Reverse-engineering biological networks from large data sets

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


Call for Late Breaking Abstracts, CCS’17

The flagship conference of the Complex Systems Society will go to Latin America for the first time in 2017. The Mexican complex systems community is enthusiast to welcome colleagues to one of our richest destinations: Cancun.

The conference will include presentations by Mario Molina (Environment, Nobel Prize in Chemistry), Raissa D’Souza (network science), Ranulfo Romo (neruoscience), Jaime Urrutia-Fucugauchi (geophysics), Antonio Lazcano (origins of life), Marta González (human mobility), Dirk Brockmann (epidemiology), Kristina Lerman (information sciences), Stefano Battiston (economics) John Quackenbush (computational biology), Giovanna Miritello (data science), César Hidalgo (collective learning), and many more.

There will be a discussion panel on the past, present, and future of complexity, 23 satellites, and more than 300 oral presentations. Join us for a rich exchange of the latest scientific advances.

We invite abstract contributions (500 words maximum) for poster presentations in the following tracks:
• Foundations of Complex Systems
• Information and Communication Technologies
• Language, Linguistics Cognition and Social Systems
• Economics and Finance
• Infrastructures, Planning and Environment
• Biological and (Bio)Medical Complexity
• Socio-Ecological Systems
• Complexity in Physics and Chemistry

Posters will be available during the whole week of the conference.


Upload your abstracts at


Important dates:

  • Late breaking abstract deadline August 18th
  • Notifications within two weeks of submission
  • Conference September 17-22


The emergence and evolution of the research fronts in HIV/AIDS research

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.


Warnings and Caveats in Brain Controllability

In this work we challenge the main conclusions of Gu et al work (Controllability of structural brain networks. Nature communications 6, 8414, doi:10.1038/ncomms9414, 2015) on brain controllability. Using the same methods and analyses on four datasets we find that the minimum set of nodes to control brain networks is always larger than one. We also find that the relationships between the average/modal controllability and weighted degrees also hold for randomized data and the there are not specific roles played by Resting State Networks in controlling the brain. In conclusion, we show that there is no evidence that topology plays specific and unique roles in the controllability of brain networks. Accordingly, Gu et al. interpretation of their results, in particular in terms of translational applications (e.g. using single node controllability properties to define target region(s) for neurostimulation) should be revisited. Though theoretically intriguing, our understanding of the relationship between controllability and structural brain network remains elusive.