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The Complex Systems Approach to Protocells

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Advances in Artificial Life and Evolutionary Computation (WIVACE 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 445))

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Abstract

Systems biology is mainly focussed upon the description of specific biological items, like for example specific organisms, or specific organs in a class of animals, or specific genetic-metabolic circuits. It therefore leaves open the issue of the search for general principles of biological organization, which apply to all living beings or to at least to broad classes. So the main challenge of complex systems biology is that of looking for general principles in biological systems, in the spirit of complex systems science which searches for similar features and behaviors in various kinds of systems.

I present here some strong arguments in favor of the soundness of this approach, by reviewing data concerning allometric scaling laws and models, focusing in particular on the claim that evolution tends to drive systems towards critical states.

Then I discuss protocells, in particular the very important phenomenon of synchronization between the rate of growth of the proto-genetic material and that of the lipid container, that is a necessary condition for a sustained growth of a population of protocells). Highly simplified, generic models show that such synchronization can be an emergent property under a very wide set of different hypotheses about protocell architectures and kinetic models. Moreover, I discuss the emergence of autocatalytic sets of collectively replicating molecules in a small protocell with a semipermeable membrane, arguing that local differences in the chemical composition of the environment can give rise to a heterogeneous population of protocells.

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Notes

  1. 1.

    (The term “Complex Systems Biology” was introduced a few years ago by Kaneko [19]. Although it not of widespread use, it seems particularly well suited to indicate an approach to biology which is well rooted in complex systems science.)

  2. 2.

    For example, ceteris paribus, highly connected networks are more disordered than poorly connected ones. This is however a property of the set of networks with those parameter values, and single network realizations can behave in a way different from the typical behavior of their class.

  3. 3.

    Of course some hypotheses need to be made; in this case, the key hypothesis is that the level of cellular noise is high in stem cells and decreases during differentiation. There are some indications in favour of this hypothesis, which can be subject to experimental testing.

  4. 4.

    This remark refers to the kind of protocells we are interested in, i.e. those that are built by self-organization and self-assembly starting from various types of molecules, like nucleic acids, polypetides, lipids, etc., avoiding however those that can be obtained only by living beings, like e.g. specialized enzymes. There are other types of entities that are also called protocells, like those that have been obtained by inserting a synthetic genome into a bacterial cell.

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Acknowledgments

I am indebted to my colleague Marco Villani, with whom I shared a 15 years long research experience in complex systems biology, to Stuart Kauffman, for some wonderful discussions, and to my former Ph.D. students (and now post-doc collaborators) Alessandro Filisetti, Alex Graudenzi, Chiara Damiani who made excellent work in exploring the properties of RBNs and of protocell models and in shaping the ideas described here.

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Authors and Affiliations

  1. Department of Physics, Informatics and Mathematics, Modena and Reggio Emilia University, Modena, Italy

    Roberto Serra

  2. ECLT (European Centre for Living Technology), Venice, Italy

    Roberto Serra

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  1. Roberto Serra
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Correspondence to Roberto Serra .

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Editors and Affiliations

  1. CNR-ICAR, Rende, Italy

    Clara Pizzuti

  2. CNR-ICAR, Rende, Italy

    Giandomenico Spezzano

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Serra, R. (2014). The Complex Systems Approach to Protocells. In: Pizzuti, C., Spezzano, G. (eds) Advances in Artificial Life and Evolutionary Computation. WIVACE 2014. Communications in Computer and Information Science, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-319-12745-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-12745-3_16

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