Apr 21 2013

Remembering the Memorable

One of the features of any network is the appearance of motifs, patterns that recur within a network much more often than expected via any sort of random occurence. The first four notes of Beethoven’s Fifth Symphony are an example of a motif: This opening phrase is one of the most widely recognized in music. It has mystified musicians, historians and philosophers for 200 years. Music critic Matthew Guerrieri says it’s “short enough to remember and portentous enough to be memorable.”

These small circuits can be considered as simple building blocks from which the network is composed. This analogy is quite useful, since many of these motifs would appear to have their corollaries in electronic circuitry. Motifs appear to play an important role in transcription factor regulation, providing the simple computational elements that function as integrated wholes to produce complex algorithmic outcomes.

Like the digital circuitry in your computer, clusters of network motifs are capable of computational processes. Think about this: Humans share about 98% of their genome (at least the sequences) with apes. This of course begs the question ‘why are we not more similar?’ The answer is that while we share much of the base sequences, there are tremendous differences in the computational knowledge that acts upon these sequences, in particular the networks involved in transcription factor regulation: operons, regulons and modulons. It is the combinatoric wisdom that seems to differentiate between the classes of life forms. This is especially true with regard to gene regulatory elements, which lie within the 98% of the DNA that does not contain gene coding. Gene regulatory elements instruct genes as to when, where and at what levels to turn on or off. [1]

One of the most common and interesting motifs found in biological systems is known as a feed-forward loop. This motif is commonly found in many gene systems and organisms. The motif consists of three genes and three regulatory interactions. The target gene Z is regulated by 2 transcription factors X and Y and in addition TF Y is also regulated by transcription factor X. The target gene is usually operated on in a logical AND manner, in that it requires both inputs to be logically true (both X and Y are required for Z activation) in order to activate. Since each of the regulatory interactions may either be stimulatory or inhibitive there are possibly eight types of FFL motifs.

Feed-forward loops are classified as coherent or incoherent. A coherent feed-forward loop is distinguished by the fact that the final actions of transcription factors X and Y are symmetrical; i.e. they result in the same type of stimulus (stimulation-stimulation or inhibition-inhibition) at their termination, Z. Incoherent feed-forward loops result in differing signals (stimulus-inhibition) at their termination.

ffl

The coherent type 1 feed-forward loop (C1-FFL) with an AND gate was shown to have a function of a ‘sign-sensitive delay’ element and a persistence detector both theoretically and experimentally with the arabinose system of E. coli. [2] This means that this motif can provide pulse filtration in which short pulses of signal will not generate a response but persistent signals will generate a response after short delay. The shut off of the output when a persistent pulse is ended will be fast.

The incoherent type 1 feed-forward loop (I1-FFL) is a pulse generator and response accelerator. The two signal pathways of the I1-FFL act in opposite directions where one pathway activates Z and the other represses it. When the repression is complete this leads to a pulse-like dynamics. I1-FFL is a pulse generator and response accelerator. [3] In some cases the same regulators X and Y regulate several Z genes of the same system. This is known as a multi-output feed-forward loop. By adjusting the strength of the interactions this motif was shown to determine the temporal order of gene activation. [4]

Other common motifs include auto-regulation, single input modules and dense overlapping regulons (DOR). Negative auto-regulation (NAR) occurs when a transcription factor represses its own transcription. This motif was shown to perform two important functions. The first function is response acceleration. NAR was shown to speed-up the response to signals both theoretically and experimentally. The second function is increased stability of the auto-regulated gene product concentration against stochastic noise, thus reducing variations in protein levels between different cells. [5] Positive auto-regulation (PAR) occurs when a transcription factor enhances its own rate of production. Opposite to the NAR motif this motif slows the response time compared to simple regulation. In the case of a strong PAR the motif may lead to a bimodal distribution of protein levels in cell populations. [6] The single input module (SIM) motif occurs when a single regulator regulates a set of genes with no additional regulation. This is useful when the genes are cooperatively carrying out a specific function and therefore always need to be activated in a synchronized manner. In the dense overlapping regulon motif, many inputs regulate many outputs. This motif occurs in the case that several regulators combinatorially control a set of genes with diverse regulatory combinations. This motif was found in E. coli in various systems such as carbon utilization, anaerobic growth, stress response and others. [4]

Coding for motif detection in Datapunk QUODLIBET was quit challenging. Although there are many helpful modules in the CPAN archives, there exists no simple module for FFL motif detection in network graphs. However, with experimentation I was able to work out a simple algorithm. Any metabolic map can display FFLs (if there are any in them) by clicking the FFL icon in the floating tool bar (indicated by red arrow).

ffl2

Here we see a Type 2 Coherent FFL motif in the Datapunk Adipocytokine map. Suppressor of cytokine signaling (SOCS) proteins are key regulators of immune responses and exert their effects in a classic negative-feedback loop. SOCS3 is transiently expressed by multiple cell lineages within the immune system and functions predominantly as a negative regulator of the leptin receptor and downstream cytokines that activate Janus Kinase 2.

To me, looking for basic circuitry motifs in metabolic maps (and understanding their function and significance) is equivalent to a composer who hears an entire symphonic arrangement in his head while reading a musical score, while the rest of us mere mortals just see black dots on white paper.

FOOTNOTES

1. Davidson E. The Regulatory Genome. Academic Press. (2006)
2. Mangan S, Zaslaver A, Alon U. The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks.J Mol Biol. 2003 Nov 21;334(2):197-204
3. Mangan S, Itzkovitz S, Zaslaver A, Alon U.The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli. J Mol Biol. 2006 Mar 10;356(5):1073-81. Epub 2005 Dec 19.
4. Alon U. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman and Hall/ CRC. New York, NY (2007)
5. Rosenfeld N, Elowitz MB, Alon U. Negative autoregulation speeds the response times of transcription networks. J. Mol. Biol. 323 (5): 785–93. (November 2002)
6. Maeda YT, Sano M. Regulatory dynamics of synthetic gene networks with positive feedback. J. Mol. Biol. 359 (4): 1107–24. (June 2006)

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Jun 02 2012

Morphometrics


“There are two possible outcomes: if the result confirms the hypothesis, then you’ve made a measurement. If the result is contrary to the hypothesis, then you’ve made a discovery. —Enrico Fermi


A few months after his death in 1528, Vier Bücher von menschlicher Proportion (“The Aesthetic Anatomy of Human Proportion”) by German artist Albrecht Dürer was published in Nuremberg. This work, written, illustrated and designed by Dürer, with woodcuts on virtually every page, was the first book to discuss the problems of comparative and differential anthropometry. The classic aesthetic treatises of Villard de Honnecourt, Vitruvius, Alberti and da Vinci influenced Dürer, however, Dürer’s study of the different human physiques—fat, thin, tall, short, baby, child and adult —was entirely original.

Dürer held that the essence of true form was the primary mathematical figure (e.g., straight line, circle, curve, conic section) constructed arithmetically or geometrically, and made beautiful by the application of a canon of proportion. However, he was also convinced that beauty of form was a relative and not an absolute quality; thus, the purpose of his system of anthropometry was to provide the artist with the means to delineate, on the basis of sheer measurement, all possible types of human figures.

Albrecht Dürer, German Northern Renaissance Painter and Engraver, 1471-1528

Generally, morphometrics (from the Greek: “morph,” meaning shape or form, and “metron,” meaning measurement) comprises methods of extracting measurements from shapes. In most cases applied to biological topics in the widest sense. Schools of morphometrics are characterized by what aspects of biological “form” they are concerned with, what they choose to measure, and what kinds of questions they ask of the measurements once they are made. In many cases involves calculating angles, areas, volumes and other quantitative data from landmark and segmentation data.

Auxology is a meta-term covering the study of all aspects of human physical growth; though, it is also a fundamental of biology, generally. Auxology is a highly multi-disciplinary science involving health sciences and medicine (pediatrics, general practice, endocrinology, neuroendocrinology, physiology, epidemiology), and to a lesser extent nutrition, genetics, anthropology, anthropometry, ergonomics, history, economic history, economics, socioeconomics, sociology, public health, and psychology, among others.

Directly observable characters —such as the shape, size, and color of the body and its parts —were formerly the only means of classifying individuals and populations. They have several disadvantages for this purpose, particularly their complex inheritance, and the fact that almost every character is influenced both by heredity and by environment.

Although they have, for these reasons, largely been superseded for purposes of classification by the blood groups and other hereditary genomic markers; it must be kept in mind that they are still the means by which, in everyday life, we recognize people. We should also recall that quantitative observations of them are the only means we have of comparing skeletal material and observations made on the living before the discovery and application of the blood groups, with people living today. They also present very clear indications of probable natural selection in relation to the environment.

They must therefore continue to be observed with as much precision as possible. It would be a great advantage if their heredity could be more fully understood. It is now clear that almost any one single character, such as stature, is the effect of genes at a considerable number of different loci, so that they are known as polygenic characters.

Developmental noise is defined as perturbations in the developmental environment that arise from random fluctuations at the molecular and cellular level and canalization is the buffering of development against many, if not all of those perturbations. There is currently a bit of a debate as to whether canalization and developmental stability are the same thing. Arguing against this is the fact that canalization reduces the effects of environmental and genetic insults and thereby reduces variation. (1,2) It is sometimes the practice to speak of homeorhesis (“stabilized flow”) a phrase coined by Waddington, (3) and encouraged by others (4) to describe a sort of a developmental trajectory, distinct from canalization, that refers to the capacity for a structure to develop along an ideal developmental trajectory under a particular set of environmental conditions. The sensitivity to random perturbations —or developmental noise— can be viewed as the tendency of a developmental system to produce a morphological change in response to these perturbations and is often called developmental instability.

  1. Wilmore KE and Hallgrimsson B. Within Individual Variation. In: Variation, A central concept in Biology. Elsevier Academic Press. London (2005)
  2. Van Valen L. A study of fluctuating asymmetry. Evolution, 16. (1962).
  3. Waddington, C. H. (1957). The Strategy of the Genes. London: George Allen & Unwin.
  4. Zakharov, V. M. 1992. Population phenogenetics: Analysis of developmental stability in natural populations. Acta Zool. Fenn. 191: 7-30.

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Mar 20 2012

Fruity Loops and Arrowheads

One of the features of any network is the appearance of motifs, patterns (sub-graphs) that recur within a network much more often than expected at random. These small circuits can be considered as simple building blocks from which the network is composed. This analogy is quite useful, since many of these motifs would appear to have their corollaries in electronic circuitry. Motifs appear to play an important role in transcription factor regulation, which is pretty significant, because transcription factors regulate the expression of most genes.

Electronic circuits spend a lot of their time filtering out noise, and many motifs in molecular biology do this same function. Most of this noise is stochastic: a charmingly ancient Greek word that more-or-less equates to random, but not exactly, being derived from the Greek stochastikos, ‘skillful in aiming.’ To me, stochastic embodies the old saw that ‘if something can happen, it will.’

Stochastic occurrences mess up our pretty, deterministic, view of how things happen. For example, the response of our bodies to radiation therapy is stochastic since not every cell receiving the radiation energy is at a point in its life cycle when the radiation therapy can effect it: Some cells are (those currently reproducing) whilst other cells are not (those currently at rest). This is a problem when one uses methods of measurement that are Gaussian (i.e they ‘average things out’) because these types of measurement will yield indications of a more gradated response than really occurred.

Stochastic music was pioneered by the composer Iannis Xenakis. Unfortunately, while undoubtedly making for interesting math, I agree with this author that the music sounds more or less like an hour-long extended visit to a junk yard. I do like some of Xenakis’ other music though, such as Metastaseis..

Stochastic versus graded response.

Most network motifs try to soften up the responsiveness of transcription factors to stimulus with a high signal to noise ratio, sort of like how an experienced parent can tell the difference between a crying child who just needs to nap versus a child in true distress.

Since developing the Quodlibet module in Datapunk I’ve been increasingly aware of the need to incorporate these types of motifs in my network calculations, when and where they show up. Quodlibet currently does quite a bit of graph/network/combinatorial calculation already (click on the Analytics link in any molecular map to see it at work) so looking for network motifs seemed the logical next step.

I mostly work in Perl, so I often take advantage of CPAN (The Comprehensive Perl Archive Network) which hosts a wealth of Perl modules, including a terrific collection of bioscience libraries, such as Bioperl. These modules are what makes Perl so great: you do not have to reinvent the wheel and in many instances you don’t even have to know exactly how the wheel works. Just plug it in, feed it good stuff and get the output. CPAN has been a great asset with Quodlibet, but contained no modules for network motif detection. Fortunately an online homework assignment provided me with the means to get started.

However, before I launch into any of that stuff, in my next blog I’ll take a look at the most common network motifs. These have been identified largely through the work of Uri Alon and his lab at the Weizmann Institute of Science in Israel, and if your interest runs in the direction of computational biology, I recommend that you take a look at his book Introduction to Systems Biology: Design Principles of Biologic Circuits (Chapman and Hall/CRC). The loop is a basic network motif, so next blog we’ll take take a look a a few variations to get an idea about how these things work things.

Like the digital circuitry in your computer, clusters of network motifs are capable of computational processes. Think about this: Humans share about 98% of their genome (at least the sequences) with apes. This of course begs the question ‘why are we not more similar?’ The answer is that while we share much of the base sequences, there are tremendous differences in the ‘computational knowledge’ that acts upon these sequences, in particular the networks involved in transcription factor regulation: operons, regulons and modulons. It is the ‘combinatoric wisdom’ that seems to differentiate between the classes of life forms.

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Mar 06 2012

MTOR Dependant and Independent Autophagy

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Peter D’Adamo: ‘MTOR Dependent and Independent Autophagy’ (02/10/12)

Peter D'Adamo presenting at the 3 West Club, New York City.

A lecture given at the 3 West Club, New York, NY.

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Feb 09 2012

Practical Glycomics

Peter D’Adamo, ‘Practical Glycomics’ (10/23/11)

Initial hour of six-hour, day-long seminar done for the UB Nutrition Institute, October, 2011.

Peter D’Adamo, Flying Spines (1981)

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Jan 03 2012

COE, 1/1/2012

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In 2009, Dr. Peter D’Adamo and the University of Bridgeport College of Naturopathic Medicine co-created the Center of Excellence in Generative Medicine. Beginning in 2012, the Center will research, develop and employ the tools of systems analysis, molecular biology and bioinformatics to better understand the unique and complex self-healing behaviors that are the basis of naturopathic philosophy and therapy. The COE will be based in a beautiful Victorian house adjacent to UB Health Sciences Complex.

Beautiful woodwork surrounds the proposed lecture area.

Screening off of hallway/ waiting area.

 

Stairway to second floor.

Sunlight highlights the carved wood.

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Dec 22 2011

Eat Me

Imagine that you are the owner of a small factory that makes replacement windows.

Normally, your ordering department does a pretty good job of things and the supply of the constituent parts necessary to make a decent replacement window (one assumes these to be things like glass, vinyl, aluminum, hardware, etc.) arrive punctually and in sufficient amounts to allow you to make the maximum number of windows with the minimum amount of wastage.

However, over time changes in personnel lead to problems of supply and demand. Tired, jaded people in the ordering department forget a decimal point and you wind up with an excess of window locks; poorly trained workers on the assembly line make a variety of newbie-type errors that result in windows that are unsellable. Over time this pile of unsellable windows begins to accumulate to the point where it begins to clog up the aisles, creating an unhealthy workplace. Soon the corporate bank account is drained due to excessive purchasing and the assembly area is choked with unusable, unsellable, windows. Workers begin to grumble about the unsafe working conditions and a few threaten to strike unless conditions improve. An investigation seems to indicate that your factory supervisor, Mr. Mtor, has a grudge against you due to his being passed up for a promotion at his last salary review and has been going around sabotaging things by telling the workers to not bother about quality control and cleaning up after themselves.

Concerned about the future of the family enterprise, you fire Mr. Mtor and hire a sharp graduate of Wharton School of Business and soon things begin to right themselves. A special work team is put together to go through the piles of unsellable windows, cannibalizing parts that can be reused to create properly constructed, sell-able windows. All new orders are now reviewed to insure that no duplication or excess inventory is allowed to siphon off precious capital and storage space. Soon conditions begin to improve, your workers seem much more happier, and productivity and profitability skyrocket.

Welcome to the wonderful world of cellular autophagy.


Autophagy (‘self-eating’) is a catabolic (breakdown) process used by cells to degrade and remove some of their internal components deemed to be unnecessary or undesirable. Like our window company, cells are little factories of a sort, and as such they function under many of the same dynamic considerations: Things accumulate; byproducts are produced that can’t do anything, etc.

In cells these byproducts are usually some sort of misshapen protein that folded in some manner incomprehensible to the cell and hence not usable. This is not all that uncommon and many common chronic diseases are characterized by the production of proteins that did not fold properly. Much like having a rock in your shoe, having mis-folded proteins in the synthetic/secretory parts of the cell factory (an organelle called the endoplasmic reticulum) results in what is known as the unfolded protein response, a stress reaction to these weird proteins.

Eat them up yum yum.


This phenomenon is called ‘ER Stress’ and can result in either of two outcomes.

Try to fix things or at least spit it out: The response to the misfolded proteins can trigger molecules called chaperones that can attempt to fix/refold the proteins into something usable. Lacking this response, the cell can attempt to encapsulate the offending stuff, digest it, and then spit the capsule out. This is autophagy.

Failing that, call it quits: If things are so bad, sometimes the cell just calls it a day and commits at type of cellular hari-kari suicide called apoptosis.

In addition to acting as a type of cell cleansing mechanism, autophagy is a very old survival tool. Like a factory deprived of raw materials because of a transit site, cells deprived of nutrients will scrounge around the workplace looking under tables and behind cabinets for surplus parts. In the cell’s case, when deprived of nutrients like the amino acids tyrosine and methionine, the cell will begin to breakdown parts of itself to keep going. Much of the time, this breakdown is a good thing, and perhaps explains why things like calorie restriction seem to increase lifespan: under those conditions the cell is munching away at itself, and since it is no fool, a lot of what it munches away at is junk best gotten rid of anyway.

Autophagy is normally kept under control by a protein called MTOR that acts as a sensor for conditions that might require autophagy. Normally MTOR blocks autophagy, so when it is inhibited, autophagy strikes up the band. MTOR can get screwed up in cancer, which is not a great thing since autophagy tends to block the apoptosis suicide mechanism (why kill yourself when things are working this great?) This has led some to posit that enhancing autophagy might not be a great thing. However it is probably not this simple as other genes that act as tumor suppressors appear to enhance autophagy by blocking MTOR, so we still don’t know the complete answer on that one.

Two things for sure: autophagy looks like a winning strategy when it comes to neurodegenerative disease and aging. Slower aging associated with decreased MTOR activity, while disease like Parkinsons and Alzheimers are linked to blockages in autophagy.

Some natural products, including epigallocatechin gallate (EGCG), caffeine, curcumin, and resveratrol, have also been reported to inhibit MTOR when applied to isolated cells in culture. More work is needed to see if these work at the level of dietary supplementation.

There are sugars,and then there are sugars.

One interesting agent with well-recognized effects on autophagy is the natural disaccharide sugar trehalose, a sugar produced by bonding two glucose molecules together in a way that differs significantly from the sugar on top of a jelly donut. Trehalose is found in many organisms, including bacteria, yeast, fungi, insects, invertebrates, and plants. It functions to protect the integrity of the cell against various environmental stresses like heat, cold, desiccation, dehydration, and oxidation by preventing the screwing-up of the cell’s protein insides.

Extracting trehalose used to be a difficult and costly process, but recently an inexpensive extraction technology has allowed for its use in a broad spectrum of applications. Trehalose prevents cells from dehydrating, a phenomena that disrupts much of the cell’s insides in way that are not reparable. Trehalose-treated cells seem to resist this because the trehalose ‘splints’ their guts in place, so that when the cells get a chance to rehydrate they come back good-as-new. Dehydrated cells are common with aging, as the aging process tends to thin out the cell membrane, making it harder and harder for the cell to maintain its internal water balance.

Trehalose has been accepted as a novel food ingredient under the GRAS terms in the U.S. and the EU. Trehalose has also found commercial application as a food ingredient. It is available in dietary supplement form.

Maybe the most interesting property of trehalose is its ability to enhance autophagy. Neurofibrillary tangle (NFT) is a characteristic hallmark of Alzheimer’s disease. The accumulation of a protein called tau in the NFTs is one of the characteristic features of several diseases known as tauopathies. In cell culture studies trehalose treatment exhibited significantly decreased level of tau in all tau species.

What is especially interesting about trehalose is that it works in ways that are independent of the MTOR protein, leaving it free to do its work as a sensor of changing environmental conditions.

Turns out we didn’t have to fire Mr. Mtor after all.

The more technically inclined, with an up-to-date modern browser, may want to check out the autophagy network map in Quodlibet.


  1. Kim SI, Lee WK, Kang SS, Lee SY, Jeong MJ, Lee HJ, Kim SS, Johnson GV, Chun W. Suppression of autophagy and activation of glycogen synthase kinase 3beta facilitate the aggregate formation of tau. Korean J Physiol Pharmacol. 2011 Apr;15(2):107-14. Epub 2011 Apr 30. PUBMED
  2. Rodríguez-Navarro JA, Rodríguez L, Casarejos MJ, Solano RM, Gómez A, Perucho J, Cuervo AM, García de Yébenes J, Mena MA.Trehalose ameliorates dopaminergic and tau pathology in parkin deleted/tau overexpressing mice through autophagy activation. Neurobiol Dis. 2010 Sep;39(3):423-38. Epub 2010 May 28. PUBMED
  3. Sarkar S, Davies JE, Huang Z, Tunnacliffe A, Rubinsztein DC. Trehalose, a novel mTOR-independent autophagy enhancer, accelerates the clearance of mutant huntingtin and alpha-synuclein.J Biol Chem. 2007 Feb 23;282(8):5641-52. Epub 2006 Dec 20. PUBMED
  4. Krüger U, Wang Y, Kumar S, Mandelkow EM. Autophagic degradation of tau in primary neurons and its enhancement by trehalose. Neurobiol Aging. 2011 Dec 12. [Epub ahead of print] PUBMED

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Dec 21 2011

Concept Nodes

This entry is about an editing function in QUODLIBET. QUODLIBET is a computer program that creates, edits and queries biomedical networks. In addition to providing information about genetic-protein-phenotype interactions QUODLIBET also allows for additional information to be harvested, including data on the effects of natural products on gene-protein expression.  We are always looking for volunteers who are interested in helping out. Volunteers need not have any medical or super computer skills, just a passion for exactitude and a desire learn more about the genetics and biology.

Here is some documentation to get you going:

There is also a Forum for new users and potential editors.


Most of the nodes in a QUODLIBET network might be called ‘working nodes.’ Working nodes are, to say the least, nodes that do some sort of work: they might carry information about a gene, a naturopathic agent associated with that gene or even contain other constituent genes. These types of nodes are also involved in the calculations that drive the network Analytics. But most QUODLIBET canvases are not made up solely from working nodes. For example, a pathway may terminate in a node that describes a process of some sort, such as ‘Increased insulin resistance’ or ‘Acyl-CoA.’ We might consider these Concept Type Nodes: Nodes that identify and teach about the context by which the network functions and has meaning.

Unlike basic working nodes, where you are free (or even impelled) to ‘roll your own,’ concept nodes are chosen and inserted from a toolkit of currently available items. This is done to maintain conformity and continuity: The concept node ‘Acyl-CoA’ will hyperlink to the same description popup in the map are editing as it will in any other map it appears in. Thus we only have to produce on very good entry on Acyl-CoA versus create one anew for every different map that needs one.

A concept node in a network.

Inserting a concept node into a map is easy: try adding one into the Sandbox map. From the pull-down, select one of the available concept nodes and press Submit. The Concept node will appear as a white box with a grey border. From there you can link concept nodes with edges just link any other node. Here we’ve inserted the concept node Acyl-CoA and linked it to the Vancouver node.

Now when a user clicks on the concept node Acyl-CoA they will be treated to the following information pop-up:

Concept node user information popup

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Dec 18 2011

Quodlibet

A quodlibet is a piece of music combining several different melodies, usually popular tunes, in counterpoint and often a light-hearted, humorous manner. The term is Latin, meaning “whatever” or literally, “what pleases.”


Quodlibet (QUOD) is a suite of network creation, editing and querying software I am currenty developing. QUOD is a software application that displays biochemical pathway data in a way that is interactive and information intensive.

In addition to providing information about genetic-protein interactions QUOD also allows for additional information to be harvested, including data on the effects of natural products on gene-protein expression.

A bit of the Adipocytikine network as displayed in QUOD. Amber nodes indicate high betweenness centrality and in-degree. Dark green tags indicate that a natural product has been associated with the regulation of that node.

Unlike most biochemical pathway/network depiction programs QUOD actually ‘thinks’ in a social network sense in that it analyzes the network and reports on many graph functions including betweeness centralities, page-ranks, and cluster coefficients. This allows an immediate understand of which nodes are acting in a critically important role in the network.

It was designed to be simple, easy to user and fun to edit and develop in. Because it is web-based, no special software is required, other than a modern browser and a decent Internet connection. QUOD runs under the DataPunk platform and is open-access. Curators can use the extensive editing tools to add to, alter, or create entirely new networks.

One of the more powerful aspects of QUOD is its ability to highlight naturopathic procedures and agents that have been shown to exert an influence on the expression or function of elements in a molecular network. This will have a major influence on the future practice of Generative Medicine, since complex patterns of relationships between naturopathic agents and procedures (traditional as well as biomedical) can be superimposed over the network analysis of complex molecular graphs so as to allow clinicians to derive extraordinarily high quality suggestions about specific approaches that may more closely approximate the holism of the Vis Medicatrix Naturae.

Since the project is community-based, we are always looking for volunteers who are interested in helping out. Volunteers need not have any medical or super computer skills, just a passion for exactitude and a desire learn more about the genetics and biology. To learn more about QUODLIBET, you can download the User Guide and visit the Community Forums.

What is needed now is the community of volunteers who would be willing to devote time to curate additional maps. Hopefully this call to action will not prove too disappointing: I can think of no better way to enhance one’s own understanding and knowledge about a complex topic than to build mind-maps. Thus it is to the students of our profession that I direct this challenge, though any interested party with a computer and a decent Internet connection is welcome to help out.

Phase I of our map development program will involve translating the KEGG genomic and metabolic maps into QUOD format. KEGG maps are good and even hyperlink to relevant KEGG entries for enzyme activities, drugs, etc. However KEGG maps are hand drawn and cannot perform network graph computations. However KEGG has done much of the heavy lifting: converting a KEGG map to a QUOD map makes development quick and painless. From there QUOD will link all subsequent maps into clustered networks, allowing for greater and greater information processing.

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Nov 12 2011

Beautiful Data

It has been said that if one really wants to learn something, they should teach it. However we may want to expand that aphorism, to perhaps if one really wants to learn something, they should teach it, organize it or animate it.

The commonality between science and art is in trying to see profoundly – to develop strategies of seeing and showing. –Edward Tufte

One of the main goals of our Datapunk bioinformatics platform is the development of new and exciting information visualization  (InfoViz) tools that can be used to develop new appreciations for the relationships between data.  We are currently developing two new InfoViz platofrms that I think have great potential. But more than that, these tools feature stunning interfaces that go a long way towards again proving the fact that information, presented imaginatively, can not only yield amazing secrets, but can be stunningly beautiful as well.

Pathscrubber

The first InfoViz platform we developed is a full-bodied genomic network depictor called PathScrubber, which runs inside Datapunk. PathScrubber draws network graphs of gene-protein relationships. Each node in the network is click-able and links to a popup that provides information on that gene, through an API to OMIM (Online Mendellian Inheritance in Man). Perhaps more significantly, Datapunk is the first informatics tool that is harvesting scientific references detailing phytochemicals and dietary agents that have been reported in the literature to influence the expression of these gene-proteins.

PathScrubber.

Simply enter any gene-protein terms you wish to include in your network. Don’t worry about partial terms; PathScrubber will return a list of possible terms for you to consider and you can check which ones are appropriate in the next screen. After the program draws the network, you can zoom in or out with either the mouse or via a slider. Nodes containing genomic expression information on phytochemical or dietary agents are coded orange. PathScrubber has an extensive help page. PathScrubber is programmed in Perl, utilizing Léon Brocard’s GraphViz module to draw the graphs.

InfoViz Democratizers

This platform is designed to provide a venue to allow naturopathic physicians and researchers to animate their own data. One of the most exciting/important things we are developing is a set of guidelines for authors on how to structure their data so as to allow us to easily port it into a stunning visual display. Most JavaScript data is encoded in a format called JSON, which although paradoxically designed to be easily readable by humans when compared to other data structures, requires a heavy degree of ‘nesting’ of the data, which most non-programmers would find confusing and thus increase their proneness to data entry error. So we developed a simple language that only requires that the data be entered in a simple text file with a few codes. This is then parsed by Perl into JSON and piped to the page as HTML. Here are two examples:

Lectins: Classification and Taxonomy
Our second infoviz tool is a depiction of a the taxonomy and classification of known animal, plant and microbial lectins. Lectins are protein molecules that attach to sugars and modulate a variety of cell functions, including mitosis, agglutination, metastasis and infections. Most of the data for this infoviz is from my textbook, Fundamentals of Generative Medicine Clicking on a node should move the tree and center that node. This infoviz makes extensive use of JavaScript, especially the JavaScript InfoVis Toolkit. The tree-like structure opens and closes as one click on the various categories.

Lectin Classifications

Radio buttons on the top allow for the user to display the tree in different aspects and to chose between a ‘normal’ display, where the categories open up in a linear fashion of a ‘centering’ mode where the newly selected category moves to the center of the tree. Finally branches of the tree often contain hyperlinks to additional information.

Actions of Medicinal Plants
Our third infoviz tool is a depiction of a paper developed by Eric Yarnell, ND entitled ‘A Compendium Pharmacological Actions of Medicinal Plants and Their Constituents.’ Clicking on a node should move the tree and center that node. This InfoViz also makes extensive use of JavaScript, especially the JavaScript InfoVis Toolkit to display day in the form of a morphing hypertree. The centered node’s children are displayed in a relations list in the right column. The data set for this InfoViz is still in development.

Compendium of Medicinal Herbs

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