System Biology Tuned Computer Architecture

Transcription

1 System Biology Tuned Computer Architecture Nader Bagherzadeh and Jean-Luc Gaudiot PASCAL: PArallel Systems and Computer Architecture Lab. University of California, Irvine

2 Computational Requirements System Biology demands: Massive amounts of data for processing and retrieval Distributed and parallel processing Distributed memory organization Self organization and organic computing Bottom up approach Decentralization

3 Unique Cell Features Cells are processing elements that will die eventually Cell blueprint, cornerstone of cell computational tasks, is essentially passed on to the next generation Cells are based on chemistry that also perform some if information processing

4 How to Proceed Abstract Machine Define unique features in an abstract machine, similar to Java Direct Implementation Seek unique features of the cell interaction and directly implement them in a von-neuman architecture Revolutionary Approach Utilize material (carbon nano tubes) and architectural concepts that are non-standard (self organization)

5 Example of an Abstract Machine (Luca Cardelli, Microsoft research)

6 Using nanotechnology to build an automaton that cures cancer at the molecular level. PASCAL: PArallel Systems & Computer Architecture Lab. 6

7 Problem Current cancer therapies are not precise. Most of the cancer therapies introduce side effects. Cancer treatments are expensive. Need a cancer therapy that is precise and inexpensive with minimized side effects. DNA-based Killer Automaton: multitasked molecular-level automaton that is able to detect and kill cancerous cells. We based this project upon Shapiro s DNA machine model and the suicide gene therapy bystander effect. PASCAL: PArallel Systems & Computer Architecture Lab. 7

8 Shapiro s DNA Automata Shapiro s Automaton is a small (56 bp) DNA machine that is able to detect and block cancer expression. It consists of two parts: cancer detectors that detect the existence of mrna cancer indicators and anti-cancer drugs (single-stranded stranded DNA) that block the translational cancer expression. It also requires FoKI enzyme to cut the cancer detection site. PASCAL: PArallel Systems & Computer Architecture Lab. 8

9 DNA-based Killer Automaton (Structure) PASCAL: PArallel Systems & Computer Architecture Lab. 9

10 PASCAL: PArallel Systems & Computer Architecture Lab. 10

11 The Bystander Effect The Bystander Effect is a biological phenomenon observed in suicide gene therapy. In suicide gene therapy, cytotoxic materials, such as ganciclovir triphosphate (GCVTP), is produced in some of the cancer cells (host cancer cells). In addition to killing these host cancer cells, GCVTPs are able to propagate to neighboring non-host cancer cells through the Gap Junctional Intercellular Communication (GJIC) channels, therefore, exert toxic effects on these neighboring cancer cells as well. PASCAL: PArallel Systems & Computer Architecture Lab. 11

12 The Bystander Effect (cont.) Tumor cells may have functional gap junctions amongst themselves (homologous GJIC), but may not form permeable gap junctions with non-transformed cells (heterologous GJIC). The inability of tumor cells to communicate with normal cells was not due to the expression of inappropriate connexins, but might result from differences in the cell surfaces. PASCAL: PArallel Systems & Computer Architecture Lab. 12

13 DNA-based Killer Automaton DNA-based Killer Automaton (DKA) is a multitasked molecular-level automaton that is able to detect and kill cancerous cells. It has the same structure as Shapiro s Automata, except that the hairpin-structured single-stranded stranded DNA drug is replaced by GCVTP. The DKA cancer detection mechanism is the same as the one in Shapiro s model. The drug released by DKA, GCVTP, will kill the cell and propagate to nearby cancerous cells through the GJIC channels. PASCAL: PArallel Systems & Computer Architecture Lab. 13

14 PASCAL: PArallel Systems & Computer Architecture Lab. 14

15 DNA COMPUTING PASCAL: PArallel Systems and Computer Architecture Lab. University of California, Irvine

16 Definition DNA Computing is a newly emerging computing paradigm that is able to outperform the state-of-the-art digital computers in computing speed, power consumption, storage space, and cost. DNA computing naturally integrates the fields of biochemistry, molecular biology, and computer science. Within computer science, research focus on models and algorithms for computing with biomolecules. PASCAL: PArallel Systems & Computer Architecture Lab. 16

17 What is DNA Computing? DNA Computing is a newly emerging computing paradigm, which utilizes DNA strands, instead of silicon transistors, as the basic computing units. In digital computers, strings of 1 s and 0 s are used to represent information; in DNA computers, biochemical bases adenine (A), cytosine (C), thymine (T), and guanine (G) are used to represent information. Why DNA? While the amount of silicon is limited, there is an unlimited supply of DNA as long as there are cellular organisms. DNA is much cheaper. DNA is not toxic to the environment. PASCAL: PArallel Systems & Computer Architecture Lab. 17

18 Advantages of DNA Computing Massively Parallel Computing The Watson-Crick structure enables DNA computers to process data in parallel while traditional digital computers can only process data in series Energy efficient A DNA computer could perform 10^19 operations per joule while the existing supercomputers perform 10^9 operations per joule. Huge memory space E.g. A regular transistor is one micron wide while a DNA base is only two nanometers wide PASCAL: PArallel Systems & Computer Architecture Lab. 18

19 Disadvantages of DNA Computing The state-of-the-art DNA computer takes only milliseconds to calculate, but requires days to extract the final answer through biological i l operations. DNA Computing is heuristic: there is no guarantee that an optimal solution will be found. Errors occur frequently Application specific Manual lintervention ti by human is required PASCAL: PArallel Systems & Computer Architecture Lab. 19

20 Source: Stanford Medicine Magazine, Vol 19, 3 Nov PASCAL: PArallel Systems & Computer Architecture Lab. 20

21 Source: Tokyo Techno Forum 21, 21 June PASCAL: PArallel Systems & Computer Architecture Lab. 21

22 Research Challenges Fault tolerance Structure Design Find a strand that folds to a specified secondary structure DNA word design Find a large set of non-interfering DNA words PASCAL: PArallel Systems & Computer Architecture Lab. 22

23 Genetic Programming or Why is Sex Good? ) Similar to GA but programs are tree-like rather than linear Reproduction is sexual Application: Recognition of Targets in Infrared Background Parallel Programming PASCAL: PArallel Systems & Computer Architecture Lab. 23