Monday, August 25, 2014

An Overview of HPC and Computational Science in the USA

Found a good talk on Computational Science, and its relationship to HPC from Oak Ridge National Labs in the USA. This is highly related to what I used to do - that was tools that measure the performance of scientific application ensembles on the big computational systems in the US.

When I had started viewing this video, I was hoping for some concrete reference of the useful scientific tools for computational scientists using HPC. It turned out to be an overview, which I still found helpful as a big picture and am hence sharing here. There were a couple of slides that appeared useful to me however.

Monday, August 11, 2014

What is the Simplest DNA code to build the Simplest Self-Replicating Prokaryotic Cell?

This is a thought experiment meant to bridge the gap between reality, and my desire to understand how to start modeling a language for DNA.

The question here is - can we build a cell whose only function is to self-replicate? No DNA error corrections (it might be interesting to see how fast the replication falls apart if we allowed this.) No unnecessary machinery outside of the ability to absorb/create new material to grow the cell for division, and to support the division process itself.

I believe we know the genes responsible for some or all of these processes already. Because I have no clue how free-floating DNA can create a cell-wall around it - a mystery of first life methinks, I think this can be achieved by inserting the code into some simple existing simple Prokaryotic cell. As the cells divide, all the old functionality and internal structures go away.

From these simple artificial cell forms, I think we can start to inject functionality into its DNA. This should allow us to establish a baseline to determine how (if) DNA code influences timing, the input/output functions in the form of concentrations of proteins and amino acids. I believe this had already been done at some higher level.

Thursday, September 19, 2013

Google, Apple, and Life

This looks to be interesting, and in line with my own dreams that we can understand life as an engineering system:

http://content.time.com/time/magazine/article/0,9171,2152422,00.html


Tuesday, July 2, 2013

OpenPCR

Thanks to a friend, this (relatively) cheap tool might prove useful to me. This is pretty much a note-to-self. I have no idea what I might use it for just yet ... perhaps experimental validation of DNA computational models.

http://openpcr.org/what-is-pcr

Edit: Found another interesting link from the same article -

http://genspace.org/

At this point, I might as well link the original article itself (I have it on my random thoughts blog, but I'm starting to think it'd get lost unless I place it here):


"Citizen scientist: Out of the lab and onto the streets"
http://www.newscientist.com/article/mg21829236.300-citizen-scientist-out-of-the-lab-and-onto-the-streets.html?full=true#.UdL7N5xZBbs


Wednesday, May 22, 2013

Some Early Foundational Feasibility Outlines

Spoke with a friend who works with DNA. I had wanted to find out very roughly how DNA strands would behave in clean-room environments (e.g., distilled water). My rationale for these questions is that while we understand how DNA works under natural environments, for the purposes I am gunning for, I'd prefer to consider how DNA would function in artificial environments. This can provide a simple basis for experimental setup to validate statistically-based simulations derived from a computational model with DNA. From this basic model with artificial settings, one can imagine other parameters that can be tuned without concerns about the complexities of natural DNA environments. These can include:
  1. temperature
  2. pressure
  3. density of DNA strands
  4. density of amino acid, protein presence.
The answers so far have been encouraging:

  1. DNA behaves more or less regularly in distilled water, keeping its structure intact. There may be some differences in the way it folds, however and that is important.
  2. DNA will hold under a fair range of temperatures. I'll have to find out what that range is.
  3. It is unclear how DNA will function under different pressures. I imagine they should function without issue over a wide range of pressures, given the presence of deep undersea creatures. Something to find out.
With these parts, it is possible to get started considering computational models under nominal conditions of room temperature and 1 atmosphere of pressure. All that is required is some mechanism for arbitrary DNA sequence construction in a distilled water solution. One of my biggest early interests would be the establishment of a timing mechanism. I would like to see how a mechanism like that could control pulses of "useful work" which can then be observed.

Friday, May 3, 2013

Revisiting DNA as a Computation Model

Just a brief note, perhaps as inspiration to get things organized.

The idea calls out to me again, and a little bit of digging around has surfaced hints from other researchers that:

1) DNA processes can be modeled as a Turing-Complete computing model. I need to find the actual papers that say this and read them in my free time.

2) Peptide-Antibody interactions are also Turing-Complete. Balan et.al. have done some work on this. I'll list these in a later update after I've read the papers.

I am interested to start getting information organized to investigate to what extent bio-chemical interactions can be treated as a Turing-Complete computation model, how far the idea can be taken for practical computing purposes, and what philosophical implications it has for the understanding life and its relationship to chemistry and particle physics.

At the lowest levels, I would like to know:

a. What are the input and output functions of such models of computation? In the case of DNA, if one treats RNA chains as the no-write instruction set, then one could imagine amino acids as the input and proteins as the output, with enzymes being something of that strange in-between which transforms the data for further input/output dynamics I current am nowhere close to understanding.

b. Under what conditions do these bio-chemical interactions work? Assuming water as the medium at first, under what temperatures and pressures can/should we apply our model? How does the chemistry change with changes in temperature and pressure? Do the rates change? Do the component (e.g., amino acids) concentrations change? How does one supply the energy necessary to effect the chemistry desired? Can these parameters somehow be analogous to CPU frequency changes in silicon-based computing chips?

c. What kind of timing mechanisms are employed by life's biochemistry? I have one paper (Howard & Gerdes) with some hints, and I'm hoping to find more. As I understand it, there is an entire orchestration of what are effectively Brownian processes that drive our biological functions. How does the system ensure the smooth operation of this orchestration and under what conditions? How do these timing mechanisms change under the conditions described in point b?

d. Way-out-there ideas - what happens in a medium other than water (e.g., liquid methane like on Titan)? Can other chemical processes involving related atoms (e.g., silicon for carbon, arsenic for phosphorus) hypothetically work? Do they require different energy levels for similar chemical interactions? Do they require a different medium? Can we also create clean-room environments to isolate water-based interactions for computational purposes? Real-life biology is full of other bio-chemicals that need to be accounted for, is my thought.

Finally, how should I get such information organized? Obviously, this blog is a poor media for it. I am thinking of Wikia, but I need to be comfortable with privacy settings and dealing with any intellectual property issues before proceeding. My primary goals are to figure out the landscape of the literature surrounding the topic. The first steps I have taken so far were to establish some baseline faith that bio-chemistry can in fact be made to conform to a Turing-Complete computation model - efforts to move to efficient computation models and implementations can follow from this baseline.

Sunday, August 12, 2012

Epi-Genetics and DNA Computing

I am busy making plans for a long-distance move and so I will have only limited time to put up a stub where questions go today. Will attempt to flesh this out later.

At the local Eugene Science Pub event, a Professor from Pacific University spoke about the concept of nature and nurture in the context of recent work in epigenetics. The topic itself is fascinating and in a nutshell studies the effects of how methyl-groups (CH3) are tagged onto the DNA sequence to disable gene expression (and presumably protein synthesis) as well as tagged onto the proteins around which the DNA sequences are coiled around. In the latter case, the methyl-groups causes the DNA strands to be less tightly wound, exposing them for expression (the reverse of the former case).

On this topic, as a computer scientist and an engineer, I have several vectors of interest:

1) The methyl-groups according to her, are tagged through the aid of enzymes. If I remember my microbiology correctly, this should be similar to the way proteins are constructed - that one part of the enzyme should bind with the methyl-group while the other latches on to other identifier markers on the DNA strand or the proteins. The question really is "how?". I would like to understand the mechanics of it. Surely, this process has to be statistical, right? How would a consistent template of gene expression toggling be achieved by the mechanics of methyl-group tagging across the entire human genome and all the cells in our body? She was talking about how the tags change the regular nature of dominant and recessive gene interplay. Also, even if one assumes some statistical modeling, how do the enzymes tag, so cleanly, some set of specific genes? Do those enzymes identify an entire sub-sequence for tagging? How does the methyl-groups chemically prevent the usual protein synthesis process from working?

2) Now for the stuff I am really excited about (in the long term). Can I make use of this information to design and construct more complex scenarios using methyl-groups to enable yet another model (maybe one that is more flexible) for DNA-based computing? Can I model an environment where I do not have to mimic biology? All I need to do is to respect the chemistry and statistics involved, correct?