arXiv subjects

Alfonso Valencia

Publications and source records attributed to Alfonso Valencia.

12 recordsLinked to original sources

arXiv2026

A novel scalable high performance diffusion solver for multiscale cell simulations

Agent-based cellular models simulate tissue evolution by capturing the behavior of individual cells, their interactions with neighboring cells, and their responses to the surrounding microenvironment. An important challenge in the field is scaling cellular resolution models to real-scale tumor simulations, which is critical for the development of digital twin models of diseases and requires the use of High-Performance Computing (HPC) since every time step involves trillions of operations. We hereby present a scalable HPC solution for the molecular diffusion modeling using an efficient implementation of state-of-the-art Finite Volume Method (FVM) frameworks. The paper systematically evaluates a novel scalable Biological Finite Volume Method (BioFVM) library and presents an extensive performance analysis of the available solutions. Results shows that our HPC proposal reach almost 200x speedup and up to 36% reduction in memory usage over the current state-of-the-art solutions, paving the way to efficiently compute the next generation of biological problems.

cs.DC↗

arXiv2025

PhysiBoSS-Models: A database for multiscale models

PhysiBoSS is an open-source platform that integrates agent-based modeling of cell populations with intracellular stochastic Boolean networks, enabling multiscale simulations of complex biological behaviors. To promote model sharing and versioning, we present the PhysiBoSS-Models database: a curated repository for multiscale models built with PhysiBoSS. By providing a simple Python API, PhysiBoSS-Models provides an easy way to download and simulate preexisting models through tools such as PhysiCell Studio. By providing standardized access to validated models, PhysiBoSS-Models facilitates reuse, validation, and benchmarking, supporting research in biology.

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arXiv2025

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences

Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial datasets that mirror the characteristics of real data, allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of synthetic data in Life Sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of synthetic data evaluation metrics in several Life Sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing synthetic data. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in synthetic data but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of synthetic data in scientific discovery, clinical practice and beyond.

q-bio.OT↗

arXiv2023

Lessons learned from a performance analysis and optimization of a multiscale cellular simulation

This work presents a comprehensive performance analysis and optimization of a multiscale agent-based cellular simulation. The optimizations applied are guided by detailed performance analysis and include memory management, load balance, and a locality-aware parallelization. The outcome of this paper is not only the speedup of 2.4x achieved by the optimized version with respect to the original PhysiCell code, but also the lessons learned and best practices when developing parallel HPC codes to obtain efficient and highly performant applications, especially in the computational biology field.

cs.DC↗

arXiv2022

Parallel Model Exploration for Tumor Treatment Simulations

Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a Genetic Algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.

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arXiv2021

Algorithmic complexity in computational biology: basics, challenges and limitations

Computational problems can be classified according to their algorithmic complexity, which is defined based on how the resources needed to solve the problem, e.g. the execution time, scale with the problem size. Many problems in computational biology are computationally infeasible in the sense that the exhaustive search for the optimal solution is prohibitive in practical terms. As a consequence, these problems are tackled through heuristics and approximations aiming to overcome the exceeding computational requirements at the cost of providing suboptimal solutions. The importance of defining the computational complexity of computational biology algorithms is a topic rarely surveyed for broad audiences of bioinformaticians and users of bioinformatics tools. However, recognizing the underlying complexity of any algorithm is essential for understanding their potential and limitations. Thus, the aim of this review is to survey the main algorithmic solutions to intractable problems in computational biology, highlighting the importance of High-Performance Computing in this area.

cs.CC↗

arXiv2020

Unveiling new disease, pathway, and gene associations via multi-scale neural networks

Diseases involve complex processes and modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, biological knowledge pertaining to a disease can be derived from a patient cell's profile, improving our diagnosis ability, as well as our grasp of disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk. Here, we look at differential gene expression obtained from microarray technology for patients diagnosed with various diseases. Based on this data and cellular multi-scale organization, we aim to uncover disease--disease links, as well as disease-gene and disease--pathways associations. We propose neural networks with structures inspired by the multi-scale organization of a cell. We show that these models are able to correctly predict the diagnosis for the majority of the patients. Through the analysis of the trained models, we predict and validate disease-disease, disease-pathway, and disease-gene associations with comparisons to known interactions and literature search, proposing putative explanations for the novel predictions that come from our study.

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arXiv2016

Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity

Network analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromatin marks mediate genomic contacts. We use high-resolution Promoter Capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse embryonic stem cells to investigate promoter-centered chromatin interaction networks and calculate the presence of specific epigenomic features in the chromatin fragments constituting the nodes of the network. We estimate the association of these features to the topology of four chromatin interaction networks and identify features localized in connected areas of the network. Polycomb Group proteins and associated histone marks are the features with the highest chromatin assortativity in promoter-centred networks. We then ask which features distinguish contacts amongst promoters from contacts between promoters and other genomic elements. We observe higher chromatin assortativity of the actively elongating form of RNA Polymerase 2 (RNAPII) compared to inactive forms only in interactions between promoters and other elements. Contacts among promoters, and between promoters and other elements have different characteristic epigenomic features. We identify a possible role for the elongating form of RNAPII in mediating interactions among promoters, enhancers and transcribed gene bodies. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing the comparison of chromatin interaction networks.

q-bio.MN↗

arXiv2014

The shrinking human protein coding complement: are there now fewer than 20,000 genes?

Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein coding potential is the detection of cellular protein expression through peptide mass spectrometry experiments. Here we map the peptides detected in 7 large-scale proteomics studies to almost 60% of the protein coding genes in the GENCODE annotation the human genome. We find that conservation across vertebrate species and the age of the gene family are key indicators of whether a peptide will be detected in proteomics experiments. We find peptides for most highly conserved genes and for practically all genes that evolved before bilateria. At the same time there is almost no evidence of protein expression for genes that have appeared since primates, or for genes that do not have any protein-like features or cross-species conservation. We identify 19 non-protein-like features such as weak conservation, no protein features or ambiguous annotations in major databases that are indicators of low peptide detection rates. We use these features to describe a set of 2,001 genes that are potentially non-coding, and show that many of these genes behave more like non-coding genes than protein-coding genes. We detect peptides for just 3% of these genes. We suggest that many of these 2,001 genes do not code for proteins under normal circumstances and that they should not be included in the human protein coding gene catalogue. These potential non-coding genes will be revised as part of the ongoing human genome annotation effort.

q-bio.GN↗

arXiv2013

Late-replicating CNVs as a source of new genes

Asynchronous replication of the genome has been associated with different rates of point mutation and copy number variation (CNV) in human populations. Here, we explored if the bias in the generation of CNV that is associated to DNA replication timing might have conditioned the birth of new protein-coding genes during evolution. We show that genes that were duplicated during primate evolution are more commonly found among the human genes located in late-replicating CNV regions. We traced the relationship between replication timing and the evolutionary age of duplicated genes. Strikingly, we found that there is a significant enrichment of evolutionary younger duplicates in late replicating regions of the human and mouse genome. Indeed, the presence of duplicates in late replicating regions gradually decreases as the evolutionary time since duplication extends. Our results suggest that the accumulation of recent duplications in late replicating CNV regions is an active process influencing genome evolution.

q-bio.GN↗

arXiv2012

Accurate Demarcation of Protein Domain Linkers based on Structural Analysis of Linker Probable Region

In multi-domain proteins, the domains are connected by a flexible unstructured region called as protein domain linker. The accurate demarcation of these linkers holds a key to understanding of their biochemical and evolutionary attributes. This knowledge helps in designing a suitable linker for engineering stable multi-domain chimeric proteins. Here we propose a novel method for the demarcation of the linker based on a three-dimensional protein structure and a domain definition. The proposed method is based on biological knowledge about structural flexibility of the linkers. We performed structural analysis on a linker probable region (LPR) around domain boundary points of known SCOP domains. The LPR was described using a set of overlapping peptide fragments of fixed size. Each peptide fragment was then described by geometric invariants (GIs) and subjected to clustering process where the fragments corresponding to actual linker come up as outliers. We then discover the actual linkers by finding the longest continuous stretch of outlier fragments from LPRs. This method was evaluated on a benchmark dataset of 51 continuous multi-domain proteins, where it achieves F1 score of 0.745 (0.83 precision and 0.66 recall). When the method was applied on 725 continuous multi-domain proteins, it was able to identify novel linkers that were not reported previously. This method can be used in combination with supervised / sequence based linker prediction methods for accurate linker demarcation.

cs.CE↗

arXiv2011

Mirroring co-evolving trees in the light of their topologies

Determining the interaction partners among protein/domain families poses hard computational problems, in particular in the presence of paralogous proteins. Available approaches aim to identify interaction partners among protein/domain families through maximizing the similarity between trimmed versions of their phylogenetic trees. Since maximization of any natural similarity score is computationally difficult, many approaches employ heuristics to maximize the distance matrices corresponding to the tree topologies in question. In this paper we devise an efficient deterministic algorithm which directly maximizes the similarity between two leaf labeled trees with edge lengths, obtaining a score-optimal alignment of the two trees in question. Our algorithm is significantly faster than those methods based on distance matrix comparison: 1 minute on a single processor vs. 730 hours on a supercomputer. Furthermore we have advantages over the current state-of-the-art heuristic search approach in terms of precision as well as a recently suggested overall performance measure for mirrortree approaches, while incurring only acceptable losses in recall. A C implementation of the method demonstrated in this paper is available at http://compbio.cs.sfu.ca/mirrort.htm

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