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    September 16-18, 2024
    Dipartimento di Informatica, Pisa, Italy

    CMSB 2024 solicits original research articles, tool papers, posters, and highlight talks on the modelling and analysis of biological systems and networks, as well as the analysis of biological data. The conference brings together researchers from across biological, mathematical, computational, and physical sciences who are interested in the modelling, simulation, analysis, inference, design, and control of biological systems. It covers the broad field of computational methods and tools in systems and synthetic biology and their applications.


    Topics of interest include, but are not limited to:
    • Formalisms for modelling biological processes
    • Methods and tools for biological system analysis, modelling and simulation
    • Frameworks for model verification, validation, analysis, and simulation of biological systems
    • High-performance methods for computational systems biology
    • Identification of biological systems
    • Applications of machine learning and data analytics in biology
    • Network modelling, analysis, inference
    • Automated parameter and model synthesis
    • Model integration and biological databases
    • Multi-scale modelling and analysis methods
    • Design, analysis, and verification methods for synthetic biology
    • Methods for biomolecular computing and engineered molecular devices
    • Data-based approaches for systems and synthetic biology
    • Optimality and control of biological systems
    • Modelling, analysis and control of microbial communities
    The CMSB 2024 proceedings will be published in the Springer LNCS/LNBI series and indexed by ISI Web of Science, Scopus, ACM Digital Library, DBLP, and Google Scholar.

    Conference organizers:

    Roberta Gori, University of Pisa (Italy)
    Paolo Milazzo, University of Pisa (Italy)
    Mirco Tribastone, IMT School for Advanced Studies Lucca (Italy)

    Invited speakers:

    Juliana Bowles, University of St Andrews, (UK)
    Madalena Chaves, INRIA, Centre Inria d'Université Côte d'Azur (France)
    Karoline Faust, KU Leuven (Belgium)
    Corrado Priami, University of Pisa (Italy)


    Abstract submission (regular/tool papers): April 14, 2024
    Paper submission (regular/tool papers): April 21, 2024
    Notification: June 9, 2024
    Camera ready: June 23, 2024
    Poster/highlight talk: July 9, 2024
    Conference: September 16-18, 2024


    All questions about the conference should be emailed to cmsb2024[at]

    March 4-8, 2024
    Live online (synchronous), max 18 participants
    Sessions from Monday to Friday, 13:00 to 17:00 (Madrid time zone)


    Dr. Daniele Silvestro (University of Gothenburg, Sweden) and Tobias Andermann (University of Gothenburg, Sweden)


    This course is based on the assumption that the easiest way to understand the principles of Bayesian inference and the functioning of the main algorithms is to implement these methods yourself.

    The instructors will outline the relevant concepts and basic theory, but the focus of the course will be to learn how to do Bayesian inference in practice. He will show how to implement the most common algorithms to estimate parameters based on posterior probabilities, such as Markov Chain Monte Carlo samplers, and how to build hierarchical models.

    He will also touch upon hypothesis testing using Bayes factors and Bayesian variable selection.

    The course will take a learn-by-doing approach, in which participants will implement their own MCMCs using R or Python (templates for both languages will be provided).

    After completion of the course, the participants will have gained a better understanding of how the main Bayesian methods are implemented in many programs used in biological research work. Participants will also learn how to model at least basic problems using Bayesian statistics and how to implement the necessary algorithms to solve them.

    Participants are expected to have some knowledge of R or Python (each can choose their preferred language), but they will be guided "line-by-line" in writing their script. The aim is that, by the end of the week, each participant will have written their own MCMC – from scratch! Participants are encouraged to bring their own datasets and questions and we will (try to) figure them out during the course and implement scripts to analyze them in a Bayesian framework.

    More information and registration at or writing courses[at]

    Best regards,
    Soledad De Esteban-Trivigno

    July 15, 2024 - July 18, 2024
    Shanghai, China


    Artificial Intelligence (AI) technologies are widely used in computer applications to perform tasks such as monitoring, forecasting, recommending, predicting, and statistical reporting. They are deployed in a variety of systems, including driverless vehicles, robot-controlled warehouses, financial forecasting applications, and security enforcement and are increasingly integrated with cloud/fog/edge computing, big data analytics, robotics, Internet-of-Things (IoT), mobile computing, smart cities, smart homes, intelligent healthcare, and many more. Despite this dramatic progress, the quality assurance of existing AI application development processes is still far from satisfactory, and the demand for demonstrable levels of confidence in such systems is growing. Software testing is a fundamental, effective, and recognized quality assurance method which has shown its cost-effectiveness to ensure the reliability of many complex software systems. However, the adaptation of software testing to the peculiarities of AI applications remains largely unexplored and needs extensive research to be performed. On the other hand, the availability of AI technologies provides an exciting opportunity to improve existing software testing processes, and recent years have shown that machine learning, data mining, knowledge representation, constraint optimization, planning, scheduling, multi-agent systems, etc. have real potential to positively impact software testing. Recent years have seen a rapid growth of interest in testing AI applications as well as the application of AI techniques to software testing. This conference provides an international forum for researchers and practitioners to exchange novel research results, articulate the problems and challenges from practices, deepen our understanding of the subject area with new theories, methodologies, techniques, process models, impacts, etc., and improve the practices with new tools and resources.


    Abstract Registration Due: March 8, 2024
    Submission Deadline: April 1, 2024
    Notification Due: June 1, 2024
    Final Version Due: June 15, 2024
    Education: Hands-On NGS Data Analysis Workshops 2024
    Submitted by Dr. David Langenberger; posted on Saturday, January 20, 2024


    Check the upcoming hands-on NGS Data Analysis Workshops for 2024:

    February 28-March 1 (Online)
    Online Course - A Practical Introduction to NGS Data Analysis

    March 11-14 (Berlin, Germany)
    RNA-Seq Data Analysis Workshop

    May 6-8 (Berlin, Germany)
    Single-Cell RNA-Seq Data Analysis: A Practical Introduction

    May 27-29 (Online)
    Online Course - Bioinformatics Pipeline Development with Nextflow

    June 10-14 (Berlin, Germany)
    8th Berlin Summer School 2024

    September 2-4 (Munich, Germany)
    Next-Generation Sequencing Data Analysis: A Practical Introduction

    September 25-27 (Leipzig, Germany)
    Single-Cell RNA-Seq Data Analysis: A Practical Introduction

    November 13-15 (Online)
    Online Course - Bioinformatics Pipeline Development with Nextflow

    December 9-11 (Online)
    Online Course - A Practical Introduction to NGS Data Analysis

    Link to all courses:
    Software: BIRCH Bioinformatics System v3.90
    Submitted by Brian Fristensky; posted on Monday, January 15, 2024


    BIRCH 3.90 now available for download at


    • BioLegato 1.1.2
    • New platform: macos-arm64 (eg. M1, M2, Apple Silicon)
    • Many updates to software packages
    • Full Python3 compliance
    BIRCH unifies hundreds of popular bioinformatics tools through the BioLegato family of Object-Oriented applications. BioLegato makes it easy to try out new programs, and to experiment with your data at every step in the analytical process.

    Visit our YouTube Channel at



    Today, the U.S. Food and Drug Administration approved two milestone treatments, Casgevy and Lyfgenia, representing the first cell-based gene therapies for the treatment of sickle cell disease (SCD) in patients 12 years and older. Additionally, one of these therapies, Casgevy, is the first FDA-approved treatment to utilize a type of novel genome editing technology [CRISPR/Cas9], signaling an innovative advancement in the field of gene therapy.


    This article aims to instruct you on how to swiftly locate related sequences via a simple one-click method, using the target name.

    Taking HER2 target as an example, suppose you are very concerned about antibody drugs related to the HER2 target, and you want to inquire about the publicly available antibody sequences for the HER2 target worldwide. This article will teach you how to search for related sequences with one click through the target name.

    Learn more:

    Exciting News!📚🔬

    I am thrilled to announce that my book proposal has been accepted by Springer Nature for an upcoming publication on cutting-edge topics in Bioinformatics. We are inviting passionate authors to contribute to this comprehensive work, covering various crucial areas such as Next-Generation Sequencing, Single-Cell Genomics, Machine Learning in Bioinformatics, and more.

    If you have groundbreaking research or insights to share in any of the chapters listed below, we would love to hear from you! 🌱🧬

    1. Introduction to Bioinformatics: Past, Present, and Future
    2. Next-Generation Sequencing and Genomic Data Analysis
    3. Single-Cell Genomics and Transcriptomics Analysis
    4. Metagenomics and Microbiome Analysis
    5. Structural Bioinformatics and Protein Structure Prediction
    6. Machine Learning and Artificial Intelligence in Bioinformatics
    7. Integrative Omics and Systems Biology
    8. Bioinformatics in Precision Medicine and Personalized Healthcare
    9. Drug Discovery and Repurposing through Bioinformatics
    10. Bioinformatics in Crop Improvement and Agricultural Genomics
    11. Immunoinformatics and Vaccine Development

    Don't miss the opportunity to be part of this book📕! The deadline for submissions is 31 March 2024. Let's push the boundaries of Bioinformatics together. 🚀 #Bioinformatics #ResearchOpportunity #SpringerNature #AcademicPublishing

    👉🏻Hurry up! Only chapter 11 is available.

    For any queries, please reply to spnaturebook[at]


    November 24-26, 2023
    Vellore Institute of Technology
    Vellore, India

    Late-breaking abstracts end this Sunday. Join and experience the bioinformatics gala!

    Five reasons to attend Inbix2023: Indian Conference on Bioinformatics 2023:

    1. It is hosted by VIT, Vellore, the top 10 private universities in India taking leaps and bounds in research in the region, and organised by, India's largest bioinformatics society working for Mentor-Mentee relationships.

    2. Scintillating keynotes and Life Time Achievement Awardees by revered Kenta Nakai, PB Kavi Kishor, PK Gupta and Jayaraman K Valadi and 18 other speakers across all states of India, some of the excellent speakers in the areas of #bioinformatics, #proteomics #machinelearning #genetics and #functionalgenomics

    3. Two panel discussions on "Millet Bioinformatics", elevator pitch and our unique video abstracts, all free for life members

    4. Have you ever heard of 12-17 year old children introduced to bioinformatics and coding? Yes, you guessed it right! We have our prestigious bioinformatics for school children (BIXS) programme through #Bioclues and this time as well, children will enthrall you with their works as we take you through students projects

    5. Team Bioclues #camaraderie through the three day event where we take pride in putting it and a preconference workshop on 23rd magnifies the spirit of bioinformaticshood!

    Come, join us! You will be missing something, if you haven't registered yet! What's more! The deadline for Late-breaking abstract submission ends soon:
    #research #event #india #coding #projects #bioinformatics


    Chimeric antigen receptor (CAR) T cell therapy, in which a patient's own T lymphocytes are engineered to recognize and kill cancer cells, has achieved remarkable success in some hematological malignancies in preclinical and clinical trials, resulting in six FDA-approved CAR-T products currently available in the market. Once equipped with a CAR construct, T cells act as living drugs and recognize and eliminate the target tumor cells in an MHC-independent manner.

    CARs are synthetic immune receptors that connect a single-chain variable fragment (scFv), derived from a monoclonal antibody to T cell signaling domains to eradicate tumor cells independent of the major histocompatibility complex (MHC).

    Once equipped with a CAR, T cells, known as CAR-T cells, act as living drugs and recognize and eliminate the target tumor cells. The conventional CAR structure consists of three modular components: the ectodomain, the transmembrane domain, and the endodomain, each of which has specific components and functions and thus the potential to be optimized.

    An exemplification using Tisagenlecleucel demonstrates the approach of performing sequence retrieval for each individual region, as well as searching for the overall relevant sequence through a combination of different fragments.

    Continue reading:
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