Tag: DNA storage

Computational (DNA) storage – end of evolution part 4

Posted on by Ray in Storage density, Strategic Inflection Points

We were at a recent Storage Field Day (SFD26) where there was a presentation on DNA storage, a new SNIA technical affiliate. The talk there was on how far DNA storage has come and is capable of easily storing GB of data. But I was perusing PNAS archives the other day and ran across an interesting paper Parallel molecular computation on digital data stored in DNA, essentially DNA computational storage.

Computational storage are storage devices (SSDs or HDDs) with computational cores that can be devoted to outside compute activities. Recently, these devices have taken over much of the hyper-scalar grunt work of video/audio transcoding and data encryption activities which are both computationally and data intensive activities.

DNA strand storage and computers

The article above discusses the use of DNA “strand displacement” interactions as micro-code instructions to enable computation on DNA strand storage. The use of DNA strands for storage reduces the storage density of DNA information that currently use nucleotides to encode bits (theoretically, 2 bits per nucleotide) to 0.03 bits per nucleotide. But as DNA information density (using nucleotides) is some 6 orders of magnitude greater than current optical or magnetic storage, this shouldn’t be a concern.

A bit is represented by 5 to 7 nucleotides in DNA strand storage, which they called a domain, these are grouped into a 4 or 5 bit cells, with one or more cells arranged in a DNA strand register which is stored on a DNA plasmid.

They used a common DNA plasmid (M13mp18, 7.2k bases long) for their storage ring (which had many registers on it). M13mp18 is capable of storing several hundred bits, but for their research they used it to store 9 DNA strand registers.

The article discusses the (wet) chemical computational methods necessary to realize DNA strand registers and programing that uses that storage.

The problem with current DNA storage devices is that read out is destructive and time consuming. With current DNA storage, data has to be read out and then computation occurs electronically and then new DNA has to be re-synthesized with any results that need to be stored.

With a computational DNA strand storage device, all this could be done in a single test tube, with no need to do any work outside the test tube.

How DNA strand computer works

They figure shows a multi cell DNA strand register, with nic’s or mismatched nucleotides representing the value of 0 or 1. They use these strands, nic’s and toeholds (attachment points) on DNA strands to represent data. They attach magnetic beads to the DNA strands for manipulation.

DNA strand displacement interactions or the micro-code instructions they have defined include

  • Attachment, where an instruction can be used to attach a cell of information to a register strand.
  • Displacement, where an instruction can be used used to displace an information cell in a register strand.
  • Detachment, where an instruction can be used to a cell present in a register strand to be detach it from the register.

Instructions are introduced, one at a time, as separate DNA strands, into the test tube holding the DNA strand registers. DNA strand data can be replicated 1000s or millions of times in a test tube and the instructions could be replicated as well allowing them to operate on all the DNA strands in the tube.

Creating a SIMD (single instruction stream operating on multiple data elements) computational device based on strand DNA storage which they call SIMDDNA. Note: GPUs and CPUs with vector instructions are also SIMD devices

Using these microcoded DNA strand instructions and DNA strand register storage, they have implemented a bit counter and a Turing Rule 110, sort of like life, program. Turing Rule 110 is Turing Complete and as such, can, with enough time and memory, simulate any program calculation. Later in the a paper they discuss their implementation of a random access device where they go in and retrieve a piece of data and erase it.

Program for bit counting, information in solid blue boundary are the instructions and information in dotted boundary are the impacts to the strand data.

The process seems to flow as follows, they add magnetic beads to each register strand, add an instruction at a time to the test tube, wait for it to complete, wash out the waste products and then add another. When all instructions have been executed the DNA strand computation is done and if needed, can be read out (destructively). Or perhaps pass off to the next program for processing. An instruction can take anywhere from 2 to 10 minutes to complete (it’s early yet in the technology).

They also indicated that the instruction bath added to the test tube need not contain all the same instructions which means that it could create a MIMD (multi-instruction stream operations on multiple data elements) computational device.

The results of the DNA strand computations weren’t 100% accurate but they show that it’s 70-80% accurate at the moment. And when DNA data strands are re-used, for subsequent programs, their accuracy goes down.

There are other approaches to DNA computation and storage which we discuss in parts-1, -2 and -3 in our End of Evolution series. And if you want to learn more about current DNA storage please check out the SFD26 SNIA videos or listen to our GBoS podcast with Dr. J Metz.

Where does evolution fit in

Evolution seems to operate on mutation of DNA and natural selection, or selection of the fittest. Over time this allows good mutations to accumulate and bad mutations to die off.

There’s a mechanism in digital computing called ECC (error correcting codes) which, for example, add additional “guard” bits to every 64-128 bit word of data in a computer memory and using the guard bits, is able to detect 2 or more bit errors (mutations) and correct 1 or 2 bit errors.

If one were to create an ECC algorithm for human DNA strands, say encoding DNA guard bits in junk DNA and an ECC algorithm in a DNA (strand)computer, and inject this into a newborn, the algorithm could periodically check the accuracy of any DNA information in every cell of a human body, and correct it, if there were any mutations. Thus ending human evolution.

We seem a ways off from doing any of this but I could see something like ECC being applied to a computational DNA strand storage device in a matter years. And getting this sort of functionality into a human cell maybe a decade or two. Getting it to the point where it could do this over a lifetime maybe another decade after that.

Comments?

Photo Credit(s):

  • Section B from Figure 2 in the paper
  • Figure 1 from the paper
  • Section A from Figure 2 in the paper
  • Section C from Figure 2 in the paper
  • Section A from Figure 3 in the paper

Living forever – the end of evolution part-3

Posted on by Ray in Data consistency, Data integrity, Data precision, data protection, Data retention, Storage longevity, Storage reliability, Strategic Inflection Points

Read an article yesterday on researchers who had been studying various mammals and trying to determine the number of DNA mutations they accumulate at about the time they die. The researchers found that after about 800 mutations for mole rats, they die, see Nature article Somatic mutation rates scale with lifespan across mammals and Telegraph article reporting on the research, Mystery of why humans die around 80 may finally be solved.

Similarly, at around 3500 mutations humans die, at around 3000 mutations dogs die and at around 1500 mutations mice die. But the real interesting thing is that the DNA mutation rates and mammal lifespan are highly (negatively) correlated. That is higher mutation rates lead to mammals with shorter life spans.

C. Linear regression of somatic substitution burden (corrected for analysable genome size) on individual age for dog, human, mouse and naked mole-rat samples. Samples from the same individual are shown in the same colour. Regression was performed using mean mutation burdens per individual. Shaded areas indicate 95% confidence intervals of the regression line. A shows microscopic images of sample mammalian cels and the DNA strands examined and B shows the distribution of different types of DNA mutations (substitutions or indels [insertion/deletions of DNA]).

The Telegraph article seems to imply that at 800 mutations all mammals die. But the Nature Article clearly indicates that death is at different mutation counts for each different type of mammal.

Such research show one way on how to live forever. We have talked about similar topics in the distant past see …-the end of evolution part 1 & part 2

But in any case it turns out that one of the leading factors that explains the average age of a mammal at death is its DNA mutation rate. Again, mammals with lower DNA mutation rates live longer on average and mammals with higher DNA mutation rates live shorter lives on average.

Moral of the story

if you want to live longer reduce your DNA mutation rates.

c, Zero-intercept LME regression of somatic mutation rate on inverse lifespan (1/lifespan), presented on the scale of untransformed lifespan (x axis). For simplicity, the y axis shows mean mutation rates per species, although rates per crypt were used in the regression. The darker shaded area indicates 95% CI of the regression line, and the lighter shaded area marks a twofold deviation from the line. Point estimate and 95% CI of the regression slope (k), FVE and range of end-of-lifespan burden are indicated.

All astronauts are subject to significant forms of cosmic radiation which can’t help but accelerate DNA mutations. So one would have to say that the risk of being an astronaut is that you will die younger.

Moon and Martian colonists will also have the same problem. People traveling, living and working there will have an increased risk of dying young. And of course anyone that works around radiation has the same risk.

Note, the mutation counts/mutation rates, that seem to govern life span are averages. Some individuals have lower mutation rates than their species and some (no doubt) have higher rates. These should have shorter and longer lives on average, respectively.

Given this variability in DNA mutation rates, I would propose that space agencies use as one selection criteria, the astronauts/colonists DNA mutation rate. So that humans which have lower than average DNA mutation rates have a higher priority of being selected to become astronauts/extra-earth colonists. One could using this research and assaying astronauts as they come back to earth for their DNA mutation counts, could theoretically determine the impact to their average life span.

In addition, most life extension research is focused on rejuvenating cellular or organism functionality, mainly through the use of young blood, other select nutrients, stem cells that target specific organs, etc. For example, see MIT Scientists Say They’ve Invented a Treatment That Reverses Hearing Loss which involves taking human cells, transform them into stem cells (at a certain maturity) and injecting them into the ear drum.

Living forever

In prior posts on this topic (see parts 1 &2 linked above) we suggested that with DNA computation and DNA storage (see or listen rather, to our GBoS podcast with CTO of Catalog) now becoming viable, one could potentially come up with a DNA program that could

  • Store an individuals DNA using some very reliable and long lived coding fashion (inside a cell or external to the cell) and
  • Craft a DNA program that could periodically be activated (cellular crontab) to access the stored DNA for the individual(in the cell would be easiest) and use this copy to replace/correct any DNA mutation throughout an individuals cells.

And we would need a very reliable and correct copy of that person’s DNA (using SHA256 hashing, CRCs, ECC, Parity and every other way to insure the DNA as captured is stored correctly forever). And the earlier we obtained the DNA copy for an individual human, the better.

Also, we would need a copy of the program (and probably the DNA) to be present in every cell in a human for this to work effectively. .

However, if we could capture a good copy of a person’s DNA early in their life we could, perhaps, sometime later, incorporate DNA code/program into the individual to use this copy and sweep through a person’s body (at that point in time) and correct any mutations that have accumulated to date. Ultimately, one could schedule this activity to occur like an annual checkup.

So yeah, life extension research can continue along the lines they are going and you can have a bunch of point solutions for cellular/organism malfunction OR it can focus on correctly copying and storing DNA forever and creating a DNA program that can correct DNA defects in every individual cell, using the stored DNA.

End of evolution

Yes mammals and that means any human could live forever this way. But it would signify the start of the end of evolution for the human species. That is whenever we captured their DNA copy, from that point on evolution (by mutating DNA) of that individual and any offspring of that individual could no longer take place. And if enough humans do this, throughout their lifespan, it means the end of evolution for humanity as a species

This assumes that evolution (which is natural variation driven by genetic mutation & survival of the fittest) requires DNA variation (essentially mutation) to drive the species forward.

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So my guess, is either we can live forever and stagnate as a species OR live normal lifespans and evolve as a species into something better over time. I believe nature has made it’s choice.

The surprising thing is that we are at a point in humanities existence where we can conceive of doing away with this natural process – evolution, forever.

Photo Credit(s):

DNA IT, the next revolution

Posted on by Ray in Processing performance, Scenario planning, Storage, Strategic Inflection Points

I’ve been writing about DNA computing and storage for quite awhile now (see DNA computing and the end of natural evolution, DNA storage and the end of evolution part 2, & Random access DNA object storage system). But in the last few months there’s been a flurry of activity in this space that seems worthy of note.

DNA programing language

First up, A logic programing language for computational nucleic acid devices, a research article in ACS Synthetic Biology magazine. The research describes a new approach to programming DNA computers, that’s uniquely designed to mimic molecular algorithmic capabilities for DNA devices. T\

The language uses logical statements and predicates (reminds me of Prolog). Indeed, the language was modeled after Prolog with equational and molecular extensions to represent DNA functionality. As with Prolog, output is a function of declarative, predicate logic rather than control flow and assignment in normal programming languages. Logic programming takes a different mind set and demands an understanding of formal logic.

The article talks about applications for DNA computing for in vitro (chemical/protien) manufacturing, diagnosis, and therapeutics (operating inside living cells) devices (cells).

DNA storage device

Next up, a recent article in Scientific Reports, Demonstration of end-to-end automation of DNA data storage.

The intent here is to create a fully automated data storage device that uses DNA as its recording media. The current device (seen in the bottom right above) is a lab prototype, that fits on a bench and costs $10K that can store 5 bytes of data with error correction.

The system has three hardware modules: synthesis (writing), storage and sequencing (reading). It also includes encoding and decoding software that translates bits to nucleic acid bases and adds error correction to it. They need to add more bases to be compatible with the sequencing (reading) process.

The limits to storage may have something to do with the size of the storage vessel as well as the size of the DNA string that can be synthesized/sequenced. . Error correction is based on a 6 base (bit) hashing code (less than a byte for 5 bytes). The systems write to read-back time is ~21 hrs.

The device creates many copies of the DNA (data) strand. The 5 byte (“HELLO”) string took 4 micrograms of liquid and yielded 3469 DNA strands, 1973 of which aligned properly to their adapter sequence. Of those properly aligned DNA strands, 30 had extractable payload regions of which 1 was correct, the other 29 were corrupted.

This is a very poor BER (bit error rate). For comparison LTO-7/8 has a BER of 1:10**19 bits, and enterprise disk has a BER of 1:10**15 bits. This DNA storage device has a BER of 3469:1 or ~99.9% of all bits written were lost.

To get a better understanding of the BER, they stored a 100 base (~12 byte) data payload. Of the 25,592 strands created, 286 aligned properly and of those 251 were corrupted, 11 had invalid hashes, and 8 were corrupted but correctable (valid hashes invalid data) and 16 were perfect reads. So 25592 strands had 24 proper reads ~1K:1 BER (not entirely correct because the correctable strands actually had bit errors but we can give them that).

DNA computer architecture

Last up, an IEEE Spectrum article, discussing CalTech Research, DNA computer shows programmable chemical machines are possible, reporting on an article in Nature, Diverse and robust molecular algorithms using reprogrammable DNA self-assembly (paywall). This DNA computer system is made of just DNA and salt water. It computes algorithms on 6 bits of input and uses DNA logic gates.

The Caltech team created 2 input-2-output boolean gates out of DAN sequences, five of these gates are connected to form a computation layer. It supports 6 input and 6 output bits. But you can layer multiple computational levels on top of one another where the output of one layer can be fed in as input to the layer on top of it.

One key, is that the DNA computer self assemblies the computational layer. They use a seed layer as a starter DNA strand and then the input (mixed inside a vial) is attached to this seed layer and then the computational layers are attached one by one until the output is generated.

Each computational layer is made up of DNA computational tiles that attach together sort of like a circuit. they were able to create a 355 instruction set for their DNA computer. In comparison the IBM 360 had a one byte op code (at most 256 instructions).

They have a compiler that allows researchers to write a software algorithm and this translates code into DNA circuit tiles, computational layers and ultimately into a DNA computer.

According to the article, it takes 1-2 hours to grow the computational DNA crystal and another day or so for the computation to complete.

An interesting approach to DNA computation but it’s unclear if they have any branching mechanisms in their “instruction set”. And 6 bit input/output seems a bit limiting. However, by creating boolean gates with DNA, they could recreate any type of electronic computer that exists today.

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Put it all together and someday you could have a DNA compute server and storage.

One thing that’s missing is a (packet switched or token ring) network for transferring data between cells (and maybe into and out of DNA storage). They could probably use some sort of vascular (network) system with a way to transfer data from inside a cell to the network and into another cell .

That way they could gang a number of DNA compute servers (cells) together and maybe create a cellular automata machine.

The future of computation looks wetter now.

Photo Credit(s):

Random access, DNA object storage system

Posted on by Ray in Clustered storage, data access, Data density, Data index, Data longevity, data protection, File Storage, Object storage, Strategic Inflection Points, Visionary leadershp

Read a couple of articles this week Inching closer to a DNA-based file system in ArsTechnica and DNA storage gets random access in IEEE Spectrum. Both of these seem to be citing an article in Nature, Random access in large-scale DNA storage (paywall).

We’ve known for some time now that we can encode data into DNA strings (see my DNA as storage … and Genomic informatics takes off posts).

However, accessing DNA data has been sequential and reading and writing DNA data has been glacial. Researchers have started to attack the sequentiality of DNA data access. The prize, DNA can store 215PB of data in one gram and DNA data can conceivably last millions of years.

Researchers at Microsoft and the University of Washington have come up with a solution to the sequential access limitation. They have used polymerase chain reaction (PCR) primers as a unique identifier for files. They can construct a complementary PCR primer that can be used to extract just DNA segments that match this primer and amplify (replicate) all DNA sequences matching this primer tag that exist in the cell.

DNA data format

The researchers used a Reed-Solomon (R-S) erasure coding mechanism for data protection and encode the DNA data into many DNA strings, each with multiple (metadata) tags on them. One of tags is the PCR primer tag header, another tag indicates the position of the DNA data segment in the file and an end of data tag that is the same PCR primer tag.

The PCR primer tag was used as sort of a file address. They could configure a complementary PCR tag to match the primer tag of the file they wanted to access and then use the PCR process to replicate (amplify) only those DNA segments that matched the searched for primer tag.

Apparently the researchers chunk file data into a block of 150 base pairs. As there are 2 complementary base pairs, I assume one bit to one base pair mapping. As such, 150 base pairs or bits of data per segment means ~18 bytes of data per segment. Presumably this is to allow for more efficient/effective encoding of data into DNA strings.

DNA strings don’t work well with replicated sequences of base pairs, such as all zeros. So the researchers created a random sequence of 150 base pairs and XOR the file DNA data with this random sequence to determine the actual DNA sequence to use to encode the data. Reading the DNA data back they need to XOR the data segment with the random string again to reconstruct the actual file data segment.

Not clear how PCR replicated DNA segments are isolated and where they are originally decoded (with a read head). But presumably once you have thousands to millions of copies of a DNA segment,  it’s pretty straightforward to decode them.

Once decoded and XORed, they use the R-S erasure coding scheme to ensure that the all the DNA data segments represent the actual data that was encoded in them. They can then use the position of the DNA data segment tag to indicate how to put the file data back together again.

What’s missing?

I am assuming the cellular data storage system has multiple distinct cells of data, which are clustered together into some sort of organism.

Each cell in the cellular data storage system would hold unique file data and could be extracted and a file read out individually from the cell and then the cell could be placed back in the organism. Cells of data could be replicated within an organism or to other organisms.

To be a true storage system, I would think we need to add:

  • DNA data parity – inside each DNA data segment, every eighth base pair would be a parity for the eight preceding base pairs, used to indicate when a particular base pair in eight has mutated.
  • DNA data segment (block) and file checksums –  standard data checksums, used to verify and correct for double and triple base pair (bit) corruption in DNA data segments and in the whole file.
  • Cell directory – used to indicate the unique Cell ID of the cell, a file [name] to PCR primer tag mapping table, a version of DNA file metadata tags, a version of the DNA file XOR string, a DNA file data R-S version/level, the DNA file length or number of DNA data segments, the DNA data creation data time stamp, the DNA last access date-time stamp,and DNA data modification data-time stamp (these last two could be omited)
  • Organism directory – used to indicate unique organism ID, organism metadata version number, organism unique cell count,  unique cell ID to file list mapping, cell ID creation data-time stamp and cell ID replication count.

The problem with an organism cell-ID file list is that this could be quite long. It might be better to somehow indicate a range or list of ranges of PCR primer tags that are in the cell-ID. I can see other alternatives using a segmented organism directory or indirect organism cell to file lists b-tree, which could hold file name lists to cell-ID mapping.

It’s unclear whether DNA data storage should support a multi-level hierarchy, like file system  directories structures or a flat hierarchy like object storage data, which just has buckets of objects data. Considering the cellular structure of DNA data it appears to me more like buckets and the glacial access seems to be more useful to archive systems. So I would lean to a flat hierarchy and an object storage structure.

Is DNA data is WORM or modifiable? Given the effort required to encode and create DNA data segment storage, it would seem it’s more WORM like than modifiable storage.

How will the DNA data storage system persist or be kept alive, if that’s the right word for it. There must be some standard internal cell mechanisms to maintain its existence. Perhaps, the researchers have just inserted file data DNA into a standard cell as sort of junk DNA.

If this were the case, you’d almost want to create a separate, data  nucleus inside a cell, that would just hold file data and wouldn’t interfere with normal cellular operations.

But doesn’t the PCR primer tag approach lend itself better to a  key-value store data base?

Photo Credit(s): Cell structure National Cancer Institute

Prentice Hall textbook

Guide to Open VMS file applications

Unix Inodes CSE410 Washington.edu

Key Value Databases, Wikipedia By Clescop – Own work, CC BY-SA 4.0, Link

A knowledge ark, the Arch project

Posted on by Ray in Data longevity, Storage archive

Read an article last week on the Arch Mission Foundation project, which is a non-profit, organization that intends “to continuously preserve and disseminate human knowledge throughout time and space”.

The way I read this is they want to capture, preserve  and replicate all mankind’s knowledge onto (semi-)permanent media and store this information  at various locations around the globe and wherever we may go.

Interesting way to go about doing this. There are plenty of questions and considerations to capturing all of mankind’s knowledge.

Google’s way

 Google has electronically scanned every book in a number of library partners to help provide a searchable database of literature, check out the Google Books Library Project.

There’s over 40 library partners around the globe and the intent of the project was to digitize their collections. The library partners can then provide access to their digital copies. Google will provide full access to books in the public domain and will provide search results for all the rest, with pointers as to where the books can be found in libraries, purchased and otherwise obtained.

Google Books can be searched at Google Books. Last I heard they had digitized over 30M books from their library partners, which is pretty impressive since the Library of Congress has around 37M books. Google Books is starting to scan magazines as well.

Arch’s way

The intent is to create Arch’s (pronounced Ark’s) that can last billions of years. The organization is funding R&D into long lived storage technologies.

Some of these technologies include:

  • 5D laser optical data storage in quartz, I wrote about this before (see my 5D storage … post). Essentially, they are able to record two-tone scans of documents in transparent quartz that can last eons. Data is recorded in 5 dimensions, size of dot, polarity of dot  and 3 layers of dot locations through the media. 5D media lasts for 1000s of years.
  • Nickel ion-beam atomic scale storage, couldn’t find much on this online but we suppose this technology uses ion-beams to etch a nickel surface with nano-scale information.
  • Molecular storage on DNA molecules, I wrote about this before as well (see my DNA as storage… post) but there’s been plenty of research on this more recently. A group from Padua, IT  shows the way forward to use bacteria as a read/write head for DNA storage and there are claims that a gram of DNA could hold a ZB (zettabyte, 10**21 bytes) of data. For some reason Microsoft has been very active in researching this technology and plan to add it to Azure someday.
  • Durable space based flash drives, couldn’t find anything on this technology but assume this is some variant of NAND storage optimized for long duration.  Current NAND loses charge over time. Alternatively, this could be a version of other NVM storage, such as, MRAM, 3DX, ReRAM, Graphene Flash, and  Memristor all of which I have written about
  • Long duration DVD technology, this is sort of old school but there exists archive class WORM DVDs out and available on the market today, (see my post on M[illeniata]-Disc…).
  • Quantum information storage, current quantum memory lifetimes don’t much over exceed 180 seconds, but this is storage not memory. Couldn’t find much else on this, but it might be referring to permanent data storage with light.

M-Disc (c) 2011 Millenniata (from their website)
M-Disc (c) 2011 Millenniata (from their website)

They seem technology agnostic but want something that will last forever.

But what knowledge do they plan to store

In Arch’s FAQ they talk about open data sets like Wikipedia and the Internet Archive. But they have an interesting perspective on which knowledge to save. From an advanced future civilization perspective, they are probably not as interested in our science and technology but rather more interested in our history, art and culture.

They believe that science and technology should be roughly the same in every advanced civilization. But history, art and culture are going to be vastly different across different civilizations. As such, history, art and culture are uniquely valuable to some future version of ourselves or any other advanced scientific civilization.

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Arch intends to have multiple libraries positioned on the Earth, on the Moon and Mars over time. And they are actively looking for donations and participation (see link above).

Although, I agree that culture, art and history will be most beneficial to any advanced civilization. But there’s always a small but distinct probability that we may not continue to exist as an advanced scientific civilization. In that case, I would think, science and technology would also be needed to boot strap civilization.

To the Wikipedia, I would add GitHub, probably Google Books, and PLOS as well as any other publicly available scientific or humanities journals that available.

And don’t get me started on what format to record the data with. Needless to say, out-dated formats are going to be a major concern for anything but a 2D scan of information after about ten years or so.

In any case, humanity and universanity needs something like this.

Photo Credit(s): The Arch Mission Foundation web page

Google Books Library search on Republic results

“Five dimensional glass disks …” from The Verge

M-disk web page

Genome informatics takes off at 100GB/human

Posted on by Ray in Data compression, Data reduction, Information economy, Storage Features, Strategic Inflection Points, Uncategorized

All is One, the I-ching and Genome case by TheAlieness (cc) (from flickr)
All is One, the I-ching and Genome case by TheAlieness (cc) (from flickr)

Read a recent article (actually a series of charts and text) on MIT Technical Review called Bases to Bytes which discusses how the costs of having your DNA sequenced is dropping faster than Moore’s law and how storing a person’s DNA data now takes ~100GB.

Apparently Nature magazine says ~30,000 genomes have been sequenced (not counting biotech sequenced genomes), representing ~3PB of data.

Why it takes 100GB

At the moment DNA sequencing is not doing any compression, no deduplication nor any other storage efficiency tools to reduce this capacity footprint.  The 3.2Billion DNA base pairs each would take a minimum of 2 bits to store which should be ~800MB but for some reason more information about each base is saved (for future needs?) and they often re-sequence the DNA multiple times just to be sure (replica’s?).  All this seems to add up  to needing 100GB of data for a typical DNA sequencing output.

How they go from 0.8GB to 100GB with more info on each base pair and multiple copies or 125X the original data requirement is beyond me.

However, we have written about DNA informatics before (see our Dits, codons & chromozones – the storage of life post).  In that post I estimated that human DNA would need ~64GB of storage, almost right on.  (Although there was a math error somewhere in that analysis. Let’s see, 1B codons each with 64 possibilities [needing 6 bits] should require 6Bbits or ~750MB of storage, close enough).

Dedupe to the rescue

But in my view some deduplication should help.  Not clear if it’s at the Codon level or at some higher organizational level (chromosome, protein, ?)  but a “codon-differential” deduplication algorithm might just do the trick and take DNA capacity requirements down to size.  In fact with all the replication in junk DNA, it starts to looks more and more like backup sets already.

I am sure any of my Deduplication friends in the industry such as EMC Data Domain, HP StoreOnce, NetApp, SEPATON, and others would be happy to give it some thought if adequate funding were to follow.  But with this much storage at stake, some of them may take it on just to go after the storage requirements.

Gosh with a 50:1 deduplication ratio, maybe we could get a human DNA sequence down to 2GB.  Then it would only take 14EB to sequence the worlds 7B population today.

Now if we could just sequence the human microbiome with metagenomic analysis of the microbiological communities of organisms that live upon, within and around all of us.  Then we might have the answer to everything biologically we wanted to know about some person.

What we could do with all this information is another matter.

Comments?

DNA as storage, the end of evolution – part 2

Posted on by Ray in Distributed computing, Strategic Inflection Points, Strategic planning

I had talked about DNA programming/computing previously (see my DNA computing and the end of natural evolution post) and today we have an example of  another step along this journey.  A new story in today’s Science News titled DNA used as rewriteable data storage in cells discusses another capability needed for computation, namely information storage.

The new synthetic biology “logic” is able to record, erase and overwrite (DNA) data in an E. coli cell.  DNA information storage like this brings us one step closer to a universal biologic Turing machine or computational engine.

Apparently the new process uses enzymes to “flip” a small segment of DNA to read backwards and then with another set of enzymes, flip it back again.  With another application of synthetic biology, they were able to have the cell fluoresce in different colors depending on whether the DNA segment was reversed or in its normal orientation.

To top it all off, the DNA data storage device was inheritable.   Scientists showed that the data device was still present in the 100th generation of the cell they originally modified.  How’s that for persistent storage.

The universal biological Turing machine

Let’s see, my universal Turing machine parts list includes:

  • Tape or infinite memory device = DNA memory device – Check (todays post, well maybe not infinite, but certainly single bits today, bytes next year, so it’s only a matter of time before it’s KB)
  • Read head or ability to read out memory information = biological read head – Check (todays post, it can fluoresce, therefore it can be read)
  • State register = biologic counter  – Check (seems to have been discovered in 2009, see Science News article Engineered DNA counts it out, don’t know how I missed that)
  • State transition table or program = biological programming – Check (previous post plus today’s post, able to compute a new state from a given previous state and current data and write or rewrite data).

As far as I can tell this means we could construct an equivalent to a universal turing machine with today’s synthetic biology. Which of course means we could perform  just about any computation ever conceived within a single cell AND all generations of the cell would inherit this ability.

End of natural evolution, …

Gosh the possibilities of this new synthetic biological turing machine are both frightening and astonishing.  My original post talked about how adding ECC like functionality plus a ECC codeword to human DNA strand would spell the end of natural evolution for our species.

I suppose the one comforting thought is that flipping DNA segments takes hours rather than nano-seconds which means biological computation will never displace electronic/optronic computation.  But biological computation really doesn’t have to.  All it has to do is repair DNA mutations over the course of days, weeks and/or years, before it has a chance to propagate in order to end natural evolution.

…,  the dawn of un-natural evolution

Of course with such capabilities, “un-natural” or programmed evolution is quite possible but is it entirely desireable.  With such capabilities we could readily change a cell’s DNA to whatever we desire it to be.

My real problem is its inheritability.  It’s one thing to muck with a persons genome, it’s another thing to muck with their children’s, children’s, children’s, … DNA.

Let’s say you were able to change someone’s DNA to become a super-athelete, super-brain or super-beautiful/handsome person.  (Moving from a single cell’s DNA to a whole person’s is a leap, but not outside the realm of possibility).   Over time, any such changes would accumulate and could confer an seemingly un-assailable advantage to an individual’s gene line.

There’s probably some time to think these things through and set up some sort of policies, guidelines, and/or regulations environment around the use of the technology before capabilities get out of hand.

In my mind this goes well beyond genetically modified organisms (GMO) organisms that are just static changes to a gene line.  Programming gene lines to repair DNA, alter DNA, or even to make better copies, seems to me to be an order of magnitude increase in new capabilities taking us to genetically programmed organisms that has the potential to end evolution itself.

We need to have some serious discussions before it goes that far.

Comments?

Image: E. coli GFP by KitKor