Machines that make Machines?

An interesting article authored by Antoine Danchin from the Pasteur Institut was recently published and is sure to bring forth much discussion.

Bacteria as computers making computers
The abstract:

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

The quantum teleportation experiments have demonstrated that information can be viewed as a fundamental irreducible property of physics (informationalism). Information in the sense that energy supervenes on information. Energy is understood to be the ultimate foundation of all matter in this universe. From Einstein’s equation, E=mc^2, all matter was ultimately created out of energy, and is theoretically reducible to energy. From there it can also be derived that time comes to complete stop at the speed of light. In addition, the first law of thermodynamics states that energy cannot be created or destroyed. The quantum teleportation experiments showed the entire information content (properties) of one photon can be teleported instantaneously onto another photon whereby the second photon assumes the complete identity of the first photon, while the first photon loses its complete identity. So from there, energy can be viewed to supervene on information, and information can be viewed as a fundamental category of Nature.

Systems biology is moving in that same direction, as viewing cells as computers with machinery and software makes it possible to view information as a fundamental category of nature and all future developments of systems biology can include this concept when looking at cells.

There are many interesting passages in this article. A few of these are going to be highlighted for discussion.
From the article:

Historically, systems biology follows on from molecular biology, a science based on many concepts more closely linked to arithmetic and computation than to classical physics or chemistry. [b]Molecular biology relies heavily on concepts such as ‘control’, ‘coding’ or ‘information’, which are at the heart of arithmetic and computation.[/b] To accept the cell as a computer conjecture first requires an exploration of the concept of information, in relation to the concept of genetic program.

Cellular processes are exquisitely controlled and carried out by remarkable biomolecular machines. The software needed to coordinate these processes is located in a very optimal genetic code that is optimized for evolution and maintains its own functional integrity.

From the article:

The Austrian mathematician Kurt Godel showed that arithmetic (the science of whole numbers) can make statements about itself. To substantiate this remarkable claim, which implies that just manipulating whole numbers with the rules of arithmetic can generate novel information, G¨odel used a simple trick. He coded the words used in Number Theory as integers (e.g. four, which is quatre in French, vier in German and tessera in Greek, can be coded by 4) and used the corresponding code to translate propositions of arithmetic. This generated a large whole number, which could be manipulated by the rules of arithmetic, and after a sequence of operations, this manipulation generated another whole number. The latter could be decoded using the initial code. Godel’s trick was to drive the sequence of operations modifying the initial statement, to lead to a very particular conclusion. When decoded, the manipulated sequence translated into a particular proposition, which, briefly, stated: ‘I am impossible to prove’. In other words, arithmetic is incomplete, i.e. some propositions of arithmetic can be understood as valid; yet they cannot be proven within the frame of arithmetic. But this ‘incompleteness’ can also be seen as a positive feature; it is what allows the creation of new information – in Godel’s case, the statement of a fact of which the world was previously unaware. In his book, Hofstadter showed that the genetic code, which enables the world of nucleic acids to be translated into the world of proteins, which in turn manipulate nucleic acids, behaves exactly as Godel’s code does. This implies that manipulating strings of symbols, via a process that uses a code, can generate novel information. [b]Of course, in the case of nucleic acids and proteins, there is no Godel to drive the process, and no need for one: while Godel knew what he was aiming at, living systems will accumulate information through recursivity, without any design being required. We only perceive a design because the end result is familiar to us, and thus seems more ‘right’ than any other possible result. But what we commonly term the ‘genetic program’ because it unfolds through time in a consistent manner is not a programme with an aim – it is merely there, and functions because it cannot do otherwise.[/b]

Why can’t the function of the program be to actively manipulate information as a means to an end… self-replication and preservation. Later in the article something similar to this is actually suggested:

From the article:

[b]The reluctance of investigators to regard information as an authentic category of Nature suggests that, at this point in the present review of the literature, it may still be difficult for the reader to accept that a cell could behave as a computer.[/b] Indeed, what would the role of computation be in the process of evolution? We have already provided some elements of the answer to the question: Turing showed that the consequence of the process of computation along the lines he outlined is that his machine would be able to perform any conceivable operation of logic or computation by reading and writing on a data/program tape. Stated otherwise, and in a way that is easier to relate to biology, the machine manipulates information and, because arithmetic is incomplete [as illustrated in the introduction above (Hofstadter, 1979)], it is able to create information. The machine is therefore in essence unpredictable (Turing, 1936–1937), but not in a random way – quite the contrary, in a very interesting way, as lack of prediction is not due to lack of determinism, but due to a creative action that results in novel information. [b]If the image is correct, then it shows that living organisms are those material systems that are able to manipulate information so as to produce unexpected solutions that enable them to survive in an unpredictable future (Danchin, 2003, 2008a).[/b]
There we go, organisms can be viewed as entities that are able to manipulate information as a means to an end. Why would it be difficult to accept that cells to behave like computers? Yet, cells are capable of more than computers, e.g. self-replication and autonomous manipulation of information.

The article continues to discuss at length the parallels between our own created information processing systems (computers) and molecular processes fundamental to life. With more and more information being gathered on cellular mechanisms, cells can be seen as computers (machines expressing various programs), that are not only able to govern cellular processes needed to sustain the software, but also contains the necessary software and machinery to reproduce the computing machine while replicating its program.

In order to demonstrate the validity of viewing cells as computers that are able to manipulate information, consider the following finding.

The Ribosome: Perfectionist Protein-maker Trashes Errors

[QUOTE]ScienceDaily (Jan. 9, 2009) — The enzyme machine that translates a cell’s DNA code into the proteins of life is nothing if not an editorial perfectionist.

[QUOTE]It turns out, the Johns Hopkins researchers say, that the ribosome exerts far tighter quality control than anyone ever suspected over its precious protein products which, as workhorses of the cell, carry out the very business of life.

“What we now know is that in the event of miscoding, the ribosome cuts the bond and aborts the protein-in-progress, end of story,” says Rachel Green, a Howard Hughes Medical Institute investigator and professor of molecular biology and genetics in the Johns Hopkins University School of Medicine. “There’s no second chance.” Previously, Green says, molecular biologists thought the ribosome tightly managed its actions only prior to the actual incorporation of the next building block by being super-selective about which chemical ingredients it allows to enter the process.

Because a protein’s chemical “shape” dictates its function, mistakes in translating assembly codes can be toxic to cells, resulting in the misfolding of proteins often associated with neurodegenerative conditions. Working with bacterial ribosomes, Green and her team watched them react to lab-induced chemical errors and were surprised to see that the protein-manufacturing process didn’t proceed as usual, getting past the error and continuing its “walk” along the DNA’s protein-encoding genetic messages.

"We thought that once the mistake was made, it would have just gone on to make the next bond and the next," Green says. “But instead, we noticed that one mistake on the ribosomal assembly line begets another, and it’s this compounding of errors that leads to the partially finished protein being tossed into the cellular trash,” she adds.
So what is being monitored by the ribosome? Information. Material representations (amino acid sequence vs DNA sequence) of information. But, it does not only monitor it, it manipulates it as a means to an end… fidelity.

[QUOTE]To their further surprise, the ribosome lets go of error-laden proteins 10,000 times faster than it would normally release error-free proteins, a rate of destruction that Green says is “shocking” and reveals just how much of a stickler the ribosome is about high-fidelity protein synthesis.

“These are not subtle numbers,” she says, noting that there’s a clear biological cost for this ribosomal editing and jettisoning of errors, but a necessary expense.

“The cell is a wasteful system in that it makes something and then says, forget it, throw it out,” Green concedes. “But it’s evidently worth the waste to increase fidelity. There are places in life where fidelity matters.
The ribosome is optimized to manipulate information for fidelity.

Cells Are Like Robust Computational Systems, Scientists Report

[QUOTE]ScienceDaily (June 16, 2009) — Gene regulatory networks in cell nuclei are similar to cloud computing networks, such as Google or Yahoo!, researchers report today in the online journal Molecular Systems Biology. The similarity is that each system keeps working despite the failure of individual components, whether they are master genes or computer processors.
Resiliency and redundancy… signs of an optimal system. Back up mechanism are also good ideas to preserve such optimal systems. The similarities between our own designed systems and cellular mechanisms are obvious.

[QUOTE]This finding by an international team led by Carnegie Mellon University computational biologist Ziv Bar-Joseph helps explain not only the robustness of cells, but also some seemingly incongruent experimental results that have puzzled biologists.

"Similarities in the sequences of certain master genes allow them to back up each other to a degree we hadn’t appreciated," said Bar-Joseph, an assistant professor of computer science and machine learning and a member of Carnegie Mellon’s Ray and Stephanie Lane Center for Computational Biology.

Between 5 and 10 percent of the genes in all living species are master genes that produce proteins called transcription factors that turn all other genes on or off.
Many diseases are associated with mutations in one or several of these transcription factors. However, as the new study shows, if one of these genes is lost, other “parallel” master genes with similar sequences, called paralogs, often can replace it by turning on the same set of genes.

That would explain the curious results of some experiments in organisms ranging from yeast to humans, in which researchers have recently identified the genes controlled by several master genes. Researchers have been surprised to find that when they remove one master gene at a time, almost none of the genes controlled by that master gene are de-activated.

In the current work, the Carnegie Mellon researchers and their colleagues in Israel and Spain identified the most probable backup for each master gene. They found that removing the master genes that had very similar backups had almost no noticeable effect, but when they removed master genes with less similar backups, the effect was significant. Additional experiments showed that when both the master gene and its immediate backup were removed, the effects became very noticeable, even for those genes with a similar backup gene. In one example, when the gene Pdr1 was removed, researchers found almost no decrease in activation among the genes it controls; when Pdr1 and its paralog were removed, however, 19 percent of the genes Pdr1 controls failed to activate.

“It’s extremely rare in nature that a cell would lose both a master gene and its backup, so for the most part cells are very robust machines,” said Anthony Gitter, a graduate student in Carnegie Mellon’s Computer Science Department and lead author of the Nature MSB article. “We now have reason to think of cells as robust computational devices, employing redundancy in the same way that enables large computing systems, such as Amazon, to keep operating despite the fact that servers routinely fail.”

In addition to Bar-Joseph and Gitter, the authors include Itamar Simon, Zehava Siegfried and Michael Klutstein of Hebrew University Medical School in Jerusalem, Oriol Fornes of the Municipal Institute for Medical Research in Barcelona, and Baldo Oliva of Pompeu Fabra University, also in Barcelona.

This work was supported by grants from the National Science Foundation and the National Institutes of Health. Molecular Systems Biology is a peer-reviewed journal published by Nature Publishing Group.