References of "Banda, Peter 50009290"
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See detailFeedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR
Blount, Drew; Banda, Peter UL; Teuscher, Christof et al

in Artificial Life (2017), 23(3), 295-317

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel ... [more ▼]

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system. [less ▲]

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See detailCOEL: A Cloud-based Reaction Network Simulator
Banda, Peter UL; Teuscher, Christof

in Frontiers in Robotics and AI (2016), 3(13),

Chemical Reaction Networks (CRNs) are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework, which does not require a ... [more ▼]

Chemical Reaction Networks (CRNs) are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework, which does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL’s key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm-based optimization toolbox for rate constants. COEL is an open-source project hosted on GitHub (http://dx.doi.org/10.5281/zenodo.46544), which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at http://coel-sim.org. The framework is ideally suited for a collaborative use in both research and education. [less ▲]

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See detailConfiguration Symmetry and Performance Upper Bound of One-Dimensional Cellular Automata for the Leader Election Problem
Banda, Peter UL; Caugmann, John IV.; Pospichal, Jiri

in Journal of Cellular Automata (2015), 10(1-2), 1-21

Leader election plays a crucial role in numerous distributed protocols and biological societies. Yet, there is a fundamental gap in understanding its simplest variant employing components that are fully ... [more ▼]

Leader election plays a crucial role in numerous distributed protocols and biological societies. Yet, there is a fundamental gap in understanding its simplest variant employing components that are fully uniform, deterministic, and anonymous, such as in one-dimensional cellular automata (CA). In our previous work we found several binary CAs that elect a leader by using spatial-temporal patterns, domains and particles, which carry and exchange information across distance. The best strategy reached performance of 94 -99% with a modulo 6 limitation for the number of cells. As opposed to state-of-the-art distributed algorithms, a CA consists just of binary components without any extra memory or communication capabilities, and therefore operates with minimal resources possible. In this paper we identify fundamental limitations of leader election for one-dimensional CAs and formulate an upper bound on performance. We show that configurations that are symmetric or loosely-coupled are insolvable. The proportion of configurations that are insolvable decreases dramatically with the system size. Our findings could help to design more effective distributed protocols and also to model biological processes such as morphogenesis of cell differentiation, where leader election breaks symmetry in a newly formed organism. [less ▲]

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See detailNovel Methods for Learning and Adaptation in Chemical Reaction Networks
Banda, Peter UL

Doctoral thesis (2015)

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be re-programmed or trained once fabricated. The implementation of adaptive ... [more ▼]

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be re-programmed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. Currently, biochemistry lacks a systems vision on how the user-level programming interface and abstraction with a subsequent translation to chemistry should look like. By developing adaptation in chemistry, we could replace multiple hard-wired systems with a single programmable template that can be (re)trained to match a desired input-output profile benefiting smart drug delivery, pattern recognition, and chemical computing. I aimed to address these challenges by proposing several approaches to learning and adaptation in Chemical Reaction Networks (CRNs), a type of simulated chemistry, where species are unstructured, i.e., they are identified by symbols rather than molecular structure, and their dynamics or concentration evolution are driven by reactions and reaction rates that follow mass-action and Michaelis-Menten kinetics. Several CRN and experimental DNA-based models of neural networks exist. However, these models successfully implement only the forward-pass, i.e., the input-weight integration part of a perceptron model. Learning is delegated to a non-chemical system that computes the weights before converting them to molecular concentrations. Autonomous learning, i.e., learning implemented fully inside chemistry has been absent from both theoretical and experimental research. The research in this thesis offers the first constructive evidence that learning in CRNs is, in fact, possible. I have introduced the original concept of a chemical binary perceptron that can learn all 14 linearly-separable logic functions and is robust to the perturbation of rate constants. That shows learning is universal and substrate-free. To simplify the model I later proposed and applied the ``asymmetric" chemical arithmetic providing a compact solution for representing negative numbers in chemistry. To tackle more difficult tasks and to serve more complicated biochemical applications, I introduced several key modular building blocks, each addressing certain aspects of chemical information processing and learning. These parts organically combined into gradually more complex systems. First, instead of simple static Boolean functions, I tackled analog time-series learning and signal processing by modeling an analog chemical perceptron. To store past input concentrations as a sliding window I implemented a chemical delay line, which feeds the values to the underlying chemical perceptron. That allows the system to learn, e.g., the linear moving-average and to some degree predict a highly nonlinear NARMA benchmark series. Another important contribution to the area of chemical learning, which I have helped to shape, is the composability of perceptrons into larger multi-compartment networks. Each compartment hosts a single chemical perceptron and compartments communicate with each other through a channel-mediated exchange of molecular species. Besides the feedforward pass, I implemented the chemical error backpropagation analogous to that of feedforward neural networks. Also, after applying mass-action kinetics for the catalytic reactions, I succeeded to systematically analyze the ODEs of my models and derive the closed exact and approximative formulas for both the input-weight integration and the weight update with a learning rate annealing. I proved mathematically that the formulas of certain chemical perceptrons equal the formal linear and sigmoid neurons, essentially bridging neural networks and adaptive CRNs. For all my models the basic methodology was to first design species and reactions, and then set the rate constants either "empirically" by hand, automatically by a standard genetic algorithm (GA), or analytically if possible. I performed all simulations in my COEL framework, which is the first cloud-based chemistry modeling tool, accessible at http://coel-sim.org. I minimized the amount of required molecular species and reactions to make wet chemical implementation possible. I applied an automatized mapping technique, Soloveichik's CRN-to-DNA-strand-displacement transformation, to the chemical linear perceptron and the manual signalling delay line and obtained their full DNA-strand specified implementations. As an alternative DNA-based substrate, I mapped these two models also to deoxyribozyme-mediated cleavage reactions reducing the size of the displacement variant to a third. Both DNA-based incarnations could directly serve as blue-prints for wet biochemicals. Besides an actual synthesis of my models and conducting an experiment in a biochemical laboratory, the most promising future work is to employ so-called reservoir computing (RC), which is a novel machine learning method based on recurrent neural networks. The RC approach is relevant because for time-series prediction it is clearly superior to classical recurrent networks. It can also be implemented in various ways, such as electrical circuits, physical systems, such as a colony of Escherichia Coli, and water. RC's loose structural assumptions therefore suggest that it could be expressed in a chemical form as well. This could further enhance the expressivity and capabilities of chemically-embedded learning. My chemical learning systems may have applications in the area of medical diagnosis and smart medication, e.g., concentration signal processing and monitoring, and the detection of harmful species, such as chemicals produced by cancer cells in a host (cancer miRNAs) or the detection of a severe event, defined as a linear or nonlinear temporal concentration pattern. My approach could replace “hard-coded” solutions and would allow to specify, train, and reuse chemical systems without redesigning them. With time-series integration, biochemical computers could keep a record of changing biological systems and act as diagnostic aids and tools in preventative and highly personalized medicine. [less ▲]

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See detailDelay Line as a Chemical Reaction Network
Moles, Josh; Banda, Peter UL; Teuscher, Christof

in Parallel Processing Letters (2015), 25(1), 1540002

Chemistry as an unconventional computing medium presently lacks a systematic approach to gather, store, and sort data over time. To build more complicated systems in chemistries, the ability to look at ... [more ▼]

Chemistry as an unconventional computing medium presently lacks a systematic approach to gather, store, and sort data over time. To build more complicated systems in chemistries, the ability to look at data in the past would be a valuable tool to perform complex calculations. In this paper we present the first implementation of a chemical delay line providing information storage in a chemistry that can reliably capture information over an extended period of time. The delay line is capable of parallel operations in a single instruction, multiple data (SIMD) fashion. Using Michaelis-Menten kinetics, we describe the chemical delay line implementation featuring an enzyme acting as a means to reduce copy errors. We also discuss how information is randomly accessible from any element on the delay line. Our work shows how the chemical delay line retains and provides a value from a previous cycle. The system's modularity allows for integration with existing chemical systems. We exemplify the delay line capabilities by integration with a threshold asymmetric signal perceptron to demonstrate how it learns all 14 linearly separable binary functions over a size two sliding window. The delay line has applications in biomedical diagnosis and treatment, such as smart drug delivery. [less ▲]

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See detailAnonymous Leader Election in One- and Two-Dimensional Cellular Automata
Banda, Peter UL

Doctoral thesis (2014)

Leader election plays a crucial role in numerous distributed protocols, multi-agent systems and biological societies. Yet there is a fundamental gap in understanding its simplest variant, such as, in ... [more ▼]

Leader election plays a crucial role in numerous distributed protocols, multi-agent systems and biological societies. Yet there is a fundamental gap in understanding its simplest variant, such as, in cellular automata, employing components that are fully uniform, deterministic, and anonymous. In this thesis, we investigate various one- and two-dimensional binary state cellular automata that elect a leader by transforming a random initial configuration to a state where exactly one arbitrary cell is active (leader). A cellular automaton (CA) is a distributed system with a spatial topology where each processor (cell) is locally connected to its neighbors. A transition rule is represented by a look-up table, which is uniform, i.e., shared among all cells. We show that leader election is possible even in the minimal, anonymous, and uniform architecture of a binary CA. Despite being one of the structurally simplest distributed systems, a CA can exhibit various types of behavior, including complex dynamics and self-organization. Our methodology leverages evolution of CAs by employing genetic algorithms, where chromosomes encoding candidate look-up tables undergo selection, cross-over and mutation. Even though CA's transition rules are just local and uniform, the leader election task is global and requires coordination of all cells. The findings show that the emergent dynamics of the best binary CAs are characterized by sophisticated coordination and global computation of cells, a product of spatio-temporal structures or events, namely regular domains, particles and particle interactions, known from the theory of computational mechanics. This collision-based computing enables CA to carry and exchange information over distances, eventually eliminating all but one candidate for leader. In two dimensions, slowly-contracting regions connected by lines of active cells propagate throughout the lattice and sweep any remaining active cells, before shrinking to a single cell (leader). The best strategies for both instances show a remarkably high performance rate of 0.99. In one-dimensional case the number of cells N is often modulo-restricted, such as in the best-performing CA called the strategy of mirror particles, where N is restricted to 5 mod 6. We also analyze the dynamics of two-dimensional CAs by stability measures: the Derrida measure, and the damage spreading with a discrete version of Lyapunov stability. In general, the more complex the dynamics, the better-performing the CA. Furthermore, we identify fundamental limitations of leader election for one- and two-dimensional CAs. More precisely, we show that configurations that are symmetric or loosely-coupled are principally unsolvable. The proportion of these configurations, however, decreases dramatically with the system size. We enumerate such unsolvable configurations using linear algebra and group theory and formulate a universal upper bound on performance for the anonymous leader election problem in CA. Our results pave the way to new distributed algorithms that are more robust and efficient than state-of-the-art systems. Our cellular automata consist of only binary components, without any extra memory or communication capabilities, and therefore use minimal resources possible. Our findings are also relevant for better understanding leader election in nature, in order to model biological processes such as morphogenesis of cell differentiation. [less ▲]

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See detailCOEL: A Web-based Chemistry Simulation Framework
Banda, Peter UL; Blount, Drew; Teuscher, Christof

in Stepney, Susan; Andrews, Paul (Eds.) CoSMoS 2014: Proceedings of the 7th Workshop on Complex Systems Modelling and Simulation (2014)

The chemical reaction network (CRN) is a widely used formalism to describe macroscopic behavior of chemical systems. Available tools for CRN modelling and simulation require local access, installation ... [more ▼]

The chemical reaction network (CRN) is a widely used formalism to describe macroscopic behavior of chemical systems. Available tools for CRN modelling and simulation require local access, installation, and often involve local file storage, which is susceptible to loss, lacks searchable structure, and does not support concurrency. Furthermore, simulations are often single-threaded, and user interfaces are non-trivial to use. Therefore there are significant hurdles to conducting efficient and collaborative chemical research. In this paper, we introduce a new enterprise chemistry simulation framework, COEL, which addresses these issues. COEL is the first web-based framework of its kind. A visually pleasing and intuitive user interface, simulations that run on a large computational grid, reliable database storage, and transactional services make COEL ideal for collaborative research and education. COEL's most prominent features include ODE-based simulations of chemical reaction networks and multicompartment reaction networks, with rich options for user interactions with those networks. COEL provides DNA-strand displacement transformations and visualization (and is to our knowledge the first CRN framework to do so), GA optimization of rate constants, expression validation, an application-wide plotting engine, and SBML/Octave/Matlab export. We also present an overview of the underlying software and technologies employed and describe the main architectural decisions driving our development. COEL is available at this http URL for selected research teams only. We plan to provide a part of COEL's functionality to the general public in the near future. [less ▲]

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See detailLearning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System
Banda, Peter UL; Teuscher, Christof

in Ibarra, Oscar H.; Kari, Lila; Kopecki, Steffen (Eds.) Unconventional Computing and Natural Computing Conference (2014)

The current biochemical information processing systems behave in a pre-determined manner because all features are defined during the design phase. To make such unconventional computing systems reusable ... [more ▼]

The current biochemical information processing systems behave in a pre-determined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification based on external stimuli would be highly desirable. However, so far, it has been too challenging to implement these in wet chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports Michaelis-Menten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions and their simplicity allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt. [less ▲]

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See detailAn Analog Chemical Circuit with Parallel-Accessible Delay Line for Learning Temporal Tasks
Banda, Peter UL; Teuscher, Christof

in Sayama, Hiroki; Rieffel, John; Risi, Sebastian (Eds.) et al Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (2014)

Current synthetic chemical systems lack the ability to self-modify and learn to solve desired tasks. In this paper we introduce a new parallel model of a chemical delay line, which stores past ... [more ▼]

Current synthetic chemical systems lack the ability to self-modify and learn to solve desired tasks. In this paper we introduce a new parallel model of a chemical delay line, which stores past concentrations over time with minimal latency. To enable temporal processing, we integrate the delay line with our previously proposed analog chemical perceptron. We show that we can successfully train our new memory-enabled chemical learner on four non-trivial temporal tasks: the linear moving weighted average, the moving maximum, and two variants of the Nonlinear AutoRegressive Moving Average (NARMA). Our implementation is based on chemical reaction networks and follows mass-action and Michaelis-Menten kinetics. We show that despite a simple design and limited resources, a single chemical perceptron extended with memory of variable size achieves 93-99% accuracy on the above tasks. Our results present an important step toward actual biochemical systems that can learn and adapt. Such systems have applications in biomedical diagnosis and smart drug delivery. [less ▲]

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See detailTraining an asymmetric signal perceptron through reinforcement in an artificial chemistry
Banda, Peter UL; Teuscher, Christof; Stefanovic, Darko

in Journal of The Royal Society Interface (2014), 11(93),

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive ... [more ▼]

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis–Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible. [less ▲]

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See detailOnline Learning in a Chemical Perceptron
Banda, Peter UL; Teuscher, Christof; Lakin, Matthew R.

in Artificial life (2013), 19(2), 195-219

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output ... [more ▼]

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and threshold. Both chemical perceptrons can successfully identify all 14 linearly separable two-input logic functions and maintain high robustness against rate-constant perturbations. We suggest that DNA strand displacement could, in principle, provide an implementation substrate for our model, allowing the chemical perceptron to perform reusable, programmable, and adaptable wet biochemical computing. [less ▲]

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See detailComplex Dynamics in Random DNA Strand Circuits
Banda, Peter UL; Teuscher, Christof

Poster (2013)

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See detailCellular Automata Evolution Of Leader Election
Banda, Peter UL

in Kampis, George; Karsai, István; Szathmáry, Eörs (Eds.) Advances in Artificial Life. Darwin Meets von Neumann (2011)

The leader election problem is a crucial problem in the theory of distributed algorithms, multi-agent systems as well as in sociobiology. In this paper we investigate one-dimensional binary state cellular ... [more ▼]

The leader election problem is a crucial problem in the theory of distributed algorithms, multi-agent systems as well as in sociobiology. In this paper we investigate one-dimensional binary state cellular automata with an intention to track self-organizational mechanisms that finally enable a global leader to be elected. Since our model is anonymous and uniform we also have to deal with a problem of symmetry that in great majority of cases is broken by inhomogeneity of arbitrary initial configurations. Our approach to the problem is based on the evolution of cellular automata by genetic algorithms and the methodology of computational mechanics. The presented new solution of the leader election reaches remarkably high performance of 94 − 99%. The analysis shows a sophisticated collective computation demonstrated by so called particles and their interactions. Due to the simplicity of our model, presented approach is general and universal enough to be applicable even at the level of primitive biological or artificial societies. [less ▲]

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