A Biologically Inspired Model of Distributed Online Communication Supporting Efficient Search and Diffusion of Innovation

We inhabit a world that is not only “small” but supports efficient decentralized search – an individual using local information can establish a line of communication with another completely unknown individual. Here we augment a hierarchical social network model with communication between and within communities. We argue that organization into communities would decrease overall decentralized search times. We take inspiration from the biological immune system which organizes search for pathogens in a hybrid modular strategy. Our strategy has relevance in search for rare amounts of information in online social networks and could have implications for massively distributed search challenges. Our work also has implications for design of efficient online networks that could have an impact on networks of human collaboration, scientific collaboration and networks used in targeted manhunts. Real world systems, like online social networks, have high associated delays for long-distance links, since they are built on top of physical networks. Such systems have been shown to densify i.e. the average number of neighbours that an individual has increases with time. Hence such networks will have a communication cost due to space and the requirement of building and maintaining and increasing number of connections. We have incorporated such a non-spatial cost to communication in order to introduce the realism of individuals communicating within communities, which we call participation cost. We introduce the notion of a community size that increases with the size of the system, which is shown to reduce the time to search for information in networks. Our final strategy balances search times and participation costs and is shown to decrease time to find information in decentralized search in online social networks. Our strategy also balances strong-ties (within communities) and weak-ties over long distances (between communities that bring in diverse ideas) and may ultimately lead to more productive and innovative networks of human communication and enterprise. We hope that this work will lay the foundation for strategies aimed at producing global scale human interaction networks that are sustainable and lead to a more networked, diverse and prosperous society

Lymph node inspired computing: towards immune system inspired human-engineered complex systems

The immune system is a distributed decentralized system that functions without any centralized control. The immune system has millions of cells that function somewhat independently and can detect and respond to pathogens with considerable speed and efficiency. Lymph nodes are physical anatomical structures that allow the immune system to rapidly detect pathogens and mobilize cells to respond to it. Lymph nodes function as: 1) information processing centres, and 2) a distributed detection and response network. We introduce biologically inspired computing that uses lymph nodes as inspiration. We outline applications to diverse domains like mobile robots, distributed computing clusters, peer-to-peer networks and online social networks. We argue that lymph node inspired computing systems provide powerful metaphors for distributed computing and complement existing artificial immune systems. We view our work as a first step towards holistic simulations of the immune system that would capture all the complexities and the power of a complex adaptive system like the immune system. Ultimately this would lead to immune system inspired computing that captures all the complexities and power of the immune system in human-engineered complex systems

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

A Teaching Resource for Complex Systems, Machine Learning and Computational Biology

This work presents a collection of teaching materials related to complex systems, machine learning, computational biology and computational immunology

A Framework for Designing Compassionate and Ethical Artificial Intelligence and Artificial Consciousness

Intelligence and consciousness have fascinated humanity for a long time and we have long sought to replicate this in machines. In this work, we show some design principles for a compassionate and conscious artificial intelligence. We present a computational framework for engineering intelligence, empathy, and consciousness in machines. We hope that this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. Our hope is that this will also shift the discussion from fear of artificial intelligence towards designing machines that embed our cherished values. Consciousness, intelligence, and empathy would be worthy design goals that can be engineered in machines

A Computational Technique to Estimate Within-Host Productively Infected Cell Lifetimes in Emerging Viral Infections

Emerging viruses cause a lot of fatalities as they jump to humans from other species. Here we develop a novel technique to computationally estimate an important parameter of within-host viral infection: the lifespan of infected cells. Our approach is general and can be applied to a large class of viruses and leverages experimental data from replicon studies. Current techniques have difficulties reliably estimating infected cell lifetimes due to parameter identifiability and correlation of parameters. The infected cell lifetime is an important parameter that gives an estimate of how fast virus levels will decline. Our method would also help determine if some infected cells are short-lived or have longer lifespans with the consequence that longer lived cells could be reservoirs of infection. This would give a mechanistic understanding of why particular cell types are reservoirs of infection and may motivate therapy targeted towards these cell types. We apply our technique to West Nile virus (WNV), an emerging disease of public health relevance related to Zika virus. Our analysis suggests that the most abundant infectible cells are short-lived and could motivate therapy that targets these particular cells. Our approach is very general and can be combined with more traditional methods of using differential equation models to simulate viremia in hosts: the combination of these two techniques will likely yield results that may not be achievable using the models in isolation. This will be of great interest especially in modelling emerging diseases

Interactive analogical retrieval: practice, theory and technology

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.PhDCommittee Chair: Goel, Ashok; Committee Member: Kolodner, Janet; Committee Member: Maher, Mary Lou; Committee Member: Nersessian, Nancy; Committee Member: Yen, Jeannett

Exploring the intersection of biology and design for product innovations

Design, development, productization, and applications of advanced product concepts are pressing for higher multifunctionality, resilience, and maximization of available resources equitably to meet the growing and continuing demands of global customers. These demands have further accelerated during the recent COVID- 19 pandemic and are continuing to be a challenge. Engineering designs are one of the most effective ways to endow products with functions, resilience, and sustainability. Biology, through millions of years of evolution, has met these acute requirements under severe resource and environmental constraints. As the manufacturing of products is reaching the fundamental limits of raw materials, labor, and resource constraints in terms of availability, accessibility, and affordability, new approaches are a call to action to meet these challenges. Understanding the designs in biology is an attractive, novel, and desired frontier for learning and implementation to meet this call to action. This is the focus of the paper discussed through examples for convergence of fundamental engineering design concepts and the lessons learned and applied from biology

An Artificial Immune System Approach to Automated Program Verification: Towards a Theory of Undecidability in Biological Computing

We propose an immune system inspired Artificial Immune System (AIS) algorithm for the purposes of automated program verification. It is proposed to use this AIS algorithm for a specific automated program verification task: that of predicting shape of program invariants. It is shown that the algorithm correctly predicts program invariant shape for a variety of benchmarked programs. Program invariants encapsulate the computability of a particular program, e.g. whether it performs a particular function correctly and whether it terminates or not. This work also lays the foundation for applying concepts of theoretical incomputability and undecidability to biological systems like the immune system that perform robust computation to eliminate pathogens