Nonlinearity and stochasticity in biochemical networks

Recent advances in biology have revolutionized our understanding of living systems. For the first time, it is possible to study the behavior of individual cells. This has led to the discovery of many amazing phenomena. For example, cells have developed intelligent mechanisms for foraging, communicating, and responding to environmental changes. These diverse functions in cells are controlled through biochemical networks consisting of many different proteins and signaling molecules. These molecules interact and affect gene expression, which in turn affects protein production. This results in a complex mesh of feedback and feedforward interactions. These complex networks are generally highly nonlinear and stochastic, making them difficult to study quantitatively. Recent studies have shown that biochemical networks are also highly modular, meaning that different parts of the network do not interact strongly with each other. These modules tend to be conserved across species and serve specific biological functions. However, detect- ing modules and identifying their function tends to be a very difficult task. To overcome some of these complexities, I present an alternative modeling approach that builds quantitative models using coarse-grained biological processes. These coarse-grained models are often stochastic (probabilistic) and highly non-linear. In this thesis, I focus on modeling biochemical networks in two distinct biological systems: Dictyostelium discoideum and microRNAs. Chapters 2 and 3 focus on cellular communication in the social amoebae Dictyostelium discoideum. Using universality, I propose a stochastic nonlinear model that describes the behavior of individual cells and cellular populations. In chapter 4 I study the interaction between messenger RNAs and noncoding RNAs, using Langevin equations

Simulating the phase behavior of the Kuramoto tree

The Kuramoto model is a versatile mathematical framework that explains phenomena resulting from interactions among phase oscillators. It finds applications in various scientific and engineering domains. In this study, we focused on a Y-shaped network, which serves as the fundamental unit of a tree network. By simulating oscillators on the network, we generated heat maps for different numbers of nodes and coupling strengths and demonstrated the occurrence of different phases. Our findings reveal transitions between synchronization, wave state, and chaos within the system

Intrinsic noise of microRNA-regulated genes and the ceRNA hypothesis

MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally by binding and degrading target eukaryotic mRNAs. We use a quantitative model to study gene regulation by inhibitory microRNAs and compare it to gene regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a combination of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regulated by both microRNAs and sRNAs. We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regulated by prokaryotic sRNAs. This behavior is extremely robust and persists across a wide range of biologically relevant parameters. We extend our model to study crosstalk between multiple mRNAs that are regulated by a single microRNA and show that noise is a sensitive measure of microRNA-mediated interaction between mRNAs. We conclude by discussing possible experimental strategies for uncovering the microRNA-mRNA interactions and testing the competing endogenous RNA (ceRNA) hypothesis.Comment: 32 pages, 11 figure

On Dr. Stockmann’s Parrhesia: Ibsen’s “An Enemy of the People” in the Light of Foucault

An honest intellectual dutifully standing with truth against lies and treacheries of his society is a parrhesiastic figure in Foucault’s terminology. Foucault takes parrhesia as the fearless and frank speech regarding the truth of something or a situation before truth-mongering and public deception and he takes the parrhesiastic as the spokesperson for truth. In this light, Dr. Stockmann in Ibsen’s An Enemy of the People occupies a unique position within Ibsen’s political philosophy. Dutifully criticizing what the majority blindly take for granted from their liar leaders in the name of democracy, Dr. Stockmann fulfills the role of a parrhesiastic figure that stands against socio-political corruption. He enters a parrhesiastic game with both the majority and the officialdom to fulfill his democratic parrhesia as a truthful citizen before the duped community, while covertly preparing for his own philosophic parrhesia or self-care within the conformist community. However, his final failure lies in his confrontation with democracy itself, which wrongly gives the right of speaking even to the liars. This article thus aims at analyzing Ibsen’s play through a Foucauldian perspective regarding the concept of parrhesia and its relation to democracy. It is to reveal Ibsen’s satire on the fake ideology of democracy and highlight the necessity of humanity’s parrhesiastic self-care for the well-being of the self and the others

Multiscale modeling of oscillations and spiral waves in Dictyostelium populations

Unicellular organisms exhibit elaborate collective behaviors in response to environmental cues. These behaviors are controlled by complex biochemical networks within individual cells and coordinated through cell-to-cell communication. Describing these behaviors requires new mathematical models that can bridge scales -- from biochemical networks within individual cells to spatially structured cellular populations. Here, we present a family of multiscale models for the emergence of spiral waves in the social amoeba Dictyostelium discoideum. Our models exploit new experimental advances that allow for the direct measurement and manipulation of the small signaling molecule cAMP used by Dictyostelium cells to coordinate behavior in cellular populations. Inspired by recent experiments, we model the Dictyostelium signaling network as an excitable system coupled to various pre-processing modules. We use this family of models to study spatially unstructured populations by constructing phase diagrams that relate the properties of population-level oscillations to parameters in the underlying biochemical network. We then extend our models to include spatial structure and show how they naturally give rise to spiral waves. Our models exhibit a wide range of novel phenomena including a density dependent frequency change, bistability, and dynamic death due to slow cAMP dynamics. Our modeling approach provides a powerful tool for bridging scales in modeling of Dictyostelium populations

Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power.

Mutant allele frequency distributions in cancer samples have been used to estimate intratumoral heterogeneity and its implications for patient survival. However, mutation calls are sensitive to the calling algorithm. It remains unknown whether the relationship of heterogeneity and clinical outcome is robust to these variations. To resolve this question, we studied the robustness of allele frequency distributions to the mutation callers MuTect, SomaticSniper, and VarScan in 4722 cancer samples from The Cancer Genome Atlas. We observed discrepancies among the results, particularly a pronounced difference between allele frequency distributions called by VarScan and SomaticSniper. Survival analysis showed little robust predictive power for heterogeneity as measured by Mutant-Allele Tumor Heterogeneity (MATH) score, with the exception of uterine corpus endometrial carcinoma. However, we found that variations in mutant allele frequencies were mediated by variations in copy number. Our results indicate that the clinical predictions associated with MATH score are primarily caused by copy number aberrations that alter mutant allele frequencies. Finally, we present a mathematical model of linear tumor evolution demonstrating why MATH score is insufficient for distinguishing different scenarios of tumor growth. Our findings elucidate the importance of allele frequency distributions as a measure for tumor heterogeneity and their prognostic role

Tubitak i „potencijal za bitak“ u pustoj zemlji: Austerova Zemlja posljednjih stvari

This paper proposes a reading of Paul Auster’s novel In the Country of Last Things (1987) through the conceptual lens of Heidegger’s theory of Dasein. It focuses on Heidegger’s definition of human nature as Dasein by discussing the range of existential possibilities that the German philosopher outlined for human beings in order to make authentic sense of their being and life before death. The progression from birth to death constitutes Dasein’s state of being or its existence. However, not many individuals are conscious of this process, being lost in the limiting situation of their everydayness. Accordingly, inauthentic lives without understanding one’s true possibilities take place. A fictional visualization of Dasein’s attempts at an authentic existence within its limiting situation or, we could say, within its typical society, can concretize Heidegger’s points in a better way. Concerning Paul Auster’s existential outlook on life, In the Country of Last Things is a portrayal of such a struggle for an authentic existence in a dystopian predicament where humankind is thrown into the lowest possible situation. Allegorically, the novel is a laboratory for experimenting with human potentiality for being in the face of severely lacking conditions for the fulfilment of biological needs, with death always in the background. In such a thrown state of life, the protagonist, Anna Blume, is called to authenticity against others’ inauthenticity and life-threatening situations, highlighting the possibility of living in a dystopia through authentic selfhood. The paper thus argues that Auster’s existentialism in this novel is not alien to Heidegger’s worldview on human existence.Rad predlaže čitanje romana Paula Austera U zemlji posljednjih stvari (1987) kroz konceptualnu perspektivu Heideggerove teorije tubitka. Naglasak se stavlja na Heideggerovu definiciju ljudske prirode kao tubitka i pritom se raspravlja o rasponu egzistencijalnih mogućnosti na koje njemački filozof upućuje ljudska bića kako bi prije smrti postigla autentičan osjećaj vlastitoga bića i života. Kretanje od rođenja prema smrti predstavlja tubitkovo stanje bitka, odnosno egzistenciju. Međutim, rijetki su svjesni toga procesa te su izgubljeni u ograničavajućoj situaciji svakodnevnog života. U skladu s time, neautentični su životi nesvjesni vlastitih mogućnosti. Fikcionalna vizualizacija tubitkovih pokušaja autentičnoga postojanja unutar svoje ograničavajuće situacije ili, mogli bismo reći, unutar svog uobičajenoga društva, može na bolji način konkretizirati Heideggerove tvrdnje. Kada je u pitanju Austerov egzistencijalni pogled na život, U zemlji posljednjih stvari prikaz je jedne takve borbe za autentično postojanje u distopijskom svijetu u kojem je čovječanstvo svedeno na najnižu životnu situaciju. U alegorijskom smislu, roman predstavlja laboratorij za eksperimentiranje s ljudskim potencijalom bitka suočenog s izuzetno nepovoljnim uvjetima za ispunjenje bioloških potreba neprestano praćenima smrću koja vreba iz prikrajka. U takvom teškom životnom okruženju protagonistica Anna Blume osjeća poriv prema autentičnosti usprkos neautentičnosti drugih ljudi i sveprisutnoj smrtnoj opasnosti, pritom naglašavajući mogućnost života usred distopije na temelju autentične svijesti o sebi. U radu se stoga tvrdi da je egzistencijalizam u Austerovu romanu blizak Heideggerovu svjetonazoru o ljudskom postojanju

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNN architectures to analyze 27,815 hematoxylin and eosin scanned images from The Cancer Genome Atlas for tumor/normal, cancer subtype, and mutation classification. Our CNNs are able to classify TCGA pathologist-annotated tumor/normal status of whole slide images (WSIs) in 19 cancer types with consistently high AUCs (0.995 ± 0.008), as well as subtypes with lower but significant accuracy (AUC 0.87 ± 0.1). Remarkably, tumor/normal CNNs trained on one tissue are effective in others (AUC 0.88 ± 0.11), with classifier relationships also recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45 ± 0.16 between classifier pairs. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. Patterns for TP53 mutations can also be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80. Finally, we comparatively evaluate CNNs on 170 breast and colon cancer images with pathologist-annotated nuclei, finding that both cellular and intercellular regions contribute to CNN accuracy. These results demonstrate the power of CNNs not only for histopathological classification, but also for cross-comparisons to reveal conserved spatial behaviors across tumors