Bion Alex Howard ­ 11.14 

The fractal network information systems theory of molecular biology Bion Howard, Russell Hanson, and Zihai Li

“Life and death are one thread, the same line viewed from different sides.” - Lao Tzu

My goal in this essay is to describe the name and mental image I use when thinking about

the clear interconnectedness we all feel upon considering the Life on Earth, and show that this

viewpoint could be useful for the future of medicine. What happens when a node in a network is

not a node, but a network in itself? What do we see when we zoom into the tree of Life, deeper

than the organism-scale network proposed by Darwin? (1) We see that Life is a fractal network of

information systems. What is a fractal network? Networks are simply understood as webs of

things that interact, and information systems are networks of the combinatorial probabilities of

causes and effects. Even if the Universe is fully deterministic, we have no choice but to account for

our uncertainties with probability: we are agents with limited knowledge. As described by Benoit

Mandelbrot, (2) fractal geometry shows the complex emergent results of simpler processes,

endlessly iterated. A key property of fractals is self-similarity; fractal objects appear the same

regardless of the scale on which they are viewed. So, a fractal system or fractal network is a group

of interconnected pieces, where each piece is a network of sub-pieces which is a network of

sub-sub-pieces in turn, and so on. A long-winded explanation of biology shows the

communication of information (a procession of causality) on many scales of space and time:

ecosystems involve sets of interacting, evolving populations which are made of organisms which

are made of organs which are made of cells which have organelles which are made of molecules

which are networks of atoms which are made of particles. Life must be a fractal process, because it

exemplifies self-similarity. For example, feedforward and feedback control motifs are found in

political dynamics, macroscale neural and microscale cellular networks. Life is not just a set of

individuals; it is a singular, multi-scale, whirring, LIFE, vastly spread across space and time. We

think we rule the world as powerful individuals, and yet we are the intermediate products of a

single [stochastic] biochemical reaction, if you zoom out far enough. We feel innately connected to

trees we are trees; trees, cells, brains, and social structures are all networks, and they are

networked together in turn.

There is a practicality to fracticality: it helps explains why biology and medicine can be so frustratingly

[over]complicated: emergent linguistic complexity. Each human is a single tree on the beautiful tree of trees of trees, and we

are trying to name and categorize and remember the countless components of numerous networks on several scales. We are

the leaves memorizing the tree. True fractals have essentially infinite complexity; why try to fit into one leaf the labels of every

leaf in a tree with infinite branches? Let’s make Medicine and Life less about Proper Nouns and more about Mechanisms. No

wonder a multiscale fractal naming process results in a dizzying array of names and acronyms! Good luck getting that to fit

entirely in your brain. Perhaps instead, simplicity can cure our medical complexity problem. Combination therapies are

clearly important to investigate, but we might use fewer nanobiotech tools to modularly treat diverse diseases. I wish the

answer was, “DIAMONDOID NANOROBOTS!” as described by Freitas, (3) but nanomanufacturing remains difficult, and

Illness doesn’t wait around for our inventions. Viruses, if armed and conditionally functional, will be very useful, but are

nonmotile and built of immunogenic proteins, potentially causing reduced duration and effect. Nanoparticles have huge

potential for safety and computation in vivo, (24, 25) but are also nonmotile and don’t replicate. [Yet.] Bacteria move around

and replicate, but have immunogenic proteins, just like viruses. Therefore, the ideal treatment for many human diseases is

probably human cells: they move, exist, and replicate, they can be patient-specific and thus less likely to be rejected, and they

can perform complicated functions and persist for decades, moving to specific places in the body and conditionally producing

viruses, enzymes, antibodies, etc in exactly the right place at the right time. We need not limit ourselves to natural cell types;

we should combine the useful traits of different cell types and build swiss-army-knife cells. A cell is a cell is a cell, despite

details of differentiation. My personal favorite cell type for medical use is the Macrophage, the “big eater,” because

phagocytosis, or eating, of cells and debris feels like a useful phenotype to engineer for medical purposes. How many diseases

could we cure with precisely targeted cell killing? Regardless of the chassis, mobile cellular factories might enable new and

precise diagnosis and treatment of numerous diseases like cancer, autoimmunity, chronic pain, amyloidosis, and others.

 

 Bion Alex Howard ­ 11.14 

We can enhance our therapies with careful use of the powerful tools of synthetic biology, such as engineered

receptors, (4, 5) genome editing, (6) and conditional production of biomacromolecules. (7) Chimeric antigen receptors

(CARs) represent a clear example of the power of receptor engineering, and have shown great success in directing T cell

activity in “liquid” cancers like leukemia. Interestingly, similar intracellular activation motifs called ITAMs are present on

both T cell and macrophage receptors. (8, 9)

 

 Bion Alex Howard ­ 11.14 

We might also find great use for ensembles of synthetic inhibitory receptors to block unwanted attacks on cells

bearing “stop antigens” which demarcate crucial bystander organs like brain, heart, liver, kidneys, blood vessels, etc. (10)

Another useful engineered receptor would be homing receptors directing therapeutic cell movement to specific disease sites.

If the receptors on our cell therapy can be designed properly, the only objects to be phagocytosed should be those expressing

a combination of target-specific antigens and lacking stop antigens. By combining various types of engineered receptors, we

can more precisely control the location and activation of cell therapies.

Genome editing is taking the world by storm

because it enables us to turn on, turn off, and edit

genes. The most advanced such tool is called

CRISPR/Cas9. This bacterial antiviral defense system is

powerful because it is modular: a protein binds a

target-DNA-specific guide RNA (gRNA), which guides the

protein to the matching sequence of DNA, and then

makes a double-stranded nuclease cut or a

single-stranded nickase cut. A double-strand cut will

induce a targeted insertion-deletion (indel) mutation when

it is repaired via the process of non-homologous end

joining, leading to a disabling frameshift of the codons of

desired genes. The nickase enzyme, with a deactivation of one of the RuvC or HNH active sites, has the advantage that a full

double-strand cut requires a pair of gRNAs binding close together, thus preventing off-target mutations; however this nickase

technique is slightly more difficult and expensive because of the requirement for multiple gRNA to direct multiple nicks in the

genome. To turn ON a gene, we can mutate both of the cutting sites to create a defective enzyme, and then fuse the activation

domain from a transcription factor to the Cas9 protein to create a CRISPR-ON. If we give this CRISPR-ON a gRNA

corresponding to the promoter sequence upstream of a desired gene, it will activate the gene by functioning as a synthetic

transcription factor. Finally, to specifically insert into (or edit) genomes, we can use homology directed repair whereby, in

addition to CRISPR, we also insert a template DNA sequence with flanking sequences matching the ends of the

double-stranded break. (11, 12) This template enables the cell to repair the cut by inserting the template DNA between the

flanking sequences instead of causing an indel mutation. Another good method for inserting genes is to use a stable ‘episome’

vector called an S/MAR minicircle, (13) which is sort of like a bacterial plasmid except we use the CRE recombinase system to

chop out all of the bacterial bits and increase its compatibility with human cells, and we include a sequence called the S/MAR

which helps hold the vector in a safe place within the nucleus over long timescales. We might use cas9 systems to activate

stemness genes (so therapies last longer) and deactivate genes of tumor immunosuppression (so therapies keep working

within an immunosuppressive solid tumor microenvironment).

Since we can make cells move around and specifically grab onto targets with receptors and edit their genomes with

cas9, the next big goal should be to turn cells into factories producing therapeutics in exactly the right places. One option is to

program cells to secrete [multiple] soluble antibodies linking target antigens to macrophage Fc receptors, enabling cell

therapies which programmably devour many different pathogenic materials in many different diseases (cancer cells,

extracellular junk, autoimmune T-cells, etc). Besides antibodies, it should also be possible to secrete enzymes metabolizing

pathogenic molecules only in areas where the pathogen concentration is high enough to activate low-affinity (kd < 10-8)

synthetic receptors. (14) One of the most powerful possible “payloads” is the virus: by hiding them within patients’ cells, we

can enable viruses to evade immune destruction in the bloodstream. (15) By implementing this carrier idea alongside

combinatorial logic by both carrier receptors and viral DNA, we might solve the “vector problem” of gene therapy and enable

genetic engineering for a wide range of situations...chronic pain, obesity, ageing, regeneration, photosynthesis, even tentacles.

Considering these epic bio/nano possibilities in light of simultaneous revolutions in 3D printing, neurotech, aerospace,

electronics, materials, energy, statistics, A.I., and botany, the future will clearly be interesting. I vote we ought to open-source

advanced medical technology, to reduce suffering, and that cell therapy is one of many damn good ways to do it. Immense

change is possible--so let’s build the world we want. Let’s focus on ambitious translational research and work together to

improve people’s lives and solve medical cost problems with cheaper, more powerful, more precise treatments.

Dedicated to lost friends and big thinkers!

 

 Bion Alex Howard ­ 11.14 

Works Cited

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3. Freitas, Robert. Nanomedicine, Volume I: Basic Capabilities. 1999.

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5. June, Carl et al. Decade-long safety and function of retroviral-modified chimeric antigen receptor T cells. Science

Translational Medicine. 2012.

6. Church, George et al. RNA-guided human genome engineering via Cas9. Science. 2013.

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cooperative genome engineering. Nature Biotechnology. 2013.

12. Li, Kai et al. Optimization of Genome Engineering Approaches with the CRISPR/Cas9 System. PLoS One. 2014.

13. Wong, Suet-Ping and Richard Harbottle. Genetic modification of dividing cells using episomally maintained S/MAR

DNA vectors. Molecular Therapy Nucleic Acids. 2013.

14. Chmielewski, Markus et al. T Cell Activation by Antibody-Like Immunoreceptors: Increase in Afnity of the

Single-Chain Fragment Domain above Threshold Does Not Increase T Cell Activation against Antigen-Positive

Target Cells but Decreases Selectivity. The Journal of Immunology. 2014.

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delivery of the therapeutic payload in an orthotopic brain tumor model. Cancer Gene Therapy. 2012.

16. Universiteit Utrecht. CRISPR/Cas9 targeted mutagenesis.

<http://web.science.uu.nl/developmentalbiology/boxem/CRISPR.html> Accessed 2014.

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Rosetta. Bioinformatics. 2010.

18. Khalili, Jahan; Hanson, Russell; and Zoltan Szallasi. In silico prediction of tumor antigens derived from functional

missense mutations of the cancer gene census. Oncoimmunology. 2012.

19. Cox, Robert et al. PromoterCAD: data-driven design of plant regulatory DNA. Nucleic Acids Research. 2013.

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DNA Sequencing Data. Genome Research. 2010.

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best practices pipeline. Current Protocols in Bioinformatics. 2014.

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24. Heffern, Elleard; Hanson, Russell, and Jason Fuller. The aptabot: an inducibly affinity-switching, minimally

invasive in vivo contrast agent. Personal Communication. 2014.

25. Lin, Kevin; Sangeeta Bhatia et al. Self-Titrating Anticoagulant Nanocomplexes That Restore Homeostatic

Regulation of the Coagulation Cascade. ACS Nano. 2014

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