Shaping and Dilating the Fitness Landscape for Parameter Estimation in Stochastic Biochemical Models

The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology

Modeling cell proliferation in human acute myeloid leukemia xenografts

Motivation: Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Results: In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. Availability and implementation: The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell

Computational Intelligence for Life Sciences

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Ko-Optimasi Source dan Pola Mask Berdasarkan Algoritma Multi-Objektif Particle Swarm Optimization

Studi ini mengintegrasikan algoritma multi-objektif particle swarm optimization (MOPSO) kedalam proses ko-optimasi source dan mask (SMO) untuk meningkatkan performa lithografi pada sinar ekstrim ultraviolet (EUV). Sebuah metode proses secara simultan dari source dan pola reticle dikembangkan pada riset ini. Untuk konstruksi source berbentuk bebas (freeform) , sebuah optimasi berbasis pixel digunakan pada platform PC. Algoritma MOPSO digunakan untuk menghasilkan source berbentuk bebas (Source Freform). Model berbasis pendekatan koreksi optik (Optical Proximity Correction or OPC) digunakan untuk mengoreksi pola dari mask layout. Dengan mempertimbangkan karakteristik dari sistem lithografi EUV, metode SMO dikembangkan dengan algoritma MOPSO menggunakan dua fungsi tujuan: error (EPE) dan bias horizontal/vertikal. Sebuah pola satu-dimensi line/space (L/S) digunakan sebagai informasi dasar untuk menguji Pareto dari algoritma SMO. Kemudian, pola 2D dengan half-pitch 22-nm diuji menggunakan algoritma yang sama. Algoritma MOPSO berhasil untuk mengkonstruksi solusi non-dominan (non-dominated) dari source Freeform dan Pareto. Indikator performa menunjukkan kondisi process windows (PW) seperti aerial image, exposure latitutde (EL), depth of focus (DOF) dan bias. Algoritma menunjukan bahwa PW meningkat untuk EL namun DOF menunjukkan penurunan. EL meningkat sebesar 5.26% dan DOF menurun sebesar 11.34% untuk 1D L/S. EL dan DOF meningkat 43.6% dan 18.11% untuk pola 2D. ========================================================================================================= This thesis integrates multi-objective particle swarm optimization (MOPSO) algorithm into the source and mask co - optimization (SMO) process to enhance the extreme ultraviolet (EUV) lithography imaging performance. A simultaneous source and reticle pattern process method is developed in this research. For the freeform source construction, a pixelated - based optimiz ation process was performed on PC platform. The MOPSO algorithm was applied to generate freeform source. Model - based optical proximity correction (OPC) was applied to correct the mask layout patterns. Considering the characteristics of the EUV lithography system, the developed SMO with the MOPSO algorithm is constrained by two cost functions: the edge placement error (EPE) and horizontal/vertical bias. A one - dimensional line/space (L/S) pattern is used as the baseline information to test the Pareto behavior of the developed SMO algorithm. Then, the 2D pattern with half - pitch 22 - nm was assessed using the developed algorithm. The proposed MOPSO algorithm succeeded to construct non - dominated solutions of freeform sources and Pareto front which four of those sol utions are presented. The performance indicators include process windows (PW) condition such as the aerial image contrast, exposure latitude (EL), depth of focus (DOF), and bias errors. The proposed algorithm shows that the common PW conditions improved on EL while the DOF is slightly suffering. The EL increased for 5.26% and DOF suffers for 11.34% in 1D L/S and both EL and DOF increased for 43.6% and 18.11%, respectively for the 2D pattern

Computational Modeling of Cell Signaling Dynamics: Hypothesis Management and Parameter Estimation Methods Applied to the Akt Pathway

Ph.DDOCTOR OF PHILOSOPH

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Investigation of protein-protein interactions: multibody docking, association/dissociation kinetics and macromolecular crowding

Protein-protein interactions are central to understanding how cells carry out their wide array of functions and metabolic procedures. Conventional studies on specific protein interactions focus either on details of one-to-one binding interfaces, or on large networks that require a priori knowledge of binding strengths. Moreover, specific protein interactions, occurring within a crowded macromolecular environment, which is precisely the case for interactions in a real cell, are often under-investigated. A macromolecular simulation package, called BioSimz, has been developed to perform Langevin dynamics simulations on multiple protein-protein interactions at atomic resolution, aimed at bridging the gaps between structural, kinetic and crowding studies on protein-protein interactions. Simulations on twenty-seven experimentally determined protein-protein interactions, indicated that the use of contact frequency information of proteins forming specific encounters can guide docking algorithms towards the most likely binding regions. Further evidence from eleven benchmarked protein interactions showed that the association rate constant of a complex, kon, can be estimated, with good agreement to experimental values, based on the retention time of its specific encounter. Performing these simulations with ten types of environmental protein crowders, it suggests, from the change of kon, that macromolecular crowding improves the association kinetics of slower-binding proteins, while it damps the association kinetics of fast, electrostatics-driven protein-protein interactions. It is hypothesised, based on evidence from docking, kinetics and crowding, that the dynamics of specific protein-protein encounters is vitally important in determining their association affinity. There are multiple factors by which encounter dynamics, and subsequently the kon, can be influenced, such as anchor residues, long-range forces, and environmental steering via crowders’ electrostatics and/or volume exclusion. The capacity of emulating these conditions on a common platform not only provides a holistic view of interacting dynamics, but also offers the possibility of evaluating and engineering protein-protein interactions from aspects that have never been opened before