The sciences of complexity present some recurrent themes: the emergence of qualitatively new behaviors in dissipative systems out of equilibrium, the aparent tendency of complex system to lie at the border of phase transitions and bifurcation points, a historical dynamics which present punctuated equilibrium, a tentative of complementing Darwinian evolution with certain ideas of progress (understood as increase of computational power) etc. Such themes, indeed, belong to a long scientific and philosophical tradiction and, curiously, appear already in the work of Frederick Engels at the 70’s of the XIX century. So, the apparent novelity of the sciences of complexity seems to be not situated in its fundamental ideas, but in the use of mathematical and computational models for illustrate, test and develop such ideas. Since politicians as the candidate Al Gore recently declared that the sciences of complexity have influenced strongly their worldview, perhaps it could be interesting to know better the ideas and the ideology related to the notion of complex adaptive systems.

 

A precursor of the sciences of complexity in the XIX century
Osame Kinouchi

Source: arxiv.org

Data related to Science, from co-authorships to Scientists’ mobility are increasingly becoming available. We think that the readiness of these sort of data is a great opportunity for scientists interested in the social dynamics of science, especially in the context of computational social science.

 

A game-theory modeling approach to utility and strength of interactions dynamics in biomedical research social networks
J. Mario Siqueiros-García, Rodrigo García-Herrera, Enrique Hernández-Lemus and Sergio Alcalá-Corona
Complex Adaptive Systems Modeling 2017 5:5
DOI: 10.1186/s40294-017-0044-0

Source: casmodeling.springeropen.com

Much of contemporary systems biology owes its success to the abstraction of a network, the idea that diverse kinds of molecular, cellular, and organismal species and interactions can be modeled as relational nodes and edges in a graph of dependencies. Since the advent of high-throughput data acquisition technologies in fields such as genomics, metabolomics, and neuroscience, the automated inference and reconstruction of such interaction networks directly from large sets of activation data, commonly known as reverse-engineering, has become a routine procedure. Whereas early attempts at network reverse-engineering focused predominantly on producing maps of system architectures with minimal predictive modeling, reconstructions now play instrumental roles in answering questions about the statistics and dynamics of the underlying systems they represent. Many of these predictions have clinical relevance, suggesting novel paradigms for drug discovery and disease treatment. While other reviews focus predominantly on the details and effectiveness of individual network inference algorithms, here we examine the emerging field as a whole. We first summarize several key application areas in which inferred networks have made successful predictions. We then outline the two major classes of reverse-engineering methodologies, emphasizing that the type of prediction that one aims to make dictates the algorithms one should employ. We conclude by discussing whether recent breakthroughs justify the computational costs of large-scale reverse-engineering sufficiently to admit it as a mainstay in the quantitative analysis of living systems.

 

Reverse-engineering biological networks from large data sets
Joseph L. Natale, David Hofmann, Damian G. Hernández, Ilya Nemenman

Source: arxiv.org

In this paper, we have identified and analyzed the emergence, structure and dynamics of the paradigmatic research fronts that established the fundamentals of the biomedical knowledge on HIV/AIDS. A search of papers with the identifiers “HIV/AIDS”, “Human Immunodeficiency Virus”, “HIV-1” and “Acquired Immunodeficiency Syndrome” in the Web of Science (Thomson Reuters), was carried out. A citation network of those papers was constructed. Then, a sub-network of the papers with the highest number of inter-citations (with a minimal in-degree of 28) was selected to perform a combination of network clustering and text mining to identify the paradigmatic research fronts and analyze their dynamics. Thirteen research fronts were identified in this sub-network. The biggest and oldest front is related to the clinical knowledge on the disease in the patient. Nine of the fronts are related to the study of specific molecular structures and mechanisms and two of these fronts are related to the development of drugs. The rest of the fronts are related to the study of the disease at the cellular level. Interestingly, the emergence of these fronts occurred in successive “waves” over the time which suggest a transition in the paradigmatic focus. The emergence and evolution of the biomedical fronts in HIV/AIDS research is explained not just by the partition of the problem in elements and interactions leading to increasingly specialized communities, but also by changes in the technological context of this health problem and the dramatic changes in the epidemiological reality of HIV/AIDS that occurred between 1993 and 1995.

 

Fajardo-Ortiz D, Lopez-Cervantes M, Duran L, Dumontier M, Lara M, Ochoa H, et al. (2017) The emergence and evolution of the research fronts in HIV/AIDS research. PLoS ONE 12(5): e0178293. https://doi.org/10.1371/journal.pone.0178293

Source: journals.plos.org