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References 1 Johnson, S. Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Scribner, New York, 2004. 2 (https://blog.benchsci.com/startups-using- artificial-intelligence-in-drug-discovery; https://blog.benchsci.com/pharma-companies- using-artificial-intelligence-in-drug-discovery). 3Tenner, E. The Efficiency Paradox: What the big data can’t do. Knopf, New York, 2018. 4 Lushington, G. Combinatorial Chemistry & High Throughput Screening, Volume 21, Number 1, pp. 3-4 (March 2018). 5 Lushington, G. Combinatorial Chemistry & High Throughput Screening, Volume 21, Number 4, in press (2018). 6 Chen, H, Engkvist, O, Wang, Y, Olivecrona, M, Blaschke, T. Drug Discovery Today, in press (2018). 7 Gho, YS, Lee, C. Molecular BioSystems, Volume 13, Number 7, pp.1291-1296 (2017). 8 http://www.wired.co.uk/article/benevolent-ai- london-unicorn-pharma-startup. 9 Erlandsson, B-E, Akay, A, Dragomir, A. IEEE Pulse. https://pulse.embs.org/november- 2015/mining-social-media-big-data-for-health/ (2015). 10 Mullard, A. FDA approvals for the first 6 months of 2017. (2017): 519. 11 Mullin, E (2018). Stopping breast cancer with help from artificial intelligence. [online] MIT Technology Review. Available at: https://www.technologyreview.com/s/602721/sto pping-breast-cancer-with-help-from-ai/. 12 Ascr-discovery.science.doe.gov, 2018. 13 Gosak, M et al. Network science of biological systems at different scales: a review. Physics of life reviews (2017). 14 Szabo, C, Teo, YM, Chengleput, GK. Understanding complex systems: using interaction as a measure of emergence, Proceedings of the 2014 Winter Simulation Conference, December 07-10 (2014). 15 Szabo, C and Birdsey, L. Toward The Automated Detection Of Emergent Behavior. Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach, pp.228-261 (2018). 16Tanaka, H et al. Boolean modeling of mammalian cell cycle and cancer pathways. alife- robotics.co. jp (2017). 17Vidunas, R. Delegated causality of complex systems. arXiv preprint arXiv:1707.08905 (2017). 18 Deritei, D et al. Principles of dynamical modularity in biological regulatory networks. Scientific Reports volume 6, Article number: 21957 (2016). 19 Gosak, M et al. Network science of biological systems at different scales: a review. Physics of life reviews (2017). 20 Craig, J. Complex diseases: Research and applications. Nature Education 1, 184 (2008).


21 Suderman, R et al. Fundamental trade-offs between information flow in single cells and cellular populations. Proceedings of the National Academy of Sciences 114.22 (2017): 5755-5760. 22 Chamberlin, W. Networks, emergence, iteration and evolution. Emergence: Complexity and Organization. 2009 Dec 31 [last modified: 2016 Dec 4]. Edition 1. 23 Luo, J and Magee, CL (2011). Detecting evolving patterns of self organizing networks by flow hierarchy measurement. Complexity, 16(6), pp.53-61. 24 Pottie, GJ and Kaiser, WJ (2000). Wireless integrated network sensors. Communications of the ACM, 43(5), pp.51-58. 25Vigo, R. Representational information: A new general notion and measure of information. Inf. Sci. 2011, 181, 4847-4859. 26 Snášel, V et al (2017) Geometrical and topological approaches to Big Data, Future Generation Computer Systems Volume 67, 286-296. 27 Geiger, B, Spatz, JP and Bershadsky, AD (2009). Environmental sensing through focal adhesions. Nature reviews Molecular cell biology, 10(1), p.21. 28 Navlakha, S and Bar-Joseph, Z (2015). Distributed information processing in biological and computational systems. Communications of the ACM, 58(1), pp.94-102. 29 Sachs, K, Perez, O, Pe’er, D, Lauffenburger, DA and Nolan, GP (2005). Causal protein-signaling networks derived from multi parameter single- cell data. Science, 308(5721), pp.523-529. 30 Rowland, MA, Greenbaum, JM and Deeds, EJ (2017). Crosstalk and the evolvability of intracellular communication. Nature communications, 8, p.16009. 31 Shklarsh, A, Ariel, G, Schneidman, E and Ben- Jacob, E. Smart swarms of bacteria-inspired agents with performance adaptable interactions. PLoS Comput. Biol. 7, 9 (Sept. 2011), e1002177. 32 Bruns, W et al (1985). Suppression of intrinsic resistance to penicillins in Staphylococcus aureus by polidocanol, a dodecyl polyethyleneoxid ether. Antimicrobial agents and chemotherapy, 27(4), pp.632-639. 33 Sánchez-Serrano, I, Pfeifer, T and Chaguturu, R (2018). Disruptive Approaches to Accelerate Drug Discovery and Development. Part 1. Tools, Technologies and The Core Model. Drug Discovery World, Spring, 39-52.


Suggested Reading Sánchez-Serrano, I, Pfeifer, T and Chaguturu, R. Disruptive Approaches to Accelerate Drug Discovery and Development. Part 1. Tools, Technologies and The Core Model. Drug Discovery World, Spring, 39-52, 2018. Johnson, Steven. Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Scribner, New York, 2004. Tenner, Edward. The Efficiency Paradox: What the big data can’t do. Knopf, New York, 2018. Ridley, M. The Evolution of Everything: how new ideas emerge. Harper, New York, 2016. Chaguturu R, Ed. Collaborative Innovation in Drug Discovery: strategies for public and private partnerships. Wiley, New York, 2014. Gassmann, O and Schuhmacher, S. Leading Pharmaceutical Innovation: how to win the life science race. Wiley, New York, 2018. Gulfo, J. Innovation Breakdown: How the FDA and Wall Street Cripple Medical Advances, Post Hill Press, New York, 2017.


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