User:J. C. Martinez-Garcia/Notebook/HMS Activities/2008/08/13

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Readings on robustness

  1. The first thing to do today is to read something on robustness y biological systems. I will begin with the first chapter of the Andreas Wagner book and then I will write here a planning for further readings.
  2. Meeting with Yitzhak Pilpel:
    • I was trying to find Uri Alon, but he was not in his office, fortunately I met the Visiting Professor Yitzhak Pilpel (his e-mail is, who gave me some information concerning the work done by Ran Kafri as his former PhD student in the Pilpel Lab on Computation Functional Genomics at the Weizmann Institute of Tecnology (he is now working as a postdoctoral visiting fellow in the Kirschner Lab, and his e-mail address is I recovered the files from the Pilipel Lab and I will read them today.
  3. From the lecture of the general introduction of the book by Andreas Wagner:
    • In biological systems complexity does not mean fragility.
    • Genetic disturbance does not cause key organismal functions to fail catastrophically, i.e. organimss are robust.
    • Robustness is a consequence of past evolution.
    • Robusteness affects evolvability, i.e. the potential of future evolution.
    • A biological system is robust if it continues to function in the face of (genetic of envioronmental) perturbations.
    • Two cardinal questions:
      1. What feature of a living organism is robust? (fitness, the ability to survive and reproduce)
        • Fitness is hard to define rigorously.
        • A change in fitness can have many different causes.
        • It is then necessary to analyze, on all levels of organization, the systems that constitute an organism, and that sustain its life.
      2. What kind of change is this feature robust to? (mutational robustness
        • Genetic change (permanent alteration inthe wiring) has more serious consequences than nongenetic change. Three reasons to study mutational robustness:
          1. Genetic change is heritable (has much more serious consequences on organismal linneage that non genetic change).
          2. A comprehensive account of nongenetic change would be daunting.
          3. Robustness to mutation is a well-defined phenomenon whetre a search for general priciples that unify observations on different of organization is easier.
    • Why study robustness?
      • There are at least three important reasons why it is important to study robustness:
        1. To answer the question about complexity and fragility (i.e. why for living beings complexity does not implies fragility).
        2. To understand the role of neutral mutations (the ones that does not affect fitness) in genetic variation, required by evolution by natural selection (the more neutral mutations an organism allows, the greater its is mutational robustness).
        3. Is robustness in the living fundamentally similar and different robustness in engineered systems?
    • How to study robustness? There two different sources of information concerning robustness in living beings:
      1. Experimental evidence.
      2. Quantitative modeling.
      • Experimental evidence comes in two forms. First, one can perturb a part of an organism, or a capability through mutations. The less the feature change in the face of perturbation, the more robust it is. The second type of evidence relies on naturally ocurring perturbations, mutation that ocurred in evolutionary history (this aproach, based on comparisons between closely related species that have the same trait or capability, and examine whether they achieve it by different means, is more tentative than the results of systematic perturbations).
      • Since experimental evidence is not easy to produce, quantitative (analytical and computational) modeling can provide accurate predictions about a system's robustness.
    • An emphasis on Mechanism One can analyze biological systems from two different points of views:
      1. The first one emphasizes mechanistic understanding (exemplified by biochemistry and molecular biology).
      2. The second approach is represented by population genetics (statistical effects on genes on fitness rather that the roles of genes in a molecular machinary -evolutionary explanations built on statistical understading of gene effects may be difficult to understand-).
      • The second approach can be useful to tackle system whose inner working are understood to some extent.
    • Principles of robustnes
      1. Most problems the living have solved have an astronomical number of equivalent solutions, which can be thought in a vast neutral space. Robust sytem are then system associated with a large associated neutral space of equivalent solutions to a given problem (they are easiest to discover in evolution, because they represent a large proportion of all possible solution). Their robustness results from the structure of the neutral space itself, and maybe independent of the particular circumstances under which an organism or the system evolved, such as population size or mutation rates.
      2. Natural selection can further increase robustness by incremental evolution of a system within a neutral space. Neutral spaces are not homogeneous. This means that neutral spaces often have regions characterized by grater robustness, where mutations are less likely to change a system's structure or function, and regions or lesser robustness.
      3. Either mutations or nongenetic change can drive incremental evolution of mutational robustness. The first mechanism is restrictive, since mutations are rare in most organisms. Mutational robustness can emerge as a by-product of selection for robustness to nongenetic change (this mechanism is obviously less restrictive). Both of this explanations rely only on individuaƱ-bases selection, and not on group, lineage, or species selection.
      4. Robustness and neutral mutations are key to evolutionary innovation. Neutral mutations may be the source of future detriment or benefit, and also the source of of evolutionary innovation.
      5. Redundancy of a system's parts is a minor cause of robustness to mutation. More important is distributed robustness. In distributed robustness, interactions of multiple systems parts, each wuth a different role, ca compensate for the effects of mutations. The word redundancy is used by Wagner for two or more system parts that perform the same or similar tasks. Perhaps the best example is gene redundancy. Gene redundancy occurs if one gene has several copies in a genome.Such redundancy can render an organism robust to mutations in one of these copies.
      6. Fragility in a biological system, the opposite of robustness, can have several evolutionary causes. Fragility means that a system varies greatly in either structure or function in response to mutations. There at least three possible causes:
        1. The biological problem to which the system is a solution has only a few alternative solutions and thus a small associated neutral space.
        2. Variation in the system may be advantageous to the organism (e.g. antibody diversity).
        3. Trade-offs with other aspects of the system's function preclude maximal robustness (e.g enzymes require flexibility, which is not possible in that tertiary structure is maximally robust to mutations).
      7. Many natural systems below and beyond living organism show great robustness to changes in their parts. Such robustness can also increase over time, but the cause is usually self-organization instead or natural selection. One common example concerns robustness of ecological communities to species invasions (ecological communities are not living beings, even if they are constituted by living beings).
      8. Many of the mechanistic principles that underlie robustness in living beings can also be observed in man-made, engineered systems. Some examples come from telecommunications and electrical engineering.

Some comments concerning the meeting with David Miguez

  • I have today a meeting with David Miguez. He will explain to me the pathway that he follows in order to develop a mathematical model (of the AKT-rictor-AKT system).
  • I was working in the morning with David Miguez. He explained to me the way he is working with his systems. Well, I think that it would be better if I included here some comments on his project:
    • First he explained to me why he is working with the AKT pathway (Media:Akt-PKB.pdf). This biochemical network is linked to several kinds of cancer. The AKT protein is involved in cellular survival pathways, by inhibiting apoptotic processes; AKT is also able to induce protein synthesis pathways, and is therefore a key signaling protein in the cellular pathways that lead to skeletal muscle hypertrophy, and general tissue growth. Since it can block apoptosis, and thereby promote cell survival, AKT has been implicated as a major factor in many types of cancer (including tumors of the breast, ovary, colon, skin and prostate; all this kind of cancers are what is called 'cancers with a specific celular signature' and they are extremely adict to AKT, and without this protein they can not survive). Some complements:
      • This pathway is important because allows anticancer strategies based on inhibitory drugs (is more easy to inhibit that to activate a process), i.e. a drug acting on the concerned organism inhibiting the activity of AKT and as a consequence stop proliferation.
      • David isolated a part of the AKT signaling pathway, to reduce complexity and to focus his attention in a particular phenomenon (it seems that this is the common procedure followed by the people working in this domain of research).
      • Moreover, from the original AKT signaling pathway David took only the fast dynamics part, and considered that the other part is just in steady state (he is following then an approach based on two scale of time).
      • For the cells involved in the experiment (they are cancer cells of a very specific kind), if the transcription factor FOXO is in the nucleus the proliferation is stopped (it means that the protein PTEN is in the cytoplasm and as a consequence the AKT signalig pathway is inhibited). PTEN protein acts as a phosphatase to dephosphorylate phosphatidylinositol (3,4,5)-trisphosphate (PtdIns (3,4,5)P3). The product of this enzymatic reaction is PtdIns(4,5)P2. This dephosphorylation is the mechanism with inhibites the AKT signaling pathway). By the way, when the FOXO transcription factor is in the cytoplasm and not not in the nucleus the AKT signaling pathway (in the cancer cells) is activated, and there is not PTEN presented.
    • Concerning the mathematical model:
      • The experiment is based on the action of an inhibitory drug acting on the cancer cells.
      • Some cancer cells are modified in order to have the FOXO gen accompanied with the Green Flourescent Protein gene; the cell is then stimulated with the drug. The drug acts and it can be seen just watching the FOXO protein in the nucleus of the cell (obtaining pictures of the cells), and the cells are thus free from cancer, i.e. they are not proliferating (PTEN is produced to block the AKT signaling pathway). Later the cells are cleaned in order to eliminate the concerned drug, and with the time the FOXO is not found in the nucleus but in the cytoplasm, i.e. the cells are again in the cancerous state.
      • David explained me that he gives again the drug to the cells (th eones which were cleaned) and that now the cells act in a different way, i.e. the drug does not inhibits the signaling pathway in the same way, the cells have now memory and they need more drug to obtain the same effect detected previously (there is present a 'hysteresis' non linearity).
      • David visualized and verified the hysteresis behavior giving different levels of the drug to different sets of cells (he had a control group using a different cancer cells). The behavior is then 'measured' obtaining statistics counting cells by hand from pictures.
      • David explained to me that he expose his ideas to people coming from Merck.
      • We discussed something about the oscillatory behavior that he obtained from his model, and he insisted that the Euler integration technique does not affect the results. I must check this item, because if the oscillatory behavior is really present the bistability phenomenon could be argued in mathematica nonlinear terms.
    • Reducing the complexity of the model:
      • David want to model the memory phenomenon (hysteresis) using something like a double negative feedback loop (mainly involving delays!).
    • On the privacy of the results:
      • David does not want to give the information to other people, because he want to publish his results in a short time.
    • On the importance of the results:
      • Since the bistable behavior had been detected in experimental terms, it could open the door to some drug delivery strategy (by the way, the cells concerned by the study takes just one hour to be under the effect of the drug) strategy concerning the applied drug.

To contact Walter Fontana

Tomorrow I will prepare a summary concerning the activities of Walter Fontana, who is one of the most important experts on robustness and evolution. I want to contact him in order to obtain some insights on the subject. I must recover some papers to have a general idea on the subject, and redact some smart questions (robustness as a by-product of organization).

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