plasma glucose is known to show spatial differences [38], which normally are not considered in clinical practice. Similarly, insulin injections in the thigh are assumed to be as effective as those in the abdomen. However, insulin absorption and distribution is known to differ at different sites. Currently, the recognition of such spatial variations in treatment or diagnosis is lacking in clinical practice.
As the analytical techniques are becoming more sensitive, it is possible to ascertain the best sites for insulin injections. A better understanding of bodily glucose distribution can help to predict diabetes risk. We might need to know how and why certain foods modulate stimulation of beta islet cells.
Today’s medicine is more focused on symptoms and diseases rather than on the individual. In diabetes, the focus is on hyperglycemia, which is a symptom. Treatments are aimed at lowering the glucose levels. Interestingly, the systems approach to medicine might shift attention from the elevated glucose levels to the contextual milieu responsible for this. It could be genetics, behavior, lifestyle, dietary habits, sleeping patterns, immunity, and many such factors which all need to be integrated into the treatment. The individual is always at the central position in systems medicine.
This discussion may seem utopian to many modern medical practitioners. How can a diabetes patient not be given glucose-lowering drugs? The systems biology knowledge indicates that complex diseases might have different etiological processes, and might have different treatment options. Different processes do exist for specific diseases, and each process requires a different treatment. This perception also supports the personalized approaches in medicine. It is true that some patients with T2D might respond well to insulin therapy, while a few others might not; some might not require hypoglycemic drugs, but might benefit from lifestyle modification, diet planning, and meditation.
According to researchers from Harvard Medical School, Ahn et al., “the decision regarding appropriate treatments is possible through understanding of complex factors peculiar to each and every patient” [39]. This approach has a striking similarity to Ayurvedic diagnosis and treatment. Literally hundreds of variables are considered including the Prakriti of the patient and of the disease. An illustration of what happens when a patient visits a modern medical clinic and an Ayurveda clinic speaks volumes (Figure 5.4).
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Figure 5.4 The figure sketches diagnostic and treatment activities in typical clinics of modern medicine in Ayurveda.

The Future of Systems Medicine
The involvement of multiple cell types, tissues, and organs in complex diseases and syndromes necessitates the exploration of cross-tissue interactions. A recent study has shown that communications and signaling across multiple tissues—such as heart muscle, sympathetic nervous system, bone marrow, and spleen—are involved in the enhanced inflammatory response which induces atherosclerosis acceleration after myocardial infarction [41]. The recognition of such a relationship is possible only when organism-wide, systems-level data is integrated. Few studies related to cross-tissue networks have been reported, but this area certainly needs better, and more efficient methodologies. As we have discussed, most of the current methodologies capture static information—a snapshot of disease status.
Scientists at the Institute of Systems Biology (ISB) in Seattle are involved in developing new tests based on protein patterns in blood. This is expected to reveal the health status of every major organ in the body. With this knowledge, and systems understanding, early predictions of disease causation and progression are possible. Leroy Hood, founder of the ISB, has proposed a model known as P4 Medicine. The four ‘Ps’ include Predictive, Preventive, Personalized, and Participatory medicine. According to Hood, the systems approach can help in demystifying diseases and democratizing health care [42]. An ambitious project known as the 100K Wellness Project plans to study 100,000 individuals for 20 to 30   years. This project attempts to capture a variety of health-related data types when individuals are healthy, in a disease state, or in the transition between disease and wellness. Scientists will have an opportunity to closely study transitional states of common diseases such as cardiovascular diseases, cancer, and neurodegenerative disorders.
The 100K Wellness Project will carry out a series of investigations including whole genome sequencing to identify key genomic variants; detailed clinical chemistries; quantified self-measurements, including heart rate, respiration rate, quality of sleep, weight, blood pressure, and calories expended; gut microbiome; as well as organ-specific blood proteins from the brain, heart, and liver. This detailed profiling will help in the prognosis of very early stage, wellness-to-disease transitions, or vice versa. This will certainly have great importance in disease prevention, health, and wellness promotion.
The 100K project is an excellent example of how the principles of systems biology can be holistically applied to medicine. Of course, this might not be as simple as it looks. There will be difficulties in translating rapidly growing data into clinically usable knowledge for daily wellness, or at the bedside. Efforts to bridge the gap between systems biology and medicine call for collaborative research between scientists from the theoretical, experimental, computational, and clinical domains [43].
Dynamic models to capture the ever-changing nature of disease progression are needed. Only a comprehensive understanding of the whole body system in relation to its environment can lead to effective diagnostic, preventative, and therapeutic strategies—especially for disabling, deadly, and difficult-to-treat diseases [44].
The efforts of scientists in such ambitious projects can provide better insight into the future of systems biology and medicine. However, there is a danger of getting buried under the gigantic data sets where complexity will again supersede. This is what happened in the past when scientists were dreaming about genomic and molecular medicine. The hopes and aspirations created by the hype of omics technologies have not been turned into reality, especially in the field of drug discovery and molecular and personalized medicine. The smarter way is going back to traditional wisdom to get new insights, understandings, and leads so that we are able to disrupt the vicious cycle of technology–data–technology.
We feel that the integration of ancient experiential sciences like Ayurveda can help to expedite ambitious projects like 100K, P4 Medicine, Horizon 2020, P3 Medicine, person-centered medicine and whole person healing approaches. Against this background, it is interesting to review systems approaches in Ayurveda. Although some details are also available in the primer, information relevant to systems approach is discussed here for ready reference.

The Systems Approach of Ayurveda
Ayurvedic concepts of Pinda/microcosm and Brahmanda/macrocosm entities considers human being as a whole, involving body, mind, and spirit, and not merely a composite of cells, tissues, or organs. While doing so, it may not offer a detailed understanding of genes, molecules, cells, and biological processes. On the other hand, modern science and biology considers living or nonliving entities as parts in isolation and can offer a deeper understanding at the atomic, molecular, and cellular levels. However, in the process of getting deeper into parts, it loses sight of the whole. Therefore, the modern biological approach is considered reductionist, and Ayurveda, holistic. In the interconnected world it is becoming increasingly clear that assumptions based on limited understanding of parts might not be relevant or valid to the whole system. The whole need not be equivalent to the sum of the parts. This realization has given rise to systems thinking among modern scientists. Actually, the modern systems approach is an extended form of the traditional holistic approach; in reality, both are important.
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Figure 5.5 The structures and organization of macarocosm and microcosm look astonishingly similar. Microscopic structure of mouse neuron (left) and computerized model of the universe (right).
Against this background, it is important to take a closer look at the holistic or systems nature of Ayurveda and Yoga. A complex one-to-one, one-to-many, and many-to-many relationship diagram in the form of graphical notation describes the schema of the Ayurveda knowledge base. This logic chart, titled Systems Ayurveda, gives a comprehensive view of concepts and entities, including cause and effect relationships. The immense data consisting of over 200 variables, describing the logical flow and concepts of “Systems Ayurveda,” have been presented using the systems biology graphical notation (SBGN) approach for processes, entity relationships, and activity flow [45] (Figure 5.6). This chart broadly describes logical flow, forward loops, backward loops, and entity relationships, and their application to health and disease.
The foundation and logic of Ayurveda is mainly based on ancient Indian schools of philosophy. According to this philosophy, all matter is composed of five primordial elements. The manifested parts of matter are microcosm (the living cell) and macrocosm (universe); both could be assessed using similar logic and methods. The main aim of Ayurveda is to promote health and longevity and alleviate diseases. This is achieved through homeostasis, and the balancing of Dosha, Dhatu, Mala, and Agni. Appropriate conduct, including proper lifestyle and diet, leads to health. The Ayurveda approach enables augmentation of health-promoting properties and reducing the toxicity of medicines. The causative factors and disturbances in organs take several steps, which progress toward disease manifestation. The holistic approach of Ayurveda aims to establish health through multifarious treatment modalities, including counseling, diet advice, Panchakarma, herbal medicines, and surgical procedures.
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Figure 5.6 Conceptual foundation and logic of Ayurveda.
Any substance (Dravya) has some attributes (Guna) and actions (Karma). Ayurveda explains every action in terms of Dravya, Guna, and Karma. The description of the physiology, pathogenesis, and pharmacology of Ayurveda is based on this concept. For example, the drug Piper longum has a hot (ushna) property, which alleviates Kapha and aggravates Pitta (Figure 5.7).
The logic and ontology of Ayurveda is similar to systems markup language, or SBGN. It specifies various types of relationships and logical group(s) of components, along with their entity relationships. Dr Iris Bell and colleagues from the University of Arizona have proposed an interesting hypothesis that aptly captures the systems approach of Ayurveda. According to Bell et al., nonlinear dynamic complex approach and whole systems approach of medicine like Ayurveda do not necessarily treat a symptom directly but try to modulate the imbalance to regain health through dietary and lifestyle modifications in accordance with Prakriti [46].
In this context, it will be interesting to see how Ayurvedic clinical evaluation is done utilizing the whole systems approach—where the patient is at the center and in the context of the internal and external environment in relation to the disease (Figure 5.8).
Historically, many philosophers, scientists, and physicians have recognized the importance of the whole system. Plato and Aristotle, the pioneers of reductive logic, suggested that in life systems, the relationship of the whole and the parts—microcosm and macrocosm—cannot be undermined. After discovering more and more details about the parts, modern science once again realizes the need for the whole picture. This is even more relevant and crucial in modern medical health care. Systems biology is an effort to relink the part and whole understanding, without compromising the value of details and without losing the reality of the whole.
A quote from Charaka Samhita restates Plato’s admonition: “No knowledge is complete unless it is studied as a whole.”
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Figure 5.7 A drug Piper longum with Katu Rasa creates multiple systemic effects. It facilitates the process of bioregulation, depletes tissues, induces vasodilatation, causes sweating, and improves digestion.
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Figure 5.8 The figure explains systems approach of Ayurveda. The biological entities are open systems that interact continuously with the external environment. The bioregulatory mechanisms are represented as functions of three Dosha which govern nutrition, biotransformation, and excretion mechanisms.