Toward Cultural Oncology: The Evolutionary Information Dynamics of Cancer

'Racial' disparities among cancers, particularly of the breast and prostate, are something of a mystery. For the US, in the face of slavery and its sequelae, centuries of interbreeding have greatly leavened genetic differences between 'Blacks' and 'whites', but marked contrasts in disease prevalence and progression persist. 'Adjustment' for socioeconomic status and lifestyle, while statistically accounting for much of the variance in breast cancer, only begs the question of ultimate causality. Here we propose a more basic biological explanation that extends the theory of immune cognition to include elaborate tumor control mechanisms constituting the principal selection pressure acting on pathologically mutating cell clones. The interplay between them occurs in the context of an embedding, highly structured, system of culturally specific psychosocial stress which we find is able to literally write an image of itself onto disease progression. The dynamics are analogous to punctuated equilibrium in simple evolutionary proces

The generation of cortical novelty responses through inhibitory plasticity

Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions

Motif Statistics and Spike Correlations in Neuronal Networks

Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network model of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second-order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state

The Interplay of Architecture and Correlated Variability in Neuronal Networks

This much is certain: neurons are coupled, and they exhibit covariations in their output. The extent of each does not have a single answer. Moreover, the strength of neuronal correlations, in particular, has been a subject of hot debate within the neuroscience community over the past decade, as advancing recording techniques have made available a lot of new, sometimes seemingly conflicting, datasets. The impact of connectivity and the resulting correlations on the ability of animals to perform necessary tasks is even less well understood. In order to answer relevant questions in these categories, novel approaches must be developed. This work focuses on three somewhat distinct, but inseparably coupled, crucial avenues of research within the broader field of computational neuroscience. First, there is a need for tools which can be applied, both by experimentalists and theorists, to understand how networks transform their inputs. In turn, these tools will allow neuroscientists to tease apart the structure which underlies network activity. The Generalized Thinning and Shift framework, presented in Chapter 4, addresses this need. Next, taking for granted a general understanding of network architecture as well as some grasp of the behavior of its individual units, we must be able to reverse the activity to structure relationship, and understand instead how network structure determines dynamics. We achieve this in Chapters 5 through 7 where we present an application of linear response theory yielding an explicit approximation of correlations in integrate--and--fire neuronal networks. This approximation reveals the explicit relationship between correlations, structure, and marginal dynamics. Finally, we must strive to understand the functional impact of network dynamics and architecture on the tasks that a neural network performs. This need motivates our analysis of a biophysically detailed model of the blow fly visual system in Chapter 8. Our hope is that the work presented here represents significant advances in multiple directions within the field of computational neuroscience.Mathematics, Department o

A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data

Correlations in neural activity have been demonstrated to have profound consequences for sensory encoding. To understand how neural populations represent stimulus information, it is therefore necessary to model how pairwise and higher-order spiking correlations between neurons contribute to the collective structure of population-wide spiking patterns. Maximum entropy models are an increasingly popular method for capturing collective neural activity by including successively higher-order interaction terms. However, incorporating higher-order interactions in these models is difficult in practice due to two factors. First, the number of parameters exponentially increases as higher orders are added. Second, because triplet (and higher) spiking events occur infrequently, estimates of higher-order statistics may be contaminated by sampling noise. To address this, we extend previous work on the Reliable Interaction class of models to develop a normalized variant that adaptively identifies the specific pairwise and higher-order moments that can be estimated from a given dataset for a specified confidence level. The resulting “Reliable Moment” model is able to capture cortical-like distributions of population spiking patterns. Finally, we show that, compared with the Reliable Interaction model, the Reliable Moment model infers fewer strong spurious higher-order interactions and is better able to predict the frequencies of previously unobserved spiking patterns

Searching for collective behavior in a network of real neurons

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.Comment: 24 pages, 19 figure

Towards efficient siRNA delivery and gene silencing kinetics on the single cell level

RNA interference (RNAi) is a natural sequence-specific mechanism of post-transcriptional gene regulation leaded by short, double stranded RNA fragments e.g. small interfering RNAs (siRNA). Despite its high therapeutic potential, the safe and efficient systemic delivery of siRNAs into a large number of diseased cells to trigger therapeutic gene knockdown remains challenging. Moreover, novel quantitative methods for assessing activity of siRNA-based therapeutic agents in a fast and precise manner are needed. In this work, we firstly developed the folate-targeted monomolecular nucleic acid/lipid particles (FolA-mNALPs) formed using microfluidic-based method and studied their functionality regarding prospective use as a siRNA delivery agent. Secondly, we quantify the single-cell kinetics of siRNA-mediated gene silencing using micro-patterned cell cultivation substrates combined with time-lapse fluorescence microscopy (life-cell imaging on single-cell arrays, LISCA). In particular, we demonstrate that microfluidic self-assembly combined with rational design of lipid formulation results in nanoparticles of small size and narrow size distribution that in average contain single siRNA molecule covered with a single lipid bilayer (mNALP). We investigate the stability of folate-functionalized mNALPs in biological fluids, and their biological performance in terms of cellular internalisation and silencing efficiency. Small sizes, efficient targeting and presented silencing capability following facilitated endosomal release make mNALP a promising system for the future development of an in vivo siRNA delivery agent. Furthermore, using LISCA we investigate the magnitude of siRNA-induced mRNA degradation. By mathematical modelling of gene expression and fitting of expression time-courses we obtain the population distributions of rate constants related with the model, including single-cell mRNA degradation rate constants. The expression time-courses are gained by monitoring the dynamic changes in single-cell fluorescence intensities of reporter proteins (eGFP target and CayRFP reference). Obtained kinetic parameters allow us to quantify the silencing efficiency as the relative fold-change in mRNA degradation rate constants, to identify the subpopulations of cells affected by siRNA activity and, by analysis of correlations between kinetic parameters of CayRFP and eGFP expression, to infer on the properties of mRNA delivery and expression kinetics. Presented approach allows for the precise quantification of the activity of siRNA-based therapeutics in an accurate and fast (<30h) manner based on the analysis of time-independent kinetic parameters describing the silencing process.RNA-Interferenz (RNAi) ist ein natürlicher Mechanismus der posttranskriptionalen Genregulation in eukaryotischen Zellen. RNAi kann spezifische Gene ansteuern und bietet hohe Flexilitiät in der Wahl der angesteuerten mRNA Sequenzregionen.Diese beiden Charakteristika machen RNAi zu einem vielseitigen Werkzeug bei der Untersuchung von Genfunktionen und zu einem möglichen Therapeutikum für eine große Vielfalt an Erkrankungen. Im Rahmen dieser Arbeit wurden a) eine mikrofluidik-basierte Methode zur verbesserten Selbst-Assemblierung von monomolekularen Nukleinsäure/ -lipidteilchen (mNALPs) für ihren möglichen zukünftigen Nutzen als siRNA-Lieferant entwickelt und b) die Einzelzellantworten auf siRNA-induzierte Genstilllegung untersucht. Wir bestimmen insbesondere die optimalen Parameter für die Selbst-Assemblierung von mNALPs, untersuchen deren Stabilität in biologischen Flüssigkeiten und ihre Wirkungsweise bezüglich zelltypspezifischer Internalisierung und Stilllegungseffizienz in in-vitro Zellexperimenten. Des Weiteren verwenden wir Lebendzell-Videomikroskopie auf mikrostrukturierten Substraten („live-cell imaging on single cell arrys“, LISCA) um die, durch siRNA-Aktivität induzierte, relative Veränderung der mRNA-Degradierungsratenkonstanten zu untersuchen.Eine Aussage über die Stärke der siRNA-induzierten mRNA Degradierung kann durch das mathematische Modell der Genexpression und das Fitten der Fluoreszenz-Zeitkurven getroffen werden, die aus den dynamischen Veränderungen in der Einzelzellfluoreszenzintensitäten der Reporterproteine gewonnen wird. Diese Prozedur liefert die Populationsverteilung von Ratenkonstanten, welche mit dem Modell verbunden sind. Dadurch können wir die Effizienz der Gen-Stilllegung als relative Veränderung der mRNA-Degradationsratenkonstanten quantifizieren und zusätzlich Subpopulationen von Zellen identifizieren, welche von der siRNA-Aktivität nicht betroffen sind. Zudem kann die Analyse der Korrelationen zwischen den kinetischen Parametern der CayRFP- und eGFP-Expressionen einen Rückschluss auf die Eigenschaften der mRNA-Lieferung der Expressionskinetik erlauben. Die nanoskalige Größe, Stabilität, spezifisches Targeting und die demonstrierte spezifische Stillegung eines Gens, machen mNALP zu einer vielversprechenden Grundlage für ein zukünftiges in-vivo siRNA-Transfersystems. Zudem stellen wir die mikroskopiebasierte Methode LISCA vor, welche eine präzise Quantifizierung der Aktivität von siRNA-basierten Therapeutika erlaubt. Auf akkurate und schnelle Weise (< 30h) können damit zeitabhängige kinetische Parameter, welche den Stillegungsprozess von Genen beschreiben, gewonnen werden