Code, space and everyday life

In this paper we examine the role of code (software) in the spatial formation of collective life. Taking the view that human life and coded technology are folded into one another, we theorise space as ontogenesis. Space, we posit, is constantly being bought into being through a process of transduction – the constant making anew of a domain in reiterative and transformative practices - as an incomplete solution to a relational problem. The relational problem we examine is the ongoing encounter between individuals and environment where the solution, to a greater or lesser extent, is code. Code, we posit, is diversely embedded in collectives as coded objects, coded infrastructure, coded processes and coded assemblages. These objects, infrastructure, processes and assemblages possess technicity, that is, unfolding or evolutive power to make things happen; the ability to mediate, supplement, augment, monitor, regulate, operate, facilitate, produce collective life. We contend that when the technicity of code is operationalised it transduces one of three forms of hybrid spatial formations: code/space, coded space and backgrounded coded space. These formations are contingent, relational, extensible and scaleless, often stretched out across networks of greater or shorter length. We demonstrate the coded transduction of space through three vignettes – each a day in the life of three people living in London, UK, tracing the technical mediation of their interactions, transactions and mobilities. We then discuss how code becomes the relational solution to five different classes of problems – domestic living, travelling, working, communicating, and consuming

In silico evolution of signaling networks using rule-based models: bistable response dynamics

One of the ultimate goals in biology is to understand the design principles of biological systems. Such principles, if they exist, can help us better understand complex, natural biological systems and guide the engineering of de novo ones. Towards deciphering design principles, in silico evolution of biological systems with proper abstraction is a promising approach. Here, we demonstrate the application of in silico evolution combined with rule-based modelling for exploring design principles of cellular signaling networks. This application is based on a computational platform, called BioJazz, which allows in silico evolution of signaling networks with unbounded complexity. We provide a detailed introduction to BioJazz architecture and implementation and describe how it can be used to evolve and/or design signaling networks with defined dynamics. For the latter, we evolve signaling networks with switch-like response dynamics and demonstrate how BioJazz can result in new biological insights on network structures that can endow bistable response dynamics. This example also demonstrated both the power of BioJazz in evolving and designing signaling networks and its limitations at the current stage of development.Comment: 24 pages, 7 figure

Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks

Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design

SCREENING INTERACTIONS BETWEEN PROTEINS AND DISORDERED PEPTIDES BY A NOVEL COMPUTATIONAL METHOD

Concerted interactions between proteins in cells form the basis of most biological processes. Biophysicists study protein–protein association by measuring thermodynamic and kinetic properties. Naively, strong binding affinity should be preferred in protein–protein binding to conduct certain biological functions. However, evidence shows that regulatory interactions, such as those between adapter proteins and intrinsically disordered proteins, communicate via low affinity but high complementarity interactions. PDZ domains are one class of adapters that bind linear disordered peptides, which play key roles in signaling pathways. The misregulation of these signals has been implicated in the progression of human cancers. To understand the underlying mechanism of protein-peptide binding interactions and to predict new interactions, in this thesis I have developed: (a) a unique biophysical-derived model to estimate their binding free energy; (b) a novel semi-flexible structure-based method to dock disordered peptides to PDZ domains; (c) predictions of the peptide binding landscape; and, (d) an automated algorithm and web-interface to predict the likelihood that a given linear sequence of amino acids binds to a specific PDZ domain. The docking method, PepDock, takes a peptide sequence and a PDZ protein structure as input, and outputs docked conformations and their corresponding binding affinity estimation, including their optimal free energy pathway. We have applied PepDock to screen several PDZ protein domains. The results not only validated the capabilities of PepDock to accurately discriminate interactions, but also explored the underlying binding mechanism. Specifically, I showed that interactions followed downhill free energy pathways, reconciling a relatively fast association mechanism of intrinsically disordered peptides. The pathways are such that initially the peptide’s C-terminal motif binds non-specifically, forming a weak intermediate, whereas specific binding is achieved only by a subsequent network of contacts (7–9 residues in total). This mechanism allows peptides to quickly probe PDZ domains, rapidly releasing those that do not attain sufficient affinity during binding. Further kinetic analysis indicates that disorder enhanced the specificity of promiscuous interactions between proteins and peptides, while achieving association rates comparable to interactions between ordered proteins

Exploiting structure defined by data in machine learning: some new analyses

This thesis offers some new analyses and presents some new methods for learning in the context of exploiting structure defined by data – for example, when a data distribution has a submanifold support, exhibits cluster structure or exists as an object such as a graph. 1. We present a new PAC-Bayes analysis of learning in this context, which is sharp and in some ways presents a better solution than uniform convergence methods. The PAC-Bayes prior over a hypothesis class is defined in terms of the unknown true risk and smoothness of hypotheses w.r.t. the unknown data-generating distribution. The analysis is “localized” in the sense that complexity of the model enters not as the complexity of an entire hypothesis class, but focused on functions of ultimate interest. Such bounds are derived for various algorithms including SVMs. 2. We consider an idea similar to the p-norm Perceptron for building classifiers on graphs. We define p-norms on the space of functions over graph vertices and consider interpolation using the pnorm as a smoothness measure. The method exploits cluster structure and attains a mistake bound logarithmic in the diameter, compared to a linear lower bound for standard methods. 3. Rademacher complexity is related to cluster structure in data, quantifying the notion that when data clusters we can learn well with fewer examples. In particular we relate transductive learning to cluster structure in the empirical resistance metric. 4. Typical methods for learning over a graph do not scale well in the number of data points – often a graph Laplacian must be inverted which becomes computationally intractable for large data sets. We present online algorithms which, by simplifying the graph in principled way, are able to exploit the structure while remaining computationally tractable for large datasets. We prove state-of-the-art performance guarantees

A dynamical model of the distributed interaction of intracellular signals

A major goal of modern cell biology is to understand the regulation of cell behavior in the reductive terms of all the molecular interactions. This aim is made explicit by the assertion that understanding a cell\u27s response to stimuli requires a full inventory of details. Currently, no satisfactory explanation exists to explain why cells exhibit only a relatively small number of different behavioral modes. In this thesis, a discrete dynamical model is developed to study interactions between certain types of signaling proteins. The model is generic and connectionist in nature and incorporates important concepts from the biology. The emphasis is on examining dynamic properties that occur on short-term time scales and are independent of gene expression. A number of modeling assumptions are made. However, the framework is flexible enough to be extended in future studies. The dynamical states of the system are explored both computationally and analytically. Monte Carlo methods are used to study the state space of simulated networks over selected parameter regimes. Networks show a tendency to settle into fixed points or oscillations over a wide range of initial conditions. A genetic algorithm (GA) is also designed to explore properties of networks. It evolves a population of modeled cells, selecting and ranking them according to a fitness function, which is designed to mimic features of real biological evolution. An analogue of protein domain shuffling is used as the crossover operator and cells are reproduced asexually. The effects of changing the parameters of the GA are explored. A clustering algorithm is developed to test the effectiveness of the GA search at generating cells, which display a limited number of different behavioral modes. Stability properties of equilibrium states in small networks are analyzed. The ability to generalize these techniques to larger networks is discussed. Topological properties of networks generated by the GA are examined. Structural properties of networks are used to provide insight into their dynamic properties. The dynamic attractors exhibited by such signaling networks may provide a framework for understanding why cells persist in only a small number of stable behavioral modes

Comparative Genomics of Microbial Chemoreceptor Sequence, Structure, and Function

Microbial chemotaxis receptors (chemoreceptors) are complex proteins that sense the external environment and signal for flagella-mediated motility, serving as the GPS of the cell. In order to sense a myriad of physicochemical signals and adapt to diverse environmental niches, sensory regions of chemoreceptors are frenetically duplicated, mutated, or lost. Conversely, the chemoreceptor signaling region is a highly conserved protein domain. Extreme conservation of this domain is necessary because it determines very specific helical secondary, tertiary, and quaternary structures of the protein while simultaneously choreographing a network of interactions with the adaptor protein CheW and the histidine kinase CheA. This dichotomous nature has split the chemoreceptor community into two major camps, studying either an organism’s sensory capabilities and physiology or the molecular signal transduction mechanism. Fortunately, the current vast wealth of sequencing data has enabled comparative study of chemoreceptors. Comparative genomics can serve as a bridge between these communities, connecting sequence, structure, and function through comprehensive studies on scales ranging from minute and molecular to global and ecological. Herein are four works in which comparative genomics illuminates unanswered questions across the broad chemoreceptor landscape. First, we used evolutionary histories to refine chemoreceptor interactions in Thermotoga maritima, pairing phylogenetics with x-ray crystallography. Next, we uncovered the origin of a unique chemoreceptor, isolated only from hypervirulent strains of Campylobacter jejuni, by comparing chemoreceptor signaling and sensory regions from Campylobacter and Helicobacter. We then selected the opportunistic human pathogen Pseudomonas aeruginosa to address the question of assigning multiple chemoreceptors to multiple chemotaxis pathways within the same organism. We assigned all P. aeruginosa receptors to pathways using a novel in silico approach by incorporating sequence information spanning the entire taxonomic order Pseudomonadales and beyond. Finally, we surveyed the chemotaxis systems of all environmental, commensal, laboratory, and pathogenic strains of the ubiquitous Escherichia coli, where we discovered an ancestral chemoreceptor gene loss event that may have predisposed a well-studied subpopulation to adopt extra-intestinal pathogenic lifestyles. Overall, comparative genomics is a cutting edge method for comprehensive chemoreceptor study that is poised to promote synergy within and expand the significance of the chemoreceptor field

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

Background: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. Results: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. Conclusions: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences