Evolution of biological cooperation: An algorithmic approach

This manuscript presents an algorithmic approach to cooperation in biological systems, drawing on fundamental ideas from statistical mechanics and probability theory. Fisher’s geometric model of adaptation suggests that the evolution of organisms well adapted to multiple constraints comes at a significant complexity cost. By utilizing combinatorial models of fitness, we demonstrate that the probability of adapting to all constraints decreases exponentially with the number of constraints, thereby generalizing Fisher’s result. Our main focus is understanding how cooperation can overcome this adaptivity barrier. Through these combinatorial models, we demonstrate that when an organism needs to adapt to a multitude of environmental variables, division of labor emerges as the only viable evolutionary strategy

Dynamical mean field theory for models of confluent tissues and beyond

We consider a recently proposed model to understand the rigidity transition in confluent tissues and we study its dynamical behavior under several types of dynamics: gradient descent, thermal Langevin noise and active drive. We derive the dynamical mean field theory equations and integrate them numerically and compare the results with numerical simulations. In particular we focus on gradient descent dynamics and show that this algorithm is blind to the zero temperature replica symmetry breaking (RSB) transition point. In other words, even if the Gibbs measure at zero temperature is RSB, the algorithm is able to find its way to a zero energy configuration. This is somehow expected and agrees with previous findings in numerical simulations on other examples of continuous constraint satisfaction problems. Our results can be also straightforwardly applied to the study of high-dimensional regression tasks where the fitting functions are non-linear functions of a set of weights found via the optimization of the square loss.Comment: 17 pages, 3 figures, Submission to SciPos

Systems biology of breast cancer

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach

STOCHASTIC MOBILITY MODELS IN SPACE AND TIME

An interesting fact in nature is that if we observe agents (neurons, particles, animals, humans) behaving, or more precisely moving, inside their environment, we can recognize - tough at different space or time scales - very specific patterns. The existence of those patterns is quite obvious, since not all things in nature behave totally at random, especially if we take into account thinking species like human beings. If a first phenomenon which has been deeply modeled is the gas particle motion as the template of a totally random motion, other phenomena, like foraging patterns of animals such as albatrosses, and specific instances of human mobility wear some randomness away in favor of deterministic components. Thus, while the particle motion may be satisfactorily described with a Wiener Process (also called Brownian motion), the others are better described by other kinds of stochastic processes called Levy Flights. Minding at these phenomena in a unifying way, in terms of motion of agents \u2013 either inanimate like the gas particles, or animated like the albatrosses \u2013 the point is that the latter are driven by specific interests, possibly converging into a common task, to be accomplished. The whole thesis work turns around the concept of agent intentionality at different scales, whose model may be used as key ingredient in the statistical description of complex behaviors. The two main contributions in this direction are: 1. the development of a \u201cwait and chase\u201d model of human mobility having the same two-phase pattern as animal foraging but with a greater propensity of local stays in place and therefore a less dispersed general behavior; 2. the introduction of a mobility paradigm for the neurons of a multilayer neural network and a methodology to train these new kind of networks to develop a collective behavior. The lead idea is that neurons move toward the most informative mates to better learn how to fulfill their part in the overall functionality of the network. With these specific implementations we have pursued the general goal of attributing both a cognitive and a physical meaning to the intentionality so as to be able in a near future to speak of intentionality as an additional potential in the dynamics of the masses (both at the micro and a the macro-scale), and of communication as another network in the force field. This could be intended as a step ahead in the track opened by the past century physicists with the coupling of thermodynamic and Shannon entropies in the direction of unifying cognitive and physical laws

Novel approaches to study the design principles of turing patterns

A fundamental concern in biology is the origins of, and the mechanisms responsible for the structures and patterns observed within, organisms [1]. Turing patterns and the Turing mechanism may explain the processes behind biological pattern formation. Theoretical studies of the Turing mechanism show that it is highly sensitive to fluctuations and variations in kinetic parameters. Various experiments have shown that biochemical processes in living cells are inherently noisy systems, they are subjected to a diverse range of fluctuations. This ‘robustness problem’ raises the question of how such a seemingly sensitive mechanism could produce robust patterns amidst noise [2]. Recent computational advances allow for large-scale explorations of the design space of regulatory networks underpinning pattern production. Such explorations generate insights into the Turing mechanism’s robustness and sensitivity. Part 1 of the thesis performs a large-scale exploration within a discrete modelling framework, identifying the same pattern producing network types identified within previous studies. The equivalence we find across modelling frameworks suggests that a deeper underlying principle of these Turing mechanisms exist. In contrast to the continuous case , networks appear to be more robust in the discrete framework we explore here, suggesting that these networks might be more robust than previously thought. Part 2 of the thesis focuses on Turing patterns as a inverse problem: is it possible to infer the parameters that most likely produced a given pattern? Here, we distill the information of a pattern into a one-dimensional representation based on resistance distances, a concept from electrical networks [3]. We shown this representation to be robust against fluctuations in the pattern stemming from random initial conditions, or stochasticity of the model, and therefore permits the application of machine learning methods such as neural networks and support vector regression for parameter inference. We apply this method to infer one and three parameters for both deterministic and stochastic models of the Gierer-Meinhardt system. We show that the ’resistance distance histogram’ method is more robust to noise, and performs better for limited number of data samples than a vanilla convolutional neural network approach. Robustness of parameter inference with respect to noise and limited data samples is of particular importance when considering real experimental data. Overall, this thesis advances our understanding on the design principles of pattern formation, and provides insight into possible methods for inferring details of regulatory networks behind experimental evidence of Turing patterns.Open Acces

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Analysis, modelling and prediction of deterministic and stochastic complex systems

The analysis of complex systems at nano- and micro-scales often requires their numerical simulation. Atomistic simulations, that rely on solving Newton's equation for each component of the system, despite being exact, are often too computationally expensive. In this work, firstly we analyse the properties of confined systems by extracting mesoscopic information directly from particles coordinate. Then, taking advantage of Mori-Zwanzig projector operator techniques and advanced data-analysis tools, we present a novel approach to parametrize non-Markovian coarse-graining models of molecular system. We focus on the parametrization of the memory terms in the stochastic Generalized Langevin Equation through a deep-learning approach. Moreover, in the framework of Dynamical Density Functional Theory (DDFT) we derive a continuum non-Markovian formulation, able to describe, given the proper free-energy, the physical properties of an atomistic system. Comparisons between molecular dynamics, fluctuating dynamical density functional theory and fluctuating hydrodynamics simulations validate our approach. Finally, we propose some numerical schemes for the simulation of DDFT with additional complexities, i.e. with stochastic terms and non-homogeneous non-constant diffusion.Open Acces

DATA-DRIVEN ANALYSIS AND MAPPING OF THE POTENTIAL DISTRIBUTION OF MOUNTAIN PERMAFROST

In alpine environments, mountain permafrost is defined as a thermal state of the ground and it corresponds to any lithosphere material that is at or below 0°C for at least two years. Its degradation is potentially leading to an increasing rock fall activity and sediment transfer rates. During the last 20 years, knowledge on this phenomenon has significantly improved thanks to many studies and monitoring projects, revealing an extremely discontinuous and complex spatial distribution, especially at the micro scale (scale of a specific landform; tens to several hundreds of metres). The objective of this thesis was the systematic and detailed investigation of the potential of data-driven techniques for mountain permafrost distribution modelling. Machine learning (ML) algorithms are able to consider a greater number of pa- rameters compared to classic approaches. Not only can permafrost distribution be modelled by using topo-climatic parameters as a proxy, but also by taking into ac- count known field permafrost evidences. These latter were collected in a sector of the Western Swiss Alps and they were mapped from field data (thermal and geoelectrical data) and ortho-image interpretations (rock glacier inventorying). A permafrost dataset was built from these evidences and completed with environmental and mor- phological predictors. Data were firstly analysed with feature relevance techniques in order to identify the statistical contribution of each controlling factor and to exclude non-relevant or redundant predictors. Five classification algorithms, belonging to statistics and machine learning, were then applied to the dataset and tested: Logistic regression (LR), linear and non-linear Support Vector Machines (SVM), Multilayer perceptrons (MLP) and Random forests (RF). These techniques inferred a classifica- tion function from labelled training data (pixels of permafrost absence and presence) to predict the permafrost occurrence where this was unknown. Classification performances, assessed with AUROC curves, ranged between 0.75 (linear SVM) and 0.88 (RF). These values are generally indicative of good model performances. Besides these statistical measures, a qualitative evaluation was performed by using field expert knowledge. Both quantitative and qualitative evaluation approaches suggested to employ the RF algorithm to obtain the best model. As machine learning is a non-deterministic approach, an overview of the model uncertainties is also offered. It informs about the location of most uncertain sectors where further field investigations are required to be carried out to improve the reliability of permafrost maps. RF demonstrated to be efficient for permafrost distribution modelling thanks to consistent results that are comparable to the field observations. The employment of environmental variables illustrating the micro-topography and the ground charac- teristics (such as curvature indices, NDVI or grain size) favoured the prediction of the permafrost distribution at the micro scale. These maps presented variations of probability of permafrost occurrence within distances of few tens of metres. In some talus slopes, for example, a lower probability of occurrence in the mid-upper part of the slope was predicted. In addition, permafrost lower limits were automatically recognized from permafrost evidences. Lastly, the high resolution of the input dataset (10 metres) allowed elaborating maps at the micro scale with a modelled permafrost spatial distribution, which was less optimistic than traditional spatial models. The permafrost prediction was indeed computed without recurring to altitude thresh- olds (above which permafrost may be found) and the representation of the strong discontinuity of mountain permafrost at the micro scale was better respected. -- Dans les environnements alpins, le pergélisol de montagne est défini comme un état thermique du sol et correspond à tout matériau de la lithosphère qui maintient une température égale ou inférieure à 0°C pendant au moins deux ans. Sa dégradation peut conduire à une activité croissante de chutes de blocs et à une augmentation des taux de transfert de sédiments. Au cours des 20 dernières années, les connaissances sur ce phénomène ont considérablement augmenté grâce à de nombreuses études et projets de suivi, qui ont révélé une distribution spatiale extrêmement discontinue et complexe du phénomène, en particulier à la micro-échelle (échelle d’une forme géomorphologique; dizaines à plusieurs centaines de mètres). L’objectif de cette recherche était l’étude systématique et détaillée des potentialités offertes par une approche axée sur les données dans le cadre de la modélisation de la distribution du pergélisol de montagne. Les algorithmes d’apprentissage au- tomatique (machine learning) sont capables de considérer un plus grand nombre de variables que les approches classiques. La distribution du pergélisol peut être modélisée non seulement en utilisant des paramètres topo-climatiques (altitude, radiation solaire, etc.), mais aussi en tenant compte de la présence et de l’absence connues du pergélisol (observations de terrain). Collectées dans un secteur des Alpes occidentales suisses, ces dernières ont été cartographiées sur la base d’investigations de terrain (données thermiques et géoélectriques), d’interprétation d’orthophotos et d’inventaires de glaciers rocheux. Un jeu de données a été construit à partir de ces évidences de terrain et complété par des prédicteurs environnementaux et morphologiques. Les données ont d’abord été analysées avec des techniques mon- trant la pertinence des variables permettant d’identifier la contribution statistique de chaque facteur de contrôle et d’exclure les prédicteurs non pertinents ou redondants. Cinq algorithmes de classification appartenant aux domaines des statistiques et de l’apprentissage automatique ont ensuite été appliqués et testés : Logistic regression (LR), la version linéaire et non-linéaire de Support Vector Machines (SVM), Mul- tilayer perceptrons (MLP) et Random forests (RF). Ces techniques déduisent une fonction de classification à partir des données dites d’entraînement représentant l’absence et la présence certaine du pergélisol. Elles permettent ensuite de prédire l’occurrence du phénomène là où elle est inconnue. Les performances de classification, évaluées avec des courbes AUROC, variaient entre 0.75 (SVM linéaire) et 0.88 (RF). Ces valeurs sont généralement indicatives de bonnes performances. En plus de ces mesures statistiques, une évaluation qualitative a été réalisée et se base sur l’expertise géomorphologique. Les RF se sont révélées être la technique produisant le meilleur modèle. Comme l’apprentissage automatique est une approche non déterministe, il a également offert un aperçu des incertitudes de la modélisation, qui informent sur la localisation des secteurs les plus incertains dans lesquels des futures campagnes de terrain méritent d’être menées afin d’améliorer la fiabilité des cartes produites. Finalement, RF ont démontré leur efficacité dans le cadre de la modélisation de la distribution du pergélisol grâce à des résultats comparables aux observations de terrain. L’emploi de variables environnementales illustrant la micro-topographie du relief et les caractéristiques du sol (tels que les indices de courbure, le NDVI et la granulométrie) favorise la prédiction de la distribution du pergélisol à la micro- échelle, avec des cartes présentant des variations de la probabilité d’occurrence du pergélisol sur des distances de quelques dizaines de mètres. Par exemple, dans cer- tains éboulis, les cartes illustrent une probabilité plus faible dans la partie amont de la pente, ce qui s’avère cohérent avec les observations de terrain. La limite inférieure du pergélisol a ainsi été automatiquement reconnue à partir des évidences de terrain fournies à l’algorithme. Enfin, la haute résolution du jeu de données (10 mètres) a permis d’élaborer des cartes présentant une distribution spatiale du pergélisol moins optimiste que celle offerte par les modèles spatiaux classiques. La prédiction du pergélisol a en effet été calculée sans utiliser des seuils d’altitude (au-dessus desquels on peut trouver du pergélisol) et respecte ainsi mieux la représentation de la forte discontinuité du pergélisol de montagne à la micro-échelle. -- Negli ambienti alpini, il permafrost di montagna è definito come uno stato termico del suolo e corrisponde a qualsiasi materiale nella litosfera che mantiene una temper- atura uguale o inferiore a 0° C per almeno due anni. La sua degradazione può portare ad una crescente attività di caduta di blocchi e ad un aumento dei tassi di trasferi- mento dei sedimenti. Negli ultimi 20 anni, le conoscenze riguardanti il permafrost di montagna sono aumentate considerevolmente grazie ai numerosi studi e progetti di monitoraggio che hanno rivelato una distribuzione spaziale fortemente discontinua e complessa del fenomeno, in particolare alla scala della forma geomorfologica (definita come la micro scala, da decine a diverse centinaia di metri). L’obiettivo di questa ricerca é lo studio sistematico e dettagliato delle potenzialità offerte da un approccio basato sui dati, nell’ottica di una modellizzazione della distribuzione del permafrost di montagna. Gli algoritmi di apprendimento auto- matico (machine learning) sono in grado di considerare più variabili rispetto agli approcci classici. La distribuzione del permafrost può essere modellizzata non solo utilizzando i parametri topo-climatici classici (altitudine, radiazione solare, ecc.), ma anche considerando esempi di presenza e assenza del permafrost (osservazioni sul campo). Raccolti in un’area delle Alpi occidentali svizzere, questi ultimi sono stati mappati sulla base di indagini di terreno (dati termici e geoelettrici), interpretazione di ortofoto e inventari di ghiacciai rocciosi. A partire dalle evidenze di terreno, è stato creato un set di dati, al quale sono stati integrati diversi predittori ambien- tali e morfologici. I dati sono stati dapprima analizzati con tecniche di indagine della rilevanza delle variabili; tali tecniche sono capaci di identificare il contributo statistico di ciascun fattore di controllo del permafrost e sono in grado di escludere i predittori non pertinenti o ridondanti. Sono stati, quindi, applicati e testati cinque al- goritmi di classificazione appartenenti ai campi della statistica e dell’apprendimento automatico: Logistic regression (LR), la versione lineare e non lineare di Support Vector Machines (SVM), Multilayer Perceptron (MLP) e Random forest (RF). Queste tecniche deducono una funzione di classificazione dai cosiddetti dati di allenamento, che rappresentano l’assenza e la presenza certa del permafrost, e permettono in seguito di predire il fenomeno laddove è sconosciuto. Le prestazioni di classificazione, valutate con le curve AUROC, variavano da 0.75 (SVM lineare) a 0.88 (RF). Questi valori sono generalmente indicativi di buone prestazioni. Oltre a queste misure statistiche, è stata effettuata una valutazione qualitativa. RF si é rivelata essere la tecnica che produce il modello migliore. Poiché l’apprendimento automatico è un approccio non deterministico, é stato possibile ottenere informazioni sulle incertezze della modellizzazione. Quest’ultime indicano in quali aree il modello é più incerto e, dunque, dove occorre pianificare nuove campagne di terreno per migliorare l’affidabilità delle mappe prodotte. RF ha dimostrato la sua efficacia nella modellizzazione della distribuzione del per- mafrost con risultati paragonabili alle osservazioni sul campo. L’uso di variabili ambientali che illustrano la topografia e le caratteristiche del suolo (come indici di curvatura, NDVI e granulometria) aiuta a predire la distribuzione del permafrost alla micro scala, con mappe che mostrano variazioni spaziali importanti della probabilità del permafrost su distanze di poche decine di metri. In alcune falde di detrito le mappe mostrano una probabilità inferiore nella parte a monte, risultato coerente con le osservazioni sul campo. Il limite inferiore del permafrost è stato inoltre riconosci- uto automaticamente dagli esempi forniti all’algoritmo. Infine, l’alta risoluzione del set di dati (10 metri) ha permesso una simulazione della distribuzione spaziale del fenomeno meno ottimistica rispetto a quella fornita dai modelli classici. La previsione del permafrost è stata, infatti, calcolata senza utilizzare delle soglie di altitudine e quindi rispetta meglio la rappresentazione dell’alta discontinuità del permafrost di montagna alla micro scala

Intracellular network attractor selection and the problem of cell fate decision

This project aims at understanding how cell fate decision emerges from the overall intracellular network connectivity and dynamics. To achieve this goal both small paradigmatic signalling-gene regulatory networks and their generalization to highdimensional space were tested. Particularly, we drew special attention to the importance of the effects of time varying parameters in the decision genetic switch with external stimulation. The most striking feature of our findings is the clear and crucial impact of the rate with which the time-dependent parameters are changed. In the presence of small asymmetries and fluctuations, slow passage through the critical region increases substantially specific attractor selection by external transient perturbations. This has strong implications for the cell fate decision problem since cell phenotype in stem cell differentiation, cell cycle progression, or apoptosis studies, has been successfully identified as attractors of a whole network expression process induced by signalling events. Moreover, asymmetry and noise naturally exist in any integrative intracellular decision network. To further clarify the importance of the rate of parameter sweeping, we also studied models from non-equilibrium systems theory. These are traditional in the study of phase transitions in statistical physics and stood as a fundamental tool to extrapolate key results to intracellular network dynamics. Specifically, we analysed the effects of a time-dependent asymmetry in the canonical supercritical pitchfork bifurcation model, both by numerical simulations and analytical solutions. We complemented the discussion of cell fate decision with a study of the effects of non-specific targets of drugs on the Epidermal Growth Factor Receptor pathway. Pathway output has long been correlated with qualitative cell phenotype. Cancer network multitargeting therapies were assessed in the context of whole network attractor phenotypes and the importance of parameter sweeping speed