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APBioNET Talks: Dynamic modeling of chromosomal instability in somatic genomes


Chromosomal instability (CIN), a constantly high frequency of chromosome segregation errors during cell divisions, is a major form of genome instability and plays an import role in intra-tumour heterogeneity, metastasis, and therapy resistance. CIN often leads to structural or numerical chromosomal alterations, such as structural variants and copy number alterations. Linking these alterations detected from cancer genomics data with stochastic modelling and Bayesian inference provides a powerful approach to quantify CIN in an evolutionary context, which helps to better understand cancer evolution and inform cancer treatment. In this talk, I will share our work on modelling experimental and real data with this approach.

Speaker Profile:

“I am currently a Surrey Future Fellow, at Section of Systems Biology and Surrey Institute for People-Centred AI, University of Surrey.
Before joining Surrey, I was a Postdoc at Department of Cell and Developmental Biology University College London, where I worked on dynamical modelling of chromosomal instability (CIN) in cancer genomes. Previously, I was a Postdoctoral Fellow at Genome Institute of Singapore, where I mainly developed pipelines and methods to analyse tumour heterogeneity and clonal evolution in liver and lung cancer genomes. I completed my PhD in Computational Biology at School of Computing National University of Singapore, where I developed machine learning and phylogenetic methods for problems related to lateral gene transfer. I obtained my Master’s and Bachelor’s degree from Software Engineering Institute East China Normal University, where I led the development of platforms for high-throughput biological data analysis, including RNA-Seq and proteomic data.

My research is in the broad field of computational biology, which bridges software engineering, machine learning, algorithms, statistics, phylogenetics, population genetics, and omics. I am particularly interested in developing new computational methods and models to address important biological problems related to human health. My goal is to facilitate the mining of new knowledge from the accumulating huge amounts of data for the biological and biomedical community. I have developed several new methods and applied available methods to tackle basic questions arising in the study of species and cancer evolution. My current primary interests are evolutionary dynamics of cancer genomes, especially those driven by CIN, which are still less well studied than point mutations but critical in tumorigenesis and patient treatment.


APBioNETTalks 8: Everything Everywhere All at Once: Lytic Polysaccharide Monooxygenases – Structure, Function, and Dynamics

Cellulases are used by the biofuel industry for conversion of cellulose into glucose, which can be converted into bioethanol. While 1st generation biomass (starch and sugarcane) are currently established routes for production of bioethanol, the use of 2nd generation biomass (consisting of lignocellulose) is still in its infancy in India. Deconstruction of lignocellulose is a challenge that is overcome by using Celluases, a broad term for enzymes that act on Cellulose. Our focus was on two industrially important cellulases: Endoglucanases and Lytic Polysaccharide Monooxygenases (LPMO). While endoglucanase cleaves amorphous cellulose, LPMO acts on crystalline cellulose polymers.
The talk will focus on using diverse computational research (such as Elastic Network Models, Evolutionary Analysis, Machine Learning, and RNA-Seq analysis) that were performed to understand their thermostability and structural dynamics. Specifically, elastic network models (ENM) revealed the correlation of their function with respect to thermostability in Endoglucanases, and ENM highlighted the structural dynamics of the substrate binding region of LPMOs. Similarly, evolutionary analysis of the catalytic domain of LPMOs showed the substrate specificity (cellulolytic or chitinolytic) and the implication in the AA10 family of LPMOs. Recently, we developed a machine learning-based functional annotation tool for classifying a sequence belonging to either AA9 or AA10 family of LPMOs. Currently, at SASTRA university my lab is focused on developing a knowledge base for LPMOs that implements Big Data analytics along with natural language processing.

Speaker Bio:
Dr. Ragothaman M. Yennamalli is a computational biologist at SASTRA Deemed to be University at Thanjavur, Taml Nadu. He has more than a decade of experience in predictive modelling and biomolecular simulation projects. Dr. Yennamalli’s skills involve machine learning, systems biology, molecular docking, molecular dynamics simulation

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Introducing the Biggest Breakthrough in Bioinformatics: The Asia & Pacific Bioinformatics Joint Conference!

Call for Submissions: Propel Your Research onto the Global Stage at the Asia & Pacific Bioinformatics Joint Conference!
Seize this opportunity to share your discoveries, insights, and advancements with an international audience of peers, mentors, and scientific leaders. Whether you’re exploring genomics, proteomics, computational biology, or AI and their applications, your research deserves to be showcased on this stage.

💡 Submission Topics: Choose from a diverse range of submission topics tailored to accommodate a wide spectrum of bioinformatics research areas, including but not limited to:

Single-cell analysis
AI and their applications
Protein/rna structure analysis, prediction and design
Personalized medicine and computational genetics
Metagenomics and microbiome analysis
Network biology and systems biology
Drug discovery and repurposing
Ethical, legal and social issues (elsi)
Bioinformatics education
Omics-based informatics
Databases, ontologies and biocuration
Evolutionary, comparative genomics and phylogenetics
Synthetic biology and engineering
General computational biology

Any recent research, published or unpublished, is eligible for submission and should be received by July 7.

Acceptance notifications will be sent no later than July 28.

The conference will accept late poster abstracts beginning July 30 and ending August 7.


Call to Host Future InCoB:

Call to Host Future InCoB:

The International Conference on Bioinformatics (InCoB) is the flagship conference of the Asia Pacific Bioinformatics Network (APBioNET). APBioNET invites bids to host future InCoBs.

The deadline to submit an expression of interest (EOI) is 1 March 2024.

Submit an EOI here:

InCoB2023 Special Education Track

Special Track on Bioinformatics Education and Training (OPEN VIRTUAL EVENT)

We are happy to share that the special track on Bioinformatics Education Training is now open to all.

To indicate your interest in this event, please register at this link:

We thank you for your help in disseminating this invitation to your colleague.