We will introduce the BioCAD framework that we have developed to analyse, optimise and re-design biological models. The framework includes 1) Multi-Objective Optimisation, 2) Sensitivity, 3) Identifiability and 4) Robustness analyses.
More specifically, we will present single- and multi-objective optimization algorithms able to handle genetic strategies or uptake rates in a given model. We will show that the condition of Pareto optimality can be relaxed (e.g., epsilon-dominance) to include suboptimal points that can be used to boost the algorithm in its convergence process.
The Sensitivity Analysis (SA) is used to compute an index for each parameter that indicates its influence in the model. The Identifiability Analysis (IA) detects functional relations among decision variables through a statistical analysis on the values after and before the optimisation. The Robustness Analysis (RA), Local, Global and Glocal robustness, proves useful to assess the fragileness and robustness of the Pareto optimal solution (or of a given feasible solution) as a result of a perturbation occurring in the model.
Our methodology is suitable for (i) any model consisting of ordinary differential equations, differential algebraic equations, flux balance analysis and gene-protein reaction mappings and for (ii) any simulator (e.g., SBML, MatLab, NEURON, C/C++ program). In the tutorial, we will show how these techniques offer avenues to systematically explore, analyse, optimise, design and cross-compare biological models (e.g., metabolic models, gene regulatory networks).
We have developed a graph grammar based formalism to model chemical transformations. Within our formalism molecules are treated as vertex and edge labeled graphs and reactions (be- tween molecules) are handled as graph rewrite. This approach nicely captures the algebraic properties of real chemistry, where novel molecules can be produced during chemical reactions.
Graph grammars, i.e. a set of reaction rules and starting molecules, are very compact representations of entire chemical space. These spaces can contain interesting chemical transformation patterns such as auto-catalytic sub-networks, or alternative routes to molecules of interest. Such sub-networks are usually hard to find due to the vastness of chemical spaces. The situation is especially bad in the origin of life realm, where several putative prebiotic chemistries, all combinatorial complex in nature, have been suggested. Efficient computational methods for constructing and exploring chemical spaces are therefore essential to explore alternative scenarios, or to shade light on potential chemical processes which could have resulted in the emergence of life.
The tutorial will offer a mix between short background presentations and accompanying practical examples. To ensure that attendees have the right libraries and programs available, we will provide a working environment. The attendees will learn (i) how to translate chemical reactions to graph rewrite rules, (ii) various methods to explicitly construct chemical spaces (iii) query the chemical space for interesting sub-networks.
In this tutorial we will illustrate FARSA, an open-source tool available from http://laral.istc.cnr.it/farsa/, that allows to carry on research on Adaptive Robotics.
Farsa allows to simulate different robotic platforms (the iCub humanoid robot, and the Khepera, e-Puck, and marXbot wheeled robots), design the sensorimotor system of the robots, design the environment in which the robots operate, perform collective experiments with many interacting robots, design the robots’ neural controllers, and allow the robots to develop their behavioural skills through an evolutionary or learning process.
It is a cross-platform framework, that works on Linux, Windows and Mac on both 32bit and 64bit systems, constituted by a collection of integrated open-source object-oriented C++ libraries.
The framework comes with a powerful graphical application that allow to create and run a large variety of experiments and to analyse and test the obtained results. Furthermore, FARSA has a plugin mechanism that allow to add new features (new robots, new motors, new neural networks, new learning algorithms, etc) that are integrated and accessible by the graphic interface without modifying and recompiling the core code.
FARSA is well documented, easy to use and comes with a series of exemplificative experiments that allow users to quickly gain a comprehension of the tool and a base for running a large spectrum of new experiments that can be set up simply by changing the available parameters.
The aim of the tutorial is that to allow also non-technical user to quickly acquire the knowledge required to use the tool and personalize it to specific research interests.
Application of Next Generation Sequencing (NGS) in cancer research is becoming routine in laboratories all over the world and new applications of NGS are being developed at increasing speed.
The generation, analysis, interpretation, and storage of NGS data poses a number of technical challenges.
Here, the computational infrastructure and the analysis pipelines used at the Center of Genomic Science in Milan (Italian Institute of Technology) are described.
In the second part, meta-analysis approaches facilitating the interpretation of NGS data are being discussed.
In particular, we will highlight international efforts in cancer genomics aimed at collecting genomic data (e.g. somatic mutations, gene expression, epigenetic modifications, copy number variation) from cancer samples
and correlating these data with clinical parameters with the aim of identifying novel biomarkers of cancer subtypes and eventually novel targets for therapeutic intervention.
The joined analysis of genomic data of various kinds is a field of active research that is often referred to as Integromics.
We will provide an overview of the current state of the art and illustrate the use of selected novel bioinformtaic resources of general interest.
This tutorial will introduce PyCX, an online repository of sample codes, all written in plain Python, of various complex systems simulation, including iterative maps, cellular automata, dynamical networks and agent-based models. These sample codes are designed as educational materials so that students can gain practical skills for both complex systems simulation and computer programming simultaneously. The target audience of this tutorial will primarily be educators and researchers who teach complex systems-related courses and thus need simple, easy-to-understand examples of complex systems simulation. The tutorial will also be helpful for students who want to learn basics of writing complex systems simulation themselves. Prior knowledge of Python is helpful but not required. Participants should bring their own laptops to the tutorial so they can work on hands-on coding activities.
CASM open-access article about PyCX