Research Title: Algorithms in nature
Research Description: Biological systems must constantly solve what we think of as computational problems: foraging slime molds design networks to optimize food transport, and delta-notch signaling performs distributed leader election when determining cell fate. As such, many parallels can be drawn between the goals and constraints of biological and computational systems, suggesting that we can learn about one from the other. In both cases, network-based information processing is often used to enable efficient, robust, and adaptive input-output responses in real-time. Challenges to these systems include: 1) operating in noisy or adversarial environments, 2) using distributed rules of computation with limited communication between nodes, and 3) devising low-cost solutions that conserve important metabolic or physical resources. Studying such “algorithms in nature” can lead to advances in both computer science (by developing new bio-inspired algorithms for a variety of communication- and resource-constrained network problems) and biology (by establishing new, testable hypotheses about systems-level information processing).
S. Navlakha, A.L. Barth, and Z. Bar-Joseph (2015). Decreasing-rate pruning optimizes the construction of efficient and robust distributed networks. PLoS Comput. Biol., 11(7): e1004347.
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S. Navlakha, X. He, C. Faloutsos, and Z. Bar-Joseph (2014). Topological properties of robust biological and computational networks. J. R. Soc. Interface, 11(96) 20140283.
S. Navlakha, J. Suhan, A.L. Barth, and Z. Bar-Joseph (2013). A high-throughput framework to detect synapses in electron microscopy images. Bioinformatics (Proc. 21st Intl. Conf. on Intelligent Systems for Molecular Biology and 12th European Conf. on Computational Biology, ISMB/ECCB), 29 (13): i9-i17.
S. Navlakha, A. Gitter, and Z. Bar-Joseph (2012). A network-based approach for predicting missing pathway interactions. PLoS Comput. Biol., 8, 8:e1002640.
S. Navlakha and C. Kingsford (2011). Network archaeology: uncovering ancient networks from present-day interactions. PLoS Comput. Biol., 7(4), e1001119.