Cells Are Like Robust Computational Systems

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.


“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.”


5 responses to “Cells Are Like Robust Computational Systems

  1. Hi Michael,

    I stopped by to leave a link about our cellular networks and biology. And voila! Haha…

    Have you seen the work being done by MicroSoft Research in disease research? Gates started up a project not long ago based out of Italy that treats biological entities as programs.

    I found it by accident, looking up an old quote of his about the cell containing the best software he’d ever seen.

    Algorithmic systems biology

    “This work and other projects pursued at the Trento centre are based on the concept of “algorithmic systems biology.” A video on the CoSBi Web site defines the term as representing biological entities as programs.

    “The interaction of two entities,” Priami explains in the video, “becomes the exchange of a message between the programs, and the simultaneous execution of the programs simulates the dynamics of a biological system.”

    The article also has links to free shareware for researchers. 5 different modeling apps and utilities.

    “On June 9, 2005, a Research Project Agreement was signed by Bassi and Stephen Emmott, then director of the External Research Office at Microsoft Research Cambridge and now head of Computational Science at the Cambridge lab. CoSBi was inaugurated on Dec. 7 of that year, complete with a message of well wishes from then-Italian President Carlo Azeglio Ciampi. Scientific activities began the following April.”

    They won their first award this year for Formal Methdos in Molecular Biology.


    “Now, the centre includes 25 research personnel, an international, multidisciplinary—computer science, physics, chemistry, mathematics, theoretical biology, electronic engineering—assemblage that includes three senior researchers, three researchers, nine junior researchers, seven Ph.D. students, and three developers.”

    The lab is fascinating as well…
    CoSBi – Computational Systems Biology Lab – CompScie research

    This confirms that science is only now truly beginning to make substantial entry points into the Code. Understanding the need for formal systems logic. Only a Computer Scientist, like Gates could look at the cell and see software. IBM sees it too. They’re spending billions along with many other companies in this race.

    Think if your company was the first to unlock a formalized language system for modeling our molecular reality? It would be greater than any current company’s findings in history. It blows rocket science away for a long time.

    Truly good stuff. I recommend checking out their lab, their goals, and their ideas about how to model biological systems through a series of network programs.

    The two main goals of CoSBi are understanding biology(or enhancing it through cures) and enchancing computer science

    Now that is telling for our future. Gates and everyone involvded understands the race is on to improve computer systems, we need to learn how to compress information like DNA in a double helix and dynamically access it by multiple programs and network architecture that never stops replicating itself.


  2. I thought I’d share a their statements regarding how they view biology, the current state of research and what is needed for the future.

    Computing and biology have been converging over the past two decades. At first, biological research approached computing under the push of technological needs, leading to the development of bioinformatics.

    More recently, the intriguing relationship between the philosophical aspects of computing and the digital roots of biological information processing has fostered a peer-to-peer crosstalk of the two sciences, and computer science is joining mathematics, chemistry and physics as a foundational pillar of systems biology.

    This shift in the role of computing is propelling our approach. We are engaged in something new, which aims at devising proper abstractions of living systems in order to capture their intrinsic concurrency, causality and probabilistic nature into algorithmic descriptions that can be executed, analyzed and simulated by computers. We call our approach algorithmic systems biology.

    As per Nobel Laureate Sydney Brenner:

    Biology needs a theory able to highlight causality and abstract data into knowledge to elucidate the architecture of biological complexity.

    We believe algorithmic systems biology is able to take this challenge. Algorithmic approaches require modelers/biologists to think about the mechanisms governing the behavior of the system under question and favor computational thinking.

    Algorithms can help in coherently extracting general biological principles that underlie the enormous amount of data produced by high-throughput technologies. In a paper published recently in Nature, Nobel Laureate Paul Nurse advocates that a better understanding of living organisms requires both the development of the appropriate languages to describe information processing in biological systems and the generation of more effective methods to translate biochemical descriptions into the functioning of the logic circuits that underpin biological phenomena.

    Algorithmic descriptions of biological systems need a syntax to be described and a semantics to associate them with their intended meaning so that an executor can precisely perform the steps needed to implement the algorithms with no ambiguity.

    Both theoretical and practical results developed in the realm of programming languages are available to support the analysis of models and their translation into executable forms for numerical evaluation and simulation.
    By relying on core computer science technologies like algorithms, programming languages and compilers, the main challenges we are addressing with algorithmic systems biology include:

    • the relationships between genotype and phenotype, that is, between low-level local interactions and emergent high-level global behavior
    • how to cope with partial knowledge of the systems under investigation
    • efficient management of multi-level and multi-scale systems in time, space and size
    • showing causal relations between interactions

    To tackle these formidable challenges, modeling formalisms which are candidates to propel algorithmic systems biology should complement and interoperate with mathematical modeling, address parallelism and complexity, express causality and be algorithmic, quantitative, interaction-driven, composable, scalable and modular.

    In algorithmic systems biology, biological problems define the requirements of conceptual tools and provide the case studies for validating and benchmarking proposed solutions.

    Hope block quotes worked. Essentially, they have recognized the digital code within us all and the code we’ve created externally now are “convergent” and want to proceed as if we are reverse engineering a vast computer network called life.

    To cool… really enjoy this, because they’re recognizing the best way ahead is through the awareness of functionality in biological algorithms to help create better digital code for computer processing. I’ve been a champion of this for a long time.

    I’ve never understood why others could not see the Code any other way than a beautiful design. If Crick could recognize it 50yrs ago, why continue to push it back?

    Great thing about entrepreneurs is they do not wait for political correctness, they forge ahead.

  3. Design Theorist win in the end… We are into a new paradigm of biological sciences. Whatever happened in the past, the future is going to change everything about how we think about life.

    Questions can be answered about “gradualism” vs rapid speciation, etc. FrontLoading comes into view like the thoughts you cited Miker earlier from Life Engineers looking into the future.

    Exciting times ahead.

  4. Good stuff, datcg. Thanks.

  5. I’m curious about the backup or paralog genes. Would a designer have included them in the original design? Is there a way to tell if they were in LUCA?

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