In this talk I will overview ten years of research in the application of evolutionary computation ideas in the natural sciences. The talk will take us on a tour that will cover problems in nanoscience, e.g. controlling self-‐organizing systems, optimizing scanning probe microscopy, etc., problems arising in bioinformatics, such as predicting protein structures and their features, to challenges emerging in systems and synthetic biology. Although the algorithmic solutions involved in these problems are different from each other, at their core, they retain Darwin’s wonderful insights. I will conclude the talk by giving a personal view on why EC has been so successful and where, in my mind, the future lies.
June 2020: Top Read Articles in Control Theory and Computer Modellingijctcm
International Journal of Control Theory and Computer Modelling (IJCTCM) is a Quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Control Theory and Computer Modelling. The journal focuses on all technical and practical aspects of Control Theory and Computer Modelling. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced control engineering and modeling concepts and establishing new collaborations in these areas.
DrCell – A Software Tool for the Analysis of Cell Signals Recorded with Extra...CSCJournals
Microelectrode arrays (MEAs) have been applied for in vivo and in vitro recording and stimulation of electrogenic cells, namely neurons and cardiac myocytes, for almost four decades. Extracellular recordings using the MEA technique inflict minimum adverse effects on cells and enable long term applications such as implants in brain or heart tissue.
Hence, MEAs pose a powerful tool for studying the processes of learning and memory, investigating the pharmacological impacts of drugs and the fundamentals of the basic electrical interface between novel electrode materials and biological tissue. Yet in order to study the areas mentioned above, powerful signal processing and data analysis tools are necessary.
In this paper a novel toolbox for the offline analysis of cell signals is presented that allows a variety of parameters to be detected and analyzed. We developed an intuitive graphical user interface (GUI) that enables users to perform high quality data analysis. The presented MATLAB® based toolbox gives the opportunity to examine a multitude of parameters, such as spike and neural burst timestamps, network bursts, as well as heart beat frequency and signal propagation for cardiomyocytes, signal-to-noise ratio and many more. Additionally a spike-sorting tool is included, offering a powerful tool for cases of multiple cell recordings on a single microelectrode.
For stimulation purposes, artifacts caused by the stimulation signal can be removed from the recording, allowing the detection of field potentials as early as 5 ms after the stimulation.
June 2020: Top Read Articles in Control Theory and Computer Modellingijctcm
International Journal of Control Theory and Computer Modelling (IJCTCM) is a Quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Control Theory and Computer Modelling. The journal focuses on all technical and practical aspects of Control Theory and Computer Modelling. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced control engineering and modeling concepts and establishing new collaborations in these areas.
DrCell – A Software Tool for the Analysis of Cell Signals Recorded with Extra...CSCJournals
Microelectrode arrays (MEAs) have been applied for in vivo and in vitro recording and stimulation of electrogenic cells, namely neurons and cardiac myocytes, for almost four decades. Extracellular recordings using the MEA technique inflict minimum adverse effects on cells and enable long term applications such as implants in brain or heart tissue.
Hence, MEAs pose a powerful tool for studying the processes of learning and memory, investigating the pharmacological impacts of drugs and the fundamentals of the basic electrical interface between novel electrode materials and biological tissue. Yet in order to study the areas mentioned above, powerful signal processing and data analysis tools are necessary.
In this paper a novel toolbox for the offline analysis of cell signals is presented that allows a variety of parameters to be detected and analyzed. We developed an intuitive graphical user interface (GUI) that enables users to perform high quality data analysis. The presented MATLAB® based toolbox gives the opportunity to examine a multitude of parameters, such as spike and neural burst timestamps, network bursts, as well as heart beat frequency and signal propagation for cardiomyocytes, signal-to-noise ratio and many more. Additionally a spike-sorting tool is included, offering a powerful tool for cases of multiple cell recordings on a single microelectrode.
For stimulation purposes, artifacts caused by the stimulation signal can be removed from the recording, allowing the detection of field potentials as early as 5 ms after the stimulation.
Determination of Various Diseases in Two Most Consumed Fruits using Artificia...ijtsrd
Fruit diseases are manifested by deformations during or after harvesting the components in the fruit, when the infestation is caused by spores, fungi, insects or other contaminants. In early agricultural practices, it is thought that non destructive examination is possible with the analysis of pre harvest fruit leaves and early diagnosis of the disease, while post harvest detection and classification of fruit disease is possible by evaluating simple image processing techniques. Diseases of rotten or stained fruits without destruction. In this way, the disease will be identified and classified and the awareness of the producer for the next harvest will be provided. For this purpose, studies were carried out with apple and quince fruit, images were determined using still fruit pictures and machine learning, and disease classification was provided with labels. Image processing techniques are a system that detects disease made to a real time camera and prints it on the screen. Within the scope of this study, the data set was created and images of 22 apples and 18 quinces were taken. The image was classified by similarities in the literature review. The success of the proposed Convolutional Neural Network architecture in recognizing the disease was evaluated. By comparing the trained network, AlexNet architecture, with the proposed architecture, it has been determined that the success of image recognition has increased with the proposed method. Aysun Yilmaz Kizilboga | Atilla Ergüzen | Erdal Erdal "Determination of Various Diseases in Two Most Consumed Fruits using Artificial Neural Networks and Deep Learning Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38128.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38128/determination-of-various-diseases-in-two-most-consumed-fruits-using-artificial-neural-networks-and-deep-learning-techniques/aysun-yilmaz-kizilboga
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Evolutionary Algorithms for Self-Organising SystemsNatalio Krasnogor
Talk I gave at Ben Gurion University of the Negev in Israel on the 24rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist
Systems Biology and Genomics of Microbial PathogensRamy K. Aziz
Talk at SCITA-BIOFANS (02 Feb 2016), entitled
"Systems Biology and Genomics of Microbial Pathogens:
From virulence gene discovery to vaccine development and therapeutic intervention"
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Dr. Leroy Hood lectured to a group of Ohio State University College of Medicine students and faculty on May 13, 2010 in advance of an announcement of a partnership between the Ohio State University Medical Center and the Institute for Systems Biology. The partnership will be known as
Determination of Various Diseases in Two Most Consumed Fruits using Artificia...ijtsrd
Fruit diseases are manifested by deformations during or after harvesting the components in the fruit, when the infestation is caused by spores, fungi, insects or other contaminants. In early agricultural practices, it is thought that non destructive examination is possible with the analysis of pre harvest fruit leaves and early diagnosis of the disease, while post harvest detection and classification of fruit disease is possible by evaluating simple image processing techniques. Diseases of rotten or stained fruits without destruction. In this way, the disease will be identified and classified and the awareness of the producer for the next harvest will be provided. For this purpose, studies were carried out with apple and quince fruit, images were determined using still fruit pictures and machine learning, and disease classification was provided with labels. Image processing techniques are a system that detects disease made to a real time camera and prints it on the screen. Within the scope of this study, the data set was created and images of 22 apples and 18 quinces were taken. The image was classified by similarities in the literature review. The success of the proposed Convolutional Neural Network architecture in recognizing the disease was evaluated. By comparing the trained network, AlexNet architecture, with the proposed architecture, it has been determined that the success of image recognition has increased with the proposed method. Aysun Yilmaz Kizilboga | Atilla Ergüzen | Erdal Erdal "Determination of Various Diseases in Two Most Consumed Fruits using Artificial Neural Networks and Deep Learning Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38128.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38128/determination-of-various-diseases-in-two-most-consumed-fruits-using-artificial-neural-networks-and-deep-learning-techniques/aysun-yilmaz-kizilboga
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Evolutionary Algorithms for Self-Organising SystemsNatalio Krasnogor
Talk I gave at Ben Gurion University of the Negev in Israel on the 24rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist
Systems Biology and Genomics of Microbial PathogensRamy K. Aziz
Talk at SCITA-BIOFANS (02 Feb 2016), entitled
"Systems Biology and Genomics of Microbial Pathogens:
From virulence gene discovery to vaccine development and therapeutic intervention"
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Dr. Leroy Hood lectured to a group of Ohio State University College of Medicine students and faculty on May 13, 2010 in advance of an announcement of a partnership between the Ohio State University Medical Center and the Institute for Systems Biology. The partnership will be known as
A presentation conducted by Mr Mehrdad Amirghasemi, SMART Infrastructure Facility, University
of Wollongong.
Presented on Wednesday the 2nd of October 2013.
Modelling and simulation for improved infrastructure is involved with the development of adaptive systems that can learn and respond to the environment intelligently. Developing simple agents with limited intelligence that collectively represent complex behaviour can assist infrastructure planning and can model many real world
situations. By employing sophisticated techniques which highly support infrastructure planning and design, evolutionary computation can play a key role in the development of such systems. The key to presenting solution strategies for these systems is fitness landscape
which makes some problems hard and some problems easy to tackle. Moreover, constructive methods and local searches can assist evolutionary searches to improve
their performance. In this paper, all these four concepts are reviewed and their application in infrastructure planning and design is discussed. With respect to applications, the main emphasis includes city planning, and traffic equilibrium.
Modelled and Analysed the watershed Dynamics in Mahanadi River Basin. Finally came up with watershed Management Plan to minimise the future LUCC in Mahanadi River Basin
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
The 20th Century was transformed by the ability to program on silicon, an innovation that made possible technologies that fundamentally revolutionised how the world works. As we face global challenges in health, food production, and in powering an increasingly energy-greedy planet, it is becoming clear that the 21st Century could be equally transformed by programming an entirely different material: biological matter. The power to program biology could transform medicine, agriculture, and energy, but relies, fundamentally, on an understanding of biochemistry as molecular machinery in the service of biological information-processing. Unlike engineered systems, however, living cells self-generate, self-organise, and self-repair, they undertake massively parallel operations with slow and noisy components in a noisy environment, they sense and actuate at molecular scales, and most intriguingly, they blur the line between software and hardware. Understanding this biological computation presents a huge challenge to the scientific community. Yet the ultimate destination and prize at the culmination of this scientific journey is the promise of revolutionary and transformative technology: the rational design and implementation of biological function, or more succinctly, the ability to program life.
Unsupervised Deconvolution Neural Network for High Quality Ultrasound ImagingShujaat Khan
High quality US imaging demand large number of measurements that can increase the cost, size and power requirements. Therefore, low-powered, portable and 3D ultrasound imaging system require reconstruction algorithms that can produce high quality images using fewer receive measurements. Number of model specific methods has been proposed which doesn't work under perturbation. For instance, compressive deconvolution ultrasound which provide a reasonable quality with limited measurements however, it has its own down-sides such as high computation cost and accurate estimation of point spread function (PSF). An other major limitation of conventional methods is that they require RF or base-band signal which is difficult to obtain from portable US systems. To deal with the aforementioned issues, in this study we designed a novel deep deconvolution model for image domain-based deconvolution. The proposed deep deconvolution (DeepDeconv) model can be trained in an unsupervised fashion, alleviate the need of paired high and low quality images. The model was evaluated on both the phantom and in-vivo scans for various sampling configurations. The proposed DeepDeconv significantly enhance the details of anatomical structures and using unsupervised learning on average it achieved 2.14dB, 4.96dB and 0.01 units gain in CR, PSNR and SSIM values respectively, which are comparable to the supervised method.
AI&BigData Lab 2016. Артем Чернодуб: Обучение глубоких, очень глубоких и реку...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
В докладе представлен обзор новых подходов к обучению глубоких и рекуррентных нейросетей. Обсуждаются ортогональная инициализация весов для сверточных и рекуррентных нейросетей и ее влияние на проблему исчезновения градиентов (vanishing gradient effect), нормализацию мини-пакетов (batch normalization), разностное обучение (residual learning).
Understanding Protein Function on a Genome-scale through the Analysis of Molecular Networks
Cornell Medical School, Physiology, Biophysics and Systems Biology (PBSB) graduate program, 2009.01.26, 16:00-17:00; [I:CORNELL-PBSB] (Long networks talk, incl. the following topics: why networks w. amsci*, funnygene*, net. prediction intro, memint*, tse*, essen*, sandy*, metagenomics*, netpossel*, tyna*+ topnet*, & pubnet* . Fits easily into 60’ w. 10’ questions. PPT works on mac & PC and has many photos w. EXIF tag kwcornellpbsb .)
Date Given: 01/26/2009
An Evolutionary-based Neural Network for Distinguishing between Genuine and P...Md Rakibul Hasan
I presented this paper (https://doi.org/10.5220/0010985100003116) at 14th International Conference on Agents and Artificial Intelligence. It analyzes observers’ pupillary responses by developing neural network (NN) models to distinguish between genuine and posed anger.
Presentation on machine learning and materials science at Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Wisconsin, Madison, December 4, 2018
TOP 1 CITED PAPER - International Journal of Artificial Intelligence & Appli...gerogepatton
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Then, it is employed for training feedforward neural networks for two benchmark classification problems. Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search. Simulation results demonstrate the effectiveness of the proposed algorithm.
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...Chris Rackauckas
The combination of scientific models into deep learning structures, commonly referred to as scientific machine learning (SciML), has made great strides in the last few years in incorporating models such as ODEs and PDEs into deep learning through differentiable simulation. However, the vast space of scientific simulation also includes models like jump diffusions, agent-based models, and more. Is SciML constrained to the simple continuous cases or is there a way to generalize to more advanced model forms? This talk will dive into the mathematical aspects of generalizing differentiable simulation to discuss cases like chaotic simulations, differentiating stochastic simulations like particle filters and agent-based models, and solving inverse problems of Bayesian inverse problems (i.e. differentiation of Markov Chain Monte Carlo methods). We will then discuss the evolving numerical stability issues, implementation issues, and other interesting mathematical tidbits that are coming to light as these differentiable programming capabilities are being adopted.
Bio: Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.
Top Cited Article in Informatics Engineering Research: October 2020ieijjournal
Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.
Biological Apps: Rapidly Converging Technologies for Living Information Proce...Natalio Krasnogor
This is a plenary talk I gave at the 2018 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems in Cadiz, Spain
Plenary Speaker slides at the 2016 International Workshop on Biodesign Automa...Natalio Krasnogor
In this talk I discuss recent work done in my lab and with collaborators abroad that contributes towards accelerating the specify -> design -> model -> build -> test & iterate biological engineering cycle. This will describe advances in biological programming languages for specifying combinatorial DNA libraries, the utilisation of off-the-shelf microfluidic devices to build the DNA libraries as well as data analysis techniques to accelerate computational simulations
Memetic Algorithms have become one of the key methodologies behind solvers that are capable of tackling very large, real-world, optimisation problems. They are being actively investigated in research institutions as well as broadly applied in industry. In this talk we provide a pragmatic guide on the key design issues underpinning Memetic Algorithms (MA) engineering. We begin with a brief contextual introduction to Memetic Algorithms and then move on to define a Pattern Language for MAs. For each pattern, an associated design issue is tackled and illustrated with examples from the literature. We then fast forward to the future and mention what, in our mind, are the key challenges that scientistis and practitioner will need to face if Memetic Algorithms are to remain a relevant technology in the next 20 years.
These slides were used for a tutorial I gave at GECCO 2010. These are similar, yet not identical, to the other tutorials. The keynote file is too large for slideshare but if anybody needs the original I would be happy to provide a url from where to download it.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
A Genetic Programming Challenge: Evolving the Energy Function for Protein Str...Natalio Krasnogor
In this talk I introduce a computational challenge for GP researchers, namely, the automated synthesis of energy functions for protein structure prediction.
Building Executable Biology Models for Synthetic BiologyNatalio Krasnogor
The leveraging of today's unprecedented capability to manipulate biological systems by state-of-the-art computational, mathematical and engineering techniques , may profoundly affect the way we approach the solution to pressing grand challenges such as the development of sustainable green energy, next generation healthcare, etc. The conceptual cornerstone of Synthetic Biology a field very much on its infancy- is that methodologies commonly used to design and construct non-biological artefacts (e.g. computer programs, airplanes, bridges, etc) might also be mastered to create designer living entities. Computational methods for modeling in Synthetic Biology consist of a list of instructions detailing an algorithm that can be executed and whose computation resembles the behavior of the biological system under study. This computational approach to modelling biological systems has been termed executable biology. In this talk I will describe current approaches for the automated generation and testing of executable biology models for synthetic biology.
This was a colloquioum talk at the Computer Science Department, Ben-Gurion University of the Negev, Israel (30/June/2009)
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics and Systems & Synthetic Biology
1. Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics, Systems & Synthetic Biology Prof. Natalio Krasnogor Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, University of Nottingham www.cs.nott.ac.uk/~nxk twitter.com/NKrasnogor Page 1 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
2. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 2 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
3. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 3 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
4. Darwin’s Magic Page 4 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011 Thank you Youtube
5. Algorithmic Beauty Inheritable Instructions Set Limited Resources Imperfect Replication A Powerful Secondary Effect: Selection An awe inspiring product: Evolution by Natural Selection Page 5 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
6. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 6 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
7. Evolutionary Computation in the Natural Sciences Programmable algorithmic entry to the vast world of nanoscale physical, chemical & biological systems and processes Algorithmic and Artificial Living Matter (ALMA) A Research Vision How (?) do you gain algorithmic entry into Embedded behavior Information & Algorithms Complexity Robustness Tradeoffs Computer Science How does “The Logistics of Small Things” look like? Page 7 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
8. The Spatial Scales Involved Page 8 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
9. ALMA & The Logistics of Small Things How do you program complex nano/micro scale process : through billions of tiny & simple distributed programs/processors? when there is no clear distinction between hardware and software? when the wetware is not simply a stochastic program: when wetware is poorly characterised and is likely to evolve, etc. function f1(p1,p2,p3,p4) { if (p1<p2) and (rand<0.5) print p3 else print p4 } function f1(p1,p2,p3,p4) { if (p1<p2) RND print p3 RND else RND print p4 RND } function f1(p1,p2,p3,p4) { if (p1<p2) RND print p3 RND else RND print p4 RND } function f1(p1,p2,p3,p4) { if (p1<p2) RND incr p3 RND else RND decr p4 RND } Page 9 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
10. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 10 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
11. The Spatial Scales Involved Page 11 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
12. Molecular Tiles & Programmable Self-Assembly Algorithmic Self-Assembly of DNA Sierpinski Triangles. P.W.K. Rothemund, N. Papadakis, E. Winfree. PLoS Biology 2:12 (2004) Page 12 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
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14. Tiles with deterministic assembly (Model 1) Tiles with probabilistic assembly (Model 2) Page 14 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
15. Evolutionary Design Approach Variable length individuals (Genotype) Genotype -Phenotype Mapping Randomly created Wang tiles One-point crossover Phenotype Bitwise mutation Phenotype – Fitness Mapping Minkowski functionals (A, P, X) A = 12 P = 24 X = 0 A = 100 P = 40 X = 1 Population size = 100, Individuals length = [1,10], Generations = 300, Pcrossover= 0.7, PMutation= 0.1/0.05/0.01 Vs Page 15 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
16. Probabilistic Assembly + No Rotation Probabilistic Assembly + Rotation Deterministic Assembly + Rotation Deterministic Assembly + No Rotation Page 16 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
17. How Does Self-Assembly Gets Programmed? Two-tile self-assembly Three-tile self-assembly Four-tile self-assembly Five-tile self-assembly We calculated the equivalence classes of binding pockets defined by “bp1 R bp2 iif NAFE(bp1)=NAFE(bp2)” for the best tile set. We observed thatequivalence classes with NAFE smaller than T are highly likely to participate in the self-assembly process as these are more populous. More “assembable” binding pockets = Generalised Secondary Structures Page 17 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
20. Motion without interactionDiffusion across terraces on the substrate Intramolecule strength: energy between two no-functionalised porphyrins Molecule-substrate strength: energy of a porphyrin to the substrate Rotational strength: molecule-substrate strength for spinning Page 18 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
21. How Do You Image and Manipulate at This Scale? D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990) C60 Y. Sugimoto et al., Nature letters 446, 64 (2007). Hlaet al. Phys. Rev. Lett. 85, 2777–2780 (2000) D.L. Keeling et al. Phys. Rev. Lett 94, 146104 (2005) Page 19 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
22. Scanning tip Z A X Y Sample surface Axis under direct (piezo) control Even 3 Variable Problems are Difficult: Optimising a Scanning Probe Microscopy it ∝ exp(−2kd) i G http://www2.fz-juelich.de/ibn/index.php?index=1021 V The tunnel current it is highly dependant on the tip-sample distance, d. This current can be maintained with a feedback loop, G, that actively controls the tip-sample distance. Page 20 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
23. Understanding the image J. H. A. Hagelaaret al. PRB 78, 161405R 2008 L.Gross et al. Science 325 1110 (2009) Page 21 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
24. (Un)Stable and (Un)defined Tip States Imaging problems, spontaneous tip changes Page 22 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
25. Two Stage Automation Process Automated probe microscopy via evolutionary optimisation at the atomic scale. R. Woolley, J. Sterling, A. Radocea, N. Krasnogor and P. Moriarty. Applied Physical Letters (to appear) Cellular GA with Smart Initialisation In-situ Ex-situ Voltage pulsing (deliberate crash) Fine tuning (changing scan parameters) Page 23 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
26. Stage 1: Smart Initialisation (coarsely) Conditions the Probe Streaky Image. Executing cleaning pulse A deterministic approach Cloudy Image. Executing cleaning pulse Flat Surface. Zooming in to 50nm Flat Surface. Zooming in to 20nm Constant Atomic resolution. Zooming in to 4nm Poor Atomic resolution. Rescanning Consistent fair atomic resolution. Stage 1 complete. Time elapsed: 1010.1902 (~17mins) Page 24 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
27. G V i G G G V V V i i i Stage 2: Fine adjustment with CGA Starting image Cellular GA G V i Machine Optimised Page 25 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
28.
29.
30. How Does it Compares to an Expert Operator? Page 27 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
31. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 28 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
32. The Spatial Scales Involved Page 29 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
33. Protein Folding & Structure Prediction Anfinsen’s thermodynamic hypothesis [Anfinsen 1973, Dill and Chan 1997] Primary Sequence 3D Structure Protein Structure Prediction (PSP) aims to predict the 3D structure of a protein based on its primary sequence (perhaps disregarding the folding process) Page 30 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
34. Defining and Predicting Useful Features M. Stout, J. Bacardit, J. Hirst & N. Krasnogor, Bioinformatics 2008 24(7):916-923. Contact M. Stout, J. Bacardit, J.D. Hirst, R.E Smith, and N. Krasnogor. Prediction of topological contacts in proteins using learning classifier systems. Journal Soft Computing - A Fusion of Foundations, Methodologies and Applications, 13(3):245-258, 2008. Page 31 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
38. Coordination NumberIntegration of all these predictions plus other sources of information Final CM prediction (using BioHEL) Using BioHEL Page 32 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
39. The BioHEL GBML System BIOinformatics-oriented Hiearchical Evolutionary Learning – BioHEL(Bacardit & Krasnogor, 2009) BioHEL is a rule-based evolutionary learning system that employs the Iterative Rule Learning (IRL) paradigm First used in EC in Venturini’s SIA system (Venturini, 1993) Widely used for both Fuzzy and non-fuzzy evolutionary learning J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llora, and N. Krasnogor. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. Proceedings of the 2007 Genetic and Evolutionary Computation Conference, ACM Press, 2007. J. Bacardit, M. Stout, J.D. Hirst, A. Valencia, R.E. Smith, and N. Krasnogor. Automated alphabet reduction for protein datasets. BMC Bioinformatics, 10(6), 2009. Bronze Medal in the THE 2007 “HUMIES” AWARDS FOR HUMAN-COMPETITIVE RESULTS PRODUCED BY GENETIC AND EVOLUTIONARY COMPUTATION. Page 33 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
40.
41. We predict them from the closest neighbours in the chainRi SSi Ri-1 SSi-1 Ri+2 SSi+2 Ri-2 SSi-2 Ri+3 SSi+3 Ri+4 SSi+4 Ri-3 SSi-3 Ri-4 SSi-4 Ri-5 SSi-5 Ri+5 SSi+5 Ri+1 SSi+1 Ri-1 Ri Ri+1 SSi Ri Ri+1 Ri+2 SSi+1 Ri+1 Ri+2 Ri+3 SSi+2 Page 34 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
51. Whole training process takes about 289 CPU days (~5.5h/rule set)x50 Samples x25 Rule sets Consensus Predictions Page 36 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
55. 140 server groupsPage 37 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
56. Contact Map prediction in CASP 7 Accuracy for groups that predicted a common subset of targets Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209 Page 38 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
57. Xd results Contact Map prediction in CASP 7 Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209 Page 39 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
58.
59. 67% accuracyEzkudia et al. Proteins 2009; 77(Suppl 9):196-209 Page 40 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
60.
61. Set of 3262 proteins for training all the 1D predictors
62. A subset of 2413 proteins used for CM prediction
67. 25K CPU hours were employed just to train the CM ensemblePage 41 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
68. In terms of performance These two groups derived contact predictions from 3D models Page 42 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
73. 10 000 CPU days for 10μs of folding[Dill and Chan 1997] P. Widera, J.M. Garibaldi, J., and N. Krasnogor,. Evolutionary design of the energy function for protein structure prediction, Proceedings of the IEEE Congress on Evolutionary Computation 2009. P. Widera, J. Garibaldi, and N. Krasnogor. GP challenge: evolving the energy function for protein structure prediction. Journal of Genetic Programming and Evolvable Machines, 11:61-88, 1 2010. Gold Medal in the THE 2010 “HUMIES” AWARDS FOR HUMAN-COMPETITIVE RESULTS PRODUCED BY GENETIC AND EVOLUTIONARY COMPUTATION Page 43 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
88. total of 150 CPU daysPage 48 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
89. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 49 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
90. The Spatial Scales Involved Page 50 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
91. The Cell as an Information Processing Device LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007) Page 51 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
92. Transcription Networks Environment Signal2 Signal5 Signal1 Signal3 Signal4 Signaln ... Transcription Factors Genome Gene1 Gene2 Gene3 Genek Page 52 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
93. Network Motifs: Evolution’s Preferred Circuits Biological networks are complex and vast Moreover, these patterns are organised in non-trivial/non-random hierarchies “Patterns that occur in the real network significantly more often than in randomized networks are called network motifs” Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) RaduDobrin et al., Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics. 2004; 5: 10. The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector. Each network motif carries out a specific information-processing function The I1-FFL is a pulse generator and response accelerator Page 53 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
95. Nested EA for Model Synthesis F. Romero-Campero, H.Cao, M. Camara, and N. Krasnogor. Structure and parameter estimation for cell systems biology models. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), pages 331-338. ACM Publisher, 2008. Best Paper Award H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009 Page 55 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
96.
97. Different time series have very different profiles, e.g., response time or maxima occur at different times/places
100. Sometimes the time series is qualitative or microarray dataH. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009 Page 56 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
102. Target Page 58 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
103. A Signal Translatorfor Pattern Formation FP2 FP1 act1 act2 rep1 rep2 rep3 rep4 I2 I1 Pact1 Prep3 Prep2 Pact1 Prep1 Pact2 Prep2 Prep4 Prep1 Pact2 Page 59 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
104. Uniform Spatial Distribution of Signal Translators for Pattern Formation pBR322 pACYC184 E. coli DH5α ∆sdiA/∆lacI (2∆) Page 60 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
105. Pattern Formation in synthetic bacterial colonies Page 61 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
106. pAYCP (1-3) pBR322 (4-6) Starting OD=10 2∆ DH5α Magnification: 100X Page 62 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
107. pUC6S (1-6) Starting OD= 10 Magnification: 40X 2∆ DH5α Page 63 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
108.
109. Algorithms are Tiny Factoring: Let n be the number to be factored. 1. Let Δ be a negative integer with Δ = -dn where d is a multiplier and Δ is the negative discriminant of some quadratic form. 2. Take the t first primes , for some . 3. Let fq be a random prime form of GΔ with . 4. Find a generating set X of GΔ 5. Collect a sequence of relations between set X and {fq : q ∈ PΔ} satisfying: 6. Construct an ambiguous form (a, b, c) which is an element f ∈ GΔ of order dividing 2 to obtain a coprime factorization of the largest odd divisor of Δ in which Δ = -4a.c or a(a - 4c) or (b - 2a).(b + 2a) 7. If the ambiguous form provides a factorization of n then stop, otherwise find another ambiguous form until the factorization of n is found. In order to prevent that useless ambiguous forms are generated, build up the 2-Sylow group S2(Δ) of G(Δ). Calculating Pi Page 65 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
110.
111. They are not short pieces of code, but large systemsPage 66 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
112. What are Evolutionary Algorithms? Research Paradigms for Problem Solving T.S. Kuhn. The Structure of Scientific Revolutions, 1962. Design Patterns and Pattern Languages C. Alexander, S. Ishikawa, M. Silverstein, M. Jacobson, I. Fiksdahl-King, S. Angel, S.: A Pattern Language - Towns, Buildings, Construction. Oxford University Press (1977) N. Krasnogor and J.E. Smith.IEEE Transactions on Evolutionary Computation, 9(5):474- 488, 2005. Page 67 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
113. Invariants and Decorations A Compact “Memetic” Algorithm by Merz (2003) Page 68 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
114. Invariants and Decorations A “Memetic” Particles Swarm Optimisation by Petalas et al (2007) Page 69 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
115. Invariants and Decorations A “Memetic” Artificial Immune System by Yanga et al (2008) Page 70 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
116. Invariants and Decorations A “Memetic” Learning Classifier System by Bacardit & Krasnogor (2009) Page 71 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
117. Invariants and Decorations Many others based on Ant Colony Optimisation, NN, Tabu Search, SA, DE, etc. Key Invariants: Global search mode Local search mode Many Decorations, e.g.: Crossover/Mutations (EAs based MAs) Pheromones updates (ACO based MAs) Clonal selection/Hypermutations (AIS based MAs) etc Page 72 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
118. A Pattern Language for Memetic AlgorithmsMemetic Algorithms by N. Krasnogor. Handbook of Natural Computation (chapter) in Natural Computing. Springer Berlin / Heidelberg, 2009. www.cs.nott.ac.uk/~nxk/publications.html Page 73 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
119. solving 1 problem – single instances Solving multiple unrelated problem – several classes instances Programming Programming solving 1 problem – several instances (self) adaptive Programming Solving a few problem – several classes instances (self) adaptive Self-generating Programming (self) adaptive Self-generating Self-Engineering Reuse Reuse Feedback Reuse Feedback A General Trend: moving away from close-loop optimisation towards open-ended and embodied optimisation Effort (e.g. Time, $$$, etc) Effort (e.g. Time, $$$, etc) Effort (e.g. Time, $$$, etc) Effort (e.g. Time, $$$, etc) Page 74 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
120. The Future of EAsSoftware Nurseries Fundamental Change of Temporal Scales Rethink Software will be “seeded” and grown, very much like a plant or animal (including humans) Software will start in an “embryonic” state and develop when situated on a production environment What would a software “incubation” machine look like? What would a software “nursery” look like? Page 75 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
121. DNA/RNA Cells Individual Organs Tissue Specialised Function Potential To Develop into multiple different types of cells Ultimate Solver Commitment Page 76 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
122. Production Environment Input SC SC SC SC SC SC Software Cell TSP Organ Euclidean TSP Organ GraphicalTSP Organ TSP Solver Software Organism Pluripotential Solver “DNA” Page 77 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
123. TSP Solver Software Organism Protein Structure Prediction Solver Software Organism Vehicle Routing Solver Software Organism Graph Isomorphism Solver Software Organism SAT Solver Software Organism Bin Packing Solver Software Organism Graph Coloring Solver Software Organism Network Interdiction Solver Software Organism Quadratic Assignment Solver Software Organism An Ecosystem of solvers Page 78 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
124. As, e.g., Biologists & Physicists have done through an ubiquitous, worldwide spanning informatics infrastructure, we should be focusing on building an online worldwide computational problem solving infrastructure Page 79 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
125.
126. Conclusions New types of executable structures In Nanotechnology DNA tiles, DNA origami, etc Non DNA based tiles Some have very definite programmable features Others require the program to be “distributed” and exploit noise and randomness In Synthetic Biology How to orchestrate activities at multiple temporal-spatial-energetic scales? How to cope with noise in the background that execures a program and in the program itself?! How to cope for programs that will evolve? Page 81 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
127. Conclusions New types of benchmarks Structural Biology (PSP and GP4PSP) Many of these problems can be modelled both as regression or classification problems Low/high number of classes Balanced/unbalanced classes Adjustable number of attributes Ideal benchmarks !! Scanning Probe Microscopy: Even a few dimensions are hard “Chameleons” as it is sampled http://www.infobiotic.net/ Page 82 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
128.
129. Requires strong links with data mining, ALIFE and, of course, AI (beyond existing trends in constraint satisfaction), search based software engineering (beyond current trends on testing/debugging)
130. Requires on-line, computer friendly ontologies of code (e.g the pattern language in the left), self-describing source code, protocols for autonomic code reuse, etcPage 83 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
131. Conclusions Learn From Physics, Chemistry & Biology The Invariants & Patterns,the Decorations are superfluous Evolution Self-Assembly & Self-Organisation Developmental systems Depend on a core genome coding for essential functionality Epigenomicscanalises development Hierarchical control systems that modify programs including susceptibility to horizontal gene (program libraries) transfer Infrastructure Missing Components Missing Components Page 84 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
141. Colleagues at ASAPJ. Chaplin J. Blakes E. Glaab M. Franco Page 85 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
142. Page 86 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011
Editor's Notes
The Mother of all Reverse Engineering StoriesDarwin (and Wallace) got to the core of the issue by clearly separating the important invariants from the accidental decorations
Embedded behaviour (sensors, processors, actuators, simbolic carriers, universal architectures)Information and Algorithms (combinatorial specification, data structures, programming languages, compilers)Complexity (moore laws, abstration and hierarchies)Robustness (Digital signal restoration, fault tolerance & error correction, standardised interfaces, protocols, composition, rigorous proofs)Tradeoffs: optimised performance VS scalability VS cost VS designibility
AFM Images of DAO-E Crystals(A) A large templated crystal in a 5-tile reaction (no R-11). A single ‘1’ in the input row (asterisk) initiates a Sierpinski triangle, which subsequently devolves due to errors. Mismatch errors within ‘0’ domains initiate isolated Sierpinski patterns terminated by additional errors at their corners.(B) A large untemplated fragment in a 5- tile reaction (no S-11). Large triangles of ‘0’s can be seen. Crystals similar to this are also seen in samples lacking the nucleating structure.(C) Several large crystals in a 6-tile reaction, some with more zeros than ones, some with more ones than zeros. It is difficult to determine whether these crystals are templated or not.(D) An average of several scans of the boxed region from (C), containing roughly 1,000 tiles and 45 errors. (E) An average of several scans of a Sierpinski triangle that initiated by a single error in a sea of zeros and terminated by three further errors (a 1% error rate for the 400 tiles here). Red crosses in (D) and (E) indicate tiles that have been identified (by eye) to be incorrect with respect to the two tiles from which they receive their input. Scale bars are 100 nm.DOI: 10.1371/journal.pbio.0020424.g006
Lesson 1:Evolution can work around stochasticity and noise. Not only that, these ENHANCES evolution! And this has been found to be true in living systems, namely, noiseand stochasticity provide robustness and tactical maneuvering (i.e. populations do not comit deterministically to a course of action hence they hedge their bets).Lesson 2:These results were robust with a large range of Glue strength matrices! That is, evolution was able to find which building blocks were useful, i.e., you do not need a specific ideal starting condition
Lesson 3: Thus evolution “tunes” the degree of cooperativity (i.e. NAFE) and how many of these to use.Lesson 4: GSS posed at the “edge” of the freezing threshold can build stuff but also correct errors!
Porphyrin (NO2) molecules on Au(110) surfaceMolecular structures along the step edges of Au(110) Close-packed islands and one dimensional structuresTwo Optimisation Problems:[1] optimal imaging[2] reverse engineering simulation parameters from iamges
Two residues of a chain are said to be in contact if their distance is less than a certain thresholdThe contacts of a protein can be represented by a binary matrix. 1 = contact 0 = non contactPlotting this matrix reveals many characteristics from the protein structureCM prediction is used in many 3D PSP methods (e.g. I-Tasser)We model a protein as a series of nested layers, assigning each residue to a different layerStrictly speaking each layer is a convex hull of pointsThe convex hull of a point set is simple and fast to compute & parameter-lessRecursive Convex Hull is computed by iteratively identifying the layers (hulls) of a proteinRemove edges from DT if a sphere drawn between two vertices contains another vertexGabriel Graph (GG)Remove edges from GG if an spherical lune contains another vertex Relative Neighbourhood Graph (RNG)
BIOinformatics-oriented Hiearchical Evolutionary Learning – BioHEL (Bacardit et al., 2009)BioHEL is a rule-based evolutionary learning system that employs the Iterative Rule Learning (IRL) paradigmFirst used in EC in Venturini’s SIA system (Venturini, 1993)Widely used for both Fuzzy and non-fuzzy evolutionary learningBioHEL inherits most of its components from GAssist [Bacardit, 04], a Pittsburgh evolutionary learning system
We selected a set of 2811 protein chains from PDB-REPRDB with:A resolution less than 2ÅLess than 30% sequence identifyWithout chain breaks nor non-standard residues90% of this set was used for training (~490000 residues)10% for test All three features were predicted based on a window of ±4 residues around the targetEvolutionary information (as a Position-Specific Scoring Matrix) is the basis of this local informationEach residue characterised by a vector of 180 valuesThe domain for all three features was partitioned into 5 states
Contact Map is assessed using the 11 CASP targets in the Free Modelling category Also, only long-range contacts (with a minimum chain separation of 24 residues) are evaluatedPredictor groups are asked to submit a list of predicted contacts and a confidence level for each predictionThe assessors then rank the predictions for each protein and take a look at the top L/x ones, where L is the length of the protein and x={5,10}From these L/x top ranked contacts two measures are computedAccuracy: TP/(TP+FP)Xd: difference between the distribution of predicted distances and a random distribution22 groups participated in casp8, but not all of them sent enough predictions for L/10 or L/5
Basic goal: this help highlighting gaps in understandingIntermediate goal: a detailed model would allow for the verification that their understanding is consistent with the available evidenceAdvanced goal: once you have succesfully done the two above what you really want is to be able to use your models to go beyond what you currently know bothTheoretically and biologically by conduction “in silicon biology” thus saving time, money, ethical considerations (as you can kill as many virtual mice as you want) and allowing you to have unprecendented control on the experimental (virtual) conditions. That is, in silicon you can do “what if?” testing. Predictive modelling might allow you to uncover unsuspected interactions and effects between model components, which perhaps are difficult to obtain by other routes.Dream goal of Synthetic Biology: to combinatorially combine in silico well-understood components/models for the design and generation of novel experiments and hypothesis and ultimatelyto design, program, optimise & control (new) biological systems to compile the design into biological matter.
To understand their functionality in a scalable way one must choose the correct abstractionCellular functions arise from orchestrated interactions between motifs consisting of many molecular interacting species.
A P System model is a set of rules representing molecular interactions motifs that appear in many cellular systems.The main idea is to use a nested evolutionary algorithm where the first layer evolves model structures while the inner layer acts as a local search for the parameters of the model. It uses stochastic P systems as a computational, modular and discrete-stochastic modelling framework. It adopts an incremental methodology, namely starting from very simple P system modules specifying basic molecular interactions, more complicated modules are produced to model more complex molecular systems.Successfully validated evolved models can then be added to the models library
Key missing link in all work in SB: in silico simulations do not take into account in any realistic way evolutionary activity!No accounting of evolutionThe ultimate automated programming challenge
The are a Research Paradigm as the provide a framework from where to ask and answer research questions
There is an obsession with algorithms, but what about systems?!?