We Consider Human Network Physiology and Medicine – The “Body Electric” – Part Deux

An illustration symbolising network physiology in medecine and the human organism integrated network, as a complex network with the Vitruvian man at its centre. The caption reads: "The human organism is an integrated network where complex physiological systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. A new field, Network Physiology, is needed to probe the network of interactions among diverse physiologic systems."

The Network Within Us

Everything is connected.  And so it is in the human body too.  Everything in the human body is connected.  No doubt that all your organs – heart, liver, lungs – work as one to keep you alive and as close as possible to a healthy state. 

New concepts and approaches derived from recent advances in the theory of Complex Networks have been applied to provide insights into physiological structure and function in human health and disease.  The question?

Exactly HOW are our organs connected?

You may remember this blog from the previous popular article: ‘We Glimpse at the Body Electric – An Introduction to the Physics of the Human Nervous System‘.  This is my part two of the body electric…  And here’s the lowdown.

So.  There’s US!  Wonderful and unique.  Every single one of us.  Human beings… OR… human machines?

Here comes the astonishing truth in all its elegant simplicity:

We have a skeleton made of metal (Yes, calcium IS a metal!), a variety of built-in sensors, chemical gauges, a pump and a central processor, and filing systems, an internal clock…  Wait, wait!!

To consider network physiology, we must start at the beginning.


The Theory of Complex Networks

A map showing the Worldwide air transportation complex network. Source: Phys.org
The Worldwide Air Transportation Network.  Each grey link resembles traffic of passengers between more than 1,000 airports worldwide; the entire network has more than 35,000 links.  The red lines represent the network’s skeleton, a tree-like structure of only 1,300 links that represents the core structure of the network.  Links in the skeleton are the most important connections in the network.  Source: Phys.org

complex network is a graph (network) with non-trivial topological features.  In other words, features that do not occur in simple networks such as lattices or random graphs, but often occur in graphs modelling real live systems.

The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks, such as computer networks and social networks.

In 2012, researchers discovered that very different complex networks – ranging from global air traffic to neural networks – share very similar backbones.  By stripping each network down to its essential nodes and links, they found each network possesses a skeleton and these skeletons share common features, much like vertebrates do.

Complex systems – such as the Internet, Facebook, the power grid, human consciousness, even a termite colony – generate complex behaviour.  A system’s structure emerges locally; it is not designed or planned.  Components of a network work together, interacting and influencing each other, driving the network’s evolution.


Body Systems

So HOW are our organs connected?

Scientists have looked from the genetic and sub-cellular level to inter-cellular interactions and communications across integrated organ systems.  New and little-explored areas of network science concern the following:

  • Structural and dynamical aspects of physiological systems that transcend time and space scales

  • Networks comprised of diverse dynamical systems

  • Role of time-dependent network interactions for emergent transitions in network topology and function

  • Structure-function dependence

  • Manipulation, control and global dynamics of networks

  • Information flow on network topology

  • Cascades of failure across systems

  • Networks of physiological networks


Graphs and diagrams showing the transitions in the Network of Physiological Interactions. Source: Ivanov (2012)
Transitions in the Network of Physiological Interactions (a) Dynamical network of interactions between physiological systems where ten network nodes represent six physiological systems-brain activity (EEG waves: δ, θ, α, σ and β), cardiac (HR), respiratory (Resp), chin muscle tone, leg and eye movements. (b) Transition in the interactions between physiological systems across sleep stages. The time delay between two pairs of signals, (top) α-brain waves and chin muscle tone, and (bottom) HR and eye movement, quantifies their physiological interaction: highly irregular behaviour (blue dots) during deep sleep is followed by a period of TDS during light sleep indicating a stable physiological interaction (red dots for the HR–eye and orange dots for the α–chin interaction). (c) Transitions between physiological states are associated with changes in network topology: snapshots over 30-s windows during a transition from deep sleep (dark grey) to light sleep (light grey). During deep sleep, the network consists mainly of brain–brain links. With transition to light sleep, links between other physiological systems (network nodes) emerge and the network becomes highly connected. The stable α–chin and HR–eye interactions during light sleep in (b) are shown by an orange and a red network link, respectively. (d) Physiological network connectivity for one subject during night sleep calculated in 30-s windows as the fraction (%) of present links out of all possible links (brain–brain links not included).  Source: Ivanov et al. 2012

The human organism is an integrated network where complex physiological systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network.  When you receive a shock, both your heartbeat and breathing speeds increase.  Failure of one organ can lead to a catastrophic failure in other organs, resulting in possible death.

To say that identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge, is an understatement.  But that is a challenge that Ivanov et al. 2012 chose to undertake: to develop a framework to probe interactions among diverse systems and identify a physiological network.  Their proposed system-wide integrative approach may facilitate the development of a brand new field of study – Network Physiology.

Each physiological state is characterized by a specific network structure, demonstrating a robust interplay between network topology and physiological function.  Across physiological states, the network undergoes topological transitions associated with fast reorganization of physiological interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations.

For example…


Heart Rate

An animated diagram showing the beating human heart.
The heart is a wonderful biological device, designed to pump blood efficiently around the body.

The heart is the principal organ of the circulatory system, and it is responsible for maintaining a healthy flow of blood around the body.  The human heart is a large four-chambered muscular bag, lying obliquely in the thoracic cavity.  The heart’s primary function, as part of the cardiovascular system, is the delivery of oxygen O2, nutrients and metabolic requirements to living cells throughout the body and the removal of carbon dioxide CO2 and other waste product.

The heart is capable of increasing its pumping activity when a greater blood supply is required.

When we breathe in, oxygen enters the lungs – main organ of the respiratory system.  Approximately 21% of the air we breathe is O2.  In order for this oxygen to be collected from the lungs, and for the carbon dioxide to be released, the blood has to be pumped around the lungs by the heart.

Cardiac output – a measure of the volume of blood pumped out of the heart in one minute – can be increased as required, by an increase in the heart rate or the stroke volume.  The heart rate is increased by a change in activity in the heart’s natural pacemaker – the ‘sinoatrial’ or SA node.  Stroke volume is directly correlated to venous return.

Changes in activity in the SA node may itself result from:

  • a reduction in tonic inhibition – a reduced rate of firing of the vagus (or parasympathetic) nerve,
  • an increase in sympathetic stimulation,
  • an increase in levels of circulating hormones: adrenalin and noradrenalin.

Changes in blood pressure are modulated by the baroreceptors in the carotid artery and the aorta.  Information leaves these sensors to the cardiac centres of the brain’s medulla, which in turn causes either an increase or a decrease in the heart rate of contraction.

Everyone’s heart rate fluctuates.  The average is around 60 beats per minute.  So, if you want to establish a sensible range for the population, you set it within 50 – 70 beats per minute.

In healthy subjects, the distribution of fluctuations can be described as a single function over a wide range of timescales.  There appears to be an underlying temporal structure to the heart-rate fluctuations in healthy people.

The time between any two pulses is related to the time between another two pulses – sometimes seconds, minutes or hours in the past!


According to Ivanov et al., these fluctuations are not what is commonly known as “noise“.  Those fluctuations are very structured.  Scaling functions and power laws also describe temporal structure.

Unique power laws could describe unique physiological states.


For example, not just being awake or asleep, but different stages of sleep: light sleep, deep sleep, REM (Rapid Eye Movement) sleep…

Diagrams showing the network connectivity across different sleep stages. Source: Ivanov (2012)
Group-averaged time delay stability (TDS) matrices and related networks of physiological interactions during different sleep stages: (a) wake; (b) REM sleep; (c) light sleep (LS); (d) deep sleep (DS). Matrix elements are obtained by quantifying the TDS for each pair of physiological systems after obtaining the weighted average of all subjects in the group: where Li indicates the total duration of a given sleep stage for subject i, and si is the total duration of TDS within Li for the considered pair of physiological signals. Colour code represents the average strength of interaction between systems quantified as the fraction of time (out of the total duration of a given sleep stage throughout the night) when TDS is observed. A network link between two systems is defined when their interaction is characterized by a TDS of greater than or equal to 7% (arrow), a threshold determined by surrogate analysis (see Methods). The physiological network exhibits transitions across sleep stages – lowest number of links during deep sleep (d), higher during REM (b), and highest during light sleep (c) and quiet wake (a) – a behaviour observed in the group-averaged network as well as for each subject.  Source: Ivanov et al. 2012

An asleep power law could even persist if someone is awake at an unusual time, like when you travel to a different time zone.  Actually, researchers found a correlation between a diminished heart-rate fluctuations in the “half awake” physiological states of early morning and diminished fluctuations in those of us with heart problems.

The result of which might in time lead to the discovery that an underlying mechanism could explain why most heart attacks happen in the morning.  This goes against the popular-held belief that unhealthy hearts exhibit the most irregularities.

Network topology also changes with sleep-stage transitions: from predominantly brain-brain links during deep sleep to a high number of brain-periphery and periphery-periphery links during light sleep and wake.


The Human Physiolome

A series of graphs illustrating human physiology network synchronicity.
Segments of synchronously recorded physiological signals including (a) brain EEG signal, (b) heart rate, (c) respiratory rate and (d) eye movement recording. Coordinated bursts of activities with a certain time delay are consistently observed across the output signals of organ systems. The red dashed lines highlight a train of four significant bursts. These indicate networked communications among the systems. Source: PhysicsWorld, February 2016

Network physiology poses many new questions and challenges for which we do not yet have the necessary analytic and theoretical framework.

But the challenges ought to generate a new type of “big data“.

Application of statistical physiology does not end at heart rate analysis.

A photograph of Plamen Ivanov. Source: DailyFreePress
Boston University physicist Plamen Ivanov’s research includes coordinating organ system functions and integrating them as a network. Source: PhysicsWorld

According to Plamen Ivanov, the human physiolome will contain “streams of continuously recorded, high frequency, synchronized physiological signals under different physiological states and clinical conditions” and will integrate more and more data-driven analytic approaches in clinical practice.

“In the future, this new big data will have a similar impact on science, medical practice and health care as the Human Genome Project has today.”

In July 2015, Plamen Ivanov was granted USD 1 million (£ 660, 000) by the W. M. Keck Foundation to develop the new field of network physiology.


What is the Interactome?

A graph showing the disease response and hormone subnetworks in the human body.
Disease response and hormone sub-networks Source: Musungu et al. 2015

Understanding the biological interactions within an organism is vital for the comprehension of its functions.

The interactome provides a large scale mapping of protein-protein interactions (PPIs).  Interactomes are genome-wide roadmaps of PPIs. 

Interactomes have been produced for human beings, Homo sapiens (Rual et al., 2005), yeast, the fruit fly, Drosophila melanogaster (Giot et al., 2003), and Arabidopsis thaliana, and they have become invaluable tools for generating and testing hypotheses.

In 2007, the first plant interactome was released (Geisler-Lee et al., 2007).  The predicted Arabidopsis thaliana interactome was based on orthologs – genes separated by speciation – of S. cerevisiae (Yu et al., 2008).

This predicted plant interactome successfully provided hypotheses for testing interactions, including those involving membrane proteins, which are otherwise difficult to elucidate using forward and reverse genetic approaches (Lalonde et al., 2010; Nejad et al., 2012).  Interactomes of model organisms, such as Arabidopsis thaliana and Saccharomyces cerevisiae, were built using high throughput experimental methodologies (Consortium, 2011).  However, predicted interactomes in species of agronomic importance, like Citrus sinensis, Oryza sativa, and Glycine max, have provided insights into disease resistance.

Interactomes allow for hypotheses to be generated with a posteriori and a priori knowledge of a biological system.  Although experiment-based interactomes for A. thaliana are now being made (Consortium, 2011; Chen et al., 2012), the predicted interactome still makes many useful predictions for interactions not yet found in the growing experimental dataset.

For instance, studies by Guo et al., 2009 which tackled the complexity of germination and the involvement of plant hormone pathways, found interacting partners of Rack1 (receptor for activated kinases1) from a candidate list of 88 partners using a predicted interactome.  Plant predicted interactomes have also aided in determining proteins involved in resistance to the destructive bacterial pathogen Huanglongbing in citrus (Martinelli et al., 2012, 2013), as well as to the soybean cyst nematode (SCN) in soybean (Lightfoot, 2014).

The human interactome is the whole spectrum of molecular interactions in the human cell, which can be used for disease identification and prevention.  These networks have been classified as scale-free, disassortative, small-world networks, having a high betweenness centrality.  The human interactome was used to link the differential expression of genes with protein interactions in the analysis of cancer tissues, allowing researchers to analyse the connectivity between known and novel targets.

Protein-protein interactions have been mapped, using proteins as nodes and their interactions between each other as links.  Such maps utilize databases, such as BioGRID and the Human Protein Reference Database.


Metabolic Networks

metabolic network encompasses the biochemical reactions in metabolic pathways, connecting two metabolites if they are in the same pathway.  Researchers have used databases such as those to map these networks.  Others networks include cell signalling networks, gene regulatory networks, and RNA networks.

Using interactome networks, one can discover and classify diseases, as well as develop treatments through knowledge of its associations and their role in the networks.  One observation is that diseases can be classified not by their principle phenotypes (pathophenotype), but by their disease module, which is a neighbourhood or group of components in the interactome that, if disrupted, results in a specific pathophenotype.

Disease modules can be used in a variety of ways, such as predicting disease genes that have not been discovered yet.  Therefore, network medicine looks to identify the disease module for a specific pathophenotype, using clustering algorithms.


Diseasome – The Human Disease Network

A diagram showing the Human Diseasome.
Graph Representation of the Human Disease Network. Two disease nodes (circles) are connected if they share a common genetic component according to disorder disease-gene associations listed in OMIM as of the year 2005.  Source: bfg.oxfordjournals.org

Human disease networks (HDN), or diseasome, are networks in which the nodes are diseases and links are associated cellular components that two diseases share.

The first-published human disease network looked at genes, finding that many of the disease associated genes are non-essential genes.  These are the genes that do not completely disrupt the network and are able to be passed down generations.

Metabolic disease networks (MDN), in which two diseases are connected by a shared metabolite or metabolic pathway, have been extensively studied and are especially relevant in the case of metabolic disorders.

Three representations of the diseasome are:

  • Shared gene formalism, which states that if a gene is linked to two different disease phenotypes, then the two diseases are likely to have a common genetic origin (genetic disorders).

  • Shared metabolic pathway formalism, which states that if a metabolic pathway is linked to two different diseases, then the two diseases likely have a shared metabolic origin (metabolic disorders).

  • Disease co-morbidity formalism, which uses phenotypic disease networks (PDN), where two diseases are linked if the observed co-morbidity between their phenotypes exceeds a predefined threshold.  This does not look at the mechanism of action of diseases, but captures disease progression and how highly connected diseases correlate to higher mortality rates.

Some disease networks connect diseases to associated factors outside the human cell.  Networks of environmental and genetic etiological factors linked with shared diseases, called the “etiome“, can be also used to assess the clustering of environmental factors in these networks and understand the role of the environment on the interactome.

The human symptom-disease network (HSDN), published in June 2014, showed that the symptoms of disease and disease-associated cellular components were strongly correlated and that diseases of the same categories tend to form highly connected communities, with respect to their symptoms.



Activity pattern modalities of neuronal ensembles are determined by node properties, as well as network structure.  For all intents and purposes, it is of interest to be able to relate activity patterns to either node properties or network properties (or a combination of both).

In physiological neural networks, researchers observed bursting on a coarse-grained time and space scale.  A proper decision on whether bursts are the consequence of individual neurons with an inherent bursting property or whether a genuine network effect is taking place has generally not been possible to determine because of the noise in these systems.  By linking different orders of time and space scales, Ivanov et al., 2012 provide a simple coarse-grained criterion for deciding this question.

All of this to say that…

Scientists are opening the door to a new Maths-driven field of Medicine.  One day, we could well imagine people can monitor their health the same way they would check the weather on their phones.

So…  Human beings or human machines?