Information Processing in Cells and Tissues

The microscopic information processing machinery of biological cells provides inspiration for the field of molecular computation, and for the use of synthetic DNA to store and process information and instructions. A single microlitre of solution can contain billions of distinct DNA sequences and consequently DNA computation offers huge potential for parallel processing. However, conventional data readout systems are complex, and the methods used are not well-suited for combination with mainstream computer circuits. Immobilisation of DNA machines on surfaces may allow integration of molecular devices with traditional electronics, facilitating data readout and enabling low-power massively parallel processing. Here we outline a general framework for hybrid bioelectronic systems and proceed to describe the results of our preliminary experiments on dynamic DNA structures immobilised on a surface, performed using QCM-D (quartz crystal microbalance with dissipation monitoring), which involves the use of acoustic waves to probe a molecular layer on a gold-coated quartz sensor.

As spatial locality leads to advantages of computation speed-up and sequence reuse in molecular computing, molecular walkers that exhibit localized reactions are of interest for implementing logic computations. We use molecular spiders, which are a type of molecular walkers, to implement logic circuits. We develop an extended multi-spider model with a dynamic environment where signal transmission is triggered locally, and use this model to implement three basic gates (AND, OR, NOT) and a mechanism to cascade the gates. We use a kinetic Monte Carlo algorithm to simulate gate computations, and we analyze circuit complexity: our design scales linearly with formula size and has a logarithmic time complexity.

Formally, cellular information processing requires mathematical forms of representation of parts of the whole. Several types of mereological operands, co-operands, and operators are necessary to embed the symbolic meanings. The common source of propositions is the atomic numbers, taken as both (measurable) physical objects and mathematical objects. The atomic numbers serve as source of the novel notation for organic mathematics. The historical scientific basis for the notation is described in terms of the electrical particles (abstractly defined as cardinal and ordinal numbers). The regular order of atomic numbers as diagrams define the radix attributes of the perplex number system. A triad of mathematical constructs built from the perplex numerals include quantum mechanics, the table of elements, and molecular biology. The radix of the hybrid logic of biological information processing includes forms of both copulative and predicative propositions on the attractive and repulsive attributes of electrical particles.

We extend and adapt the Artificial Ecosystem Algorithm (AEA), by applying it to the dynamic redistribution of bicycles in London’s Santander Cycle scheme. Just as an ecosystem comprises many separate components that adapt to form a single synergistic whole, the AEA uses a bottom up approach to build a solution. A problem is decomposed into relative subcomponents, they then evolve and cooperate to form solution building blocks, which connect to form a single optimal solution. In this way the AEA is designed to take advantage of highly distributed computer architectures and adapt to changing problems. Three variants of the AEA are described and applied to the Santander Cycle scheme: AEA, AEA Random and AEA Nearest Neighbour. The algorithms have been tested using historical data and empirical results prove their potential effectiveness.

Ensembles are groups of classifiers which cooperate in order to reach a decision. Conventionally, the members of an ensemble are trained sequentially, and typically independently, and are not brought together until the final stages of ensemble generation. In this paper, we discuss the potential benefits of training classifiers together, so that they learn to interact at an early stage of their development. As a potential mechanism for achieving this, we consider the biological concept of mutualism, whereby cooperation emerges over the course of biological evolution. We also discuss potential mechanisms for implementing this approach within an evolutionary algorithm context.

User defined tactics for teams of unmanned systems can be brittle and difficult to define. The state and action space grows with each new system added to the team which increases the difficultly in designing robust behaviors. In this paper we present a method for using Multi-agent HyperNEAT to develop tactics for a team of simulated unmanned systems that is robust to novel situations, and scales with the number of team members. We focus on the tactics of a search area coverage task, where the need for team work, and robust asset management are critical to success.

Elucidating the role that microRNAs (miRNAs) and signaling transduction play in the directed differentiation of human embryonic stem cells (hESCs) into glucose-responsive, insulin-producing endocrine cells is critical to our understanding of systems biology and the development of cell-based therapeutics. To accomplish this, a biochemical understanding the underpinnings of hESC differentiation bias – the propensity of hESCs to differentiate into cells of a specific lineage – must be described in molecular detail. An inherent aspect of hESC culture is stress, and we hypothesize that stress is largely responsible for differentiation bias. Our results indicate that manipulating stress increases apoptosis and disrupts differentiation. Cells subjected to stress fail to become endocrine precursor cells and retain many characteristics of pluripotent cells. Many stresses induce massive apoptosis and result in a loss of up to 80 % of the cells. A consequence of the reduction in cell density is elevated stress signaling, dramatic changes in cell proliferation, maintenance of pluripotency markers, and a complete absence of transcription factors associated with pancreatic endocrine cell production. Coincident with changes in stress, we observed dramatic changes in correlated miRNAexpression, suggesting that cell stress may modulate miRNA transcription and ultimately hESC differentiation.

Motifs are small gene interaction networks that frequently occur within larger genetic regulatory networks (GRNs). However, it is unclear what evolutionary and developmental advantages motifs provide that have led to this enrichment. This study seeks to understand how motifs within developmental GRNs influence the complexity of multicellular patterns that emerge from the dynamics of the regulatory networks. A computational study was performed by creating Boolean intracellular networks with varying inserted motifs within a simulated epithelial field of embryonic cells. Each cell contains the same network and communicates with adjacent cells using contact-mediated signaling. Comparison of random networks to those with motifs demonstrated that: (1) Bistable switches that encode mutual inhibition simplify both the pattern and network dynamics. (2) All other motifs with feedback loops increase information complexity of the multicellular patterns while simplifying the network dynamics. (3) Negative feedback loops affect the dynamics complexity more significantly than positive feedback loops. (4) Feed forward motifs without feedback have little effect on the complexity of patterns formed.

The behavioural diversity of chaotic oscillator can be controlled into periodic dynamics and used to model locomotion using central pattern generators. This paper shows how controlled chaotic oscillators may improve the adaptation of the robot locomotion behaviour to terrain uncertainties when compared to nonlinear harmonic oscillators. This is quantitatively assesses by the stability, changes of direction and steadiness of the robotic movements. Our results show that the controlled Wu oscillator promotes the emergence of adaptive locomotion when deterministic sensory feedback is used. They also suggest that the chaotic nature of chaos controlled oscillators increases the expressiveness of pattern generators to explore new locomotion gaits.

Research into the biology of microRNAs (miRNA) continues to expand rapidly. As a result, their abundance and importance in cellular regulation and disease states, also continues to grow and they are considered master regulators. Despite this greater understanding, key mechanisms regulating global miRNA transcription have remained elusive. This paper addresses a critical issue regarding regulation of miRNA expression. Here, we describe and biochemically characterize a universal regulatory complex that directly binds miRNA genetic loci and regulates transcription of miRNA genes. In addition, our preliminary results provide evidence that miRNA-induced Ago2 binding can result in positive post-transcriptional regulation of many important primary miRNAs. Using chromatin immuno-precipitation (ChIP) assays, our results demonstrate that the human miRNA binding protein Argonaute 2 (Ago2) associates with endogenous promoter DNA from each of the important human miRNA genes investigated to date. Additionally, our data shows a robust, direct interaction between mature miR-21 directed Ago2 and a miR-21 promoter DNA sequence.

The artificial epigenetic network (AEN) is a computational model which is able to topologically modify its structure according to environmental stimulus. This approach is inspired by the functionality of epigenetics in nature, specifically, processes such as chromatin modifications which are able to dynamically modify the topology of gene regulatory networks. The AEN has previously been shown to perform well when applied to tasks which require a range of dynamical behaviors to be solved optimally. In addition, it has been shown that pruning of the AEN to remove non-functional elements can result in highly compact solutions to complex dynamical tasks. In this work, a method has been developed which provides the AEN with the ability to self prune throughout the optimisation process, whilst maintaining functionality. To test this hypothesis, the AEN is applied to a range of dynamical tasks and the most optimal solutions are analysed in terms of function and structure.

To ensure homeostasis as well as proliferation, cellular systems usually adapt to changes in environmental and intracellular conditions at the level of the flux phenotype. The latter is characterized by the biochemical reaction rates in the underlying metabolic network and depends on the concentration of individual metabolites. As a result, concentrations of metabolites with large effect on the flux phenotype are expected to be tightly controlled. We examine the sensitivity of the flux phenotype upon changes in metabolite concentrations via the shadow prices in a flux balance analysis using multiple contending objectives of the central carbon metabolism of E. coli. The shadow prices of the metabolites are determined individually for sampled solutions of the Pareto front and objective functions. Utilization of 13C flux measurements for different environmental conditions enables us to draw conclusions about the relation of shadow prices and physiological cellular states. We find that E. coli operates in the vicinity of an area of the Pareto front which exhibits low variation of shadow prices compared to the whole front, which enables to react to changing conditions without large changes in the reguatory machinery. In addition, the determined shadow prices under different conditions suggest an increased requirement for regulation of concentrations of metabolites from the pentose phosphate pathway under carbon-limiting conditions compared to aerobe conditions. Our study extends the applicability of concepts from classical constraint-based modelling in a multi-objective settings to obtain predictions about regulation of metabolite levels based solely on stoichiometry.

High-throughput biological data analysis has received a large amount of interest in the last decade due to pioneering technologies that are able to automatically generate large-scale datasets by performing millions of analytical tests on a daily basis. Here we present a new network-based approach to analyze a high-throughput phenomic dataset that was collected on maize inbreds and hybrids by an automated phenotyping facility. Our dataset consists of 1600 biological samples from 600 different genotypes (200 inbred and 400 hybrid lines). On each sample, 141 phenotypic traits were observed for 33 days. We apply a graph-theoretic approach to address two important problems: (i) to discover meaningful patterns in the dataset and (ii) to predict hybrid performance in terms of biomass based on automatically collected phenotypic traits. We propose a modelling framework in which the prediction problem becomes transformed into finding the shortest path in a correlation-based network. Preliminary results show small but encouraging correlations between predicted and observed biomass. Extensions of the algorithm and applications of the modelling framework to other types of biological data are discussed.

This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP), acquired over a 20 h period. Subsequent analysis, comprising feature extraction, demonstrated the ability of the technique to successfully separate the modulated classes of cell.

Densely connected parts in networks are referred to as “communities”. Community structure is a hallmark of a variety of real-world networks; individual communities form functional modules constituting complex systems described by networks. Therefore, revealing community structure in networks is essential to approaching and understanding complex systems described by networks. This is the reason why network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we examine a novel type of community detection, which has not been examined so far but will be of great practical use. Suppose that we are given a set of source nodes that includes some (but not all) of “true” members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., “false” members of the community). We propose to detect the community from this “imperfect” and “inaccurate” set of source nodes using attractor neural-network dynamics. Community detection achieved by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is also analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known.

Imagery analysis represents a significant aspect of maritime domain awareness; however, the amount of imagery is exceeding human capability to process. Unfortunately, the maritime domain presents unique challenges for machine learning to automate such analysis. Indeed, when object recognition algorithms observe real-world data, they face hurdles not present in experimental situations. Imagery from such domains suffers from degradation, have limited examples, and vary greatly in format. These limitations are present satellite imagery because of the associated constraints in expense and capability. To this end, the Hypercube-based NeuroEvolution of Augmenting Topologies approach is investigated in addressing some such challenges for classifying maritime vessels from satellite imagery. Results show that HyperNEAT learns features from such imagery that allows better classification than Principal Component Analysis (PCA). Furthermore, HyperNEAT enables a unique capability to scale image sizes through the indirect encoding.

Crossover is an important genetic operator that re-combines beneficial genes together and rapidly traverses the fitness landscape. Unfortunately, neuro-evolution (NE) has not experienced the benefits of crossover. Indeed, observations have shown that crossover has been detrimental to NE approaches. Tangentially, speciation has become an important feature in NE for diversity maintenance; however, such speciation research has focused on what measure is driving speciation versus how the measure determines species. This research posits that appropriate speciation implementations enable effective crossover by determining an individual’s potential mating partners. Prior speciation research demonstrated the impact of restricting the mating pools of genomes on search performance. This paper investigates these concepts in the context of NE and results demonstrate; (1) the impact of speciation implementation in NE, (2) crossover’s negative effect on search in NE, and (3) a novel speciation approach that enables effective crossover in NE.