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Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure

Artificial life 2025年第2期31卷 129-152页

作者： Moreno, Matthew Andres Rodriguez-Papa, Santiago Dolson, Emily University of Michigan Department of Ecology and Evolutionary Biology Center for the Study of Complex Systems Michigan Institute for Data and AI in Society Michigan State University Department of Computer Science and Engineering Michigan State University Department of Computer Science and Engineering Program in Ecology Evolution Behavior

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence the efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented Artificial Life systems, these drivers constitute key building blocks for open-ended evolution. Here we set out to assess (a) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure;(b) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure;and (c) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Furthermore, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest a potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics.

关键词： agent-based simulation Artificial Life Ecology evolution evolutionary dynamics phylogenetic analysis phylogeny metrics

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Protein codes promote selective subcellular compartmentalization

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science (New York, N.Y.) 2025年第6738期387卷 1095-1101页

作者： Kilgore, Henry R. Chinn, Itamar Mikhael, Peter G. Mitnikov, Ilan Van Dongen, Catherine Zylberberg, Guy Afeyan, Lena Banani, Salman F. Wilson-Hawken, Susana Lee, Tong Ihn Barzilay, Regina Young, Richard A. Whitehead Institute for Biomedical Research Cambridge MA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology (MIT) Cambridge MA United States Abdul Latif Jameel Clinic for Machine Learning in Health MIT Cambridge MA United States Department of Biology MIT Cambridge MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Computational and Systems Biology Program MIT Cambridge MA United States

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

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ALPINE: An Interpretable Approach for Decoding Phenotypes from Multi-condition Sequencing Data 29th

ALPINE: An Interpretable Approach for Decoding Phenotypes ...

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29th International Conference on Research in Computational Molecular biology, RECOMB 2025

作者： Lee, Wei-Hao Li, Lechuan Dannenfelser, Ruth Yao, Vicky Systems Synthetic and Physical Biology Graduate Program Rice University Houston United States Department of Computer Science Rice University Houston United States Ken Kennedy Institute Rice University Houston United States Rice Synthetic Biology Institute Rice University Houston United States

ISBN: (纸本)9783031902512

Advancements in sequencing technology have expanded data availability, capturing diverse phenotypic traits and biological perturbations. However, increased resolution also raises complexity, as studies now examine multiple dimensions, including donor phenotypes, anatomical regions, cell types, and time points. Integrating datasets across studies promises insights into health and disease beyond the scope of individual experiments, but this requires methods that can separate technical artifacts from meaningful biological signals while providing interpretable insights into condition-related genetic factors. Existing approaches tend to focus on either integration or interpretability, rarely addressing both simultaneously. To overcome these challenges, we introduce ALPINE, a joint supervised-unsupervised non-negative matrix factorization framework that disentangles technical and biological variation while directly identifying condition-associated genes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Matrix factorization

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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Computational Intelligence in Artificial Life and Cooperative Intelligent systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent systems, ALIFE-CIS 2025

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis University of Michigan Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and Ai in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Michigan State University Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Mapping dihydropteroate synthase evolvability through identification of a novel evolutionarily critical substructure

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International Journal of Biological Macromolecules 2025年 311卷 143325页

作者： Sanyal, Dwipanjan Shivram, A. Pandey, Deeptanshu Banerjee, Suharto Uversky, Vladimir N. Muzata, Danny Chivukula, Aneesh Sreevallabh Jasuja, Ravi Chattopadhyay, Krishnananda Chowdhury, Sourav Structural Biology and Bioinformatics Division CSIR-Indian Institute of Chemical Biology Kolkata India Department of Computer Science and Information Systems Birla Institute of Technology and Science-Pilani Hyderabad India Department of Biological Sciences Birla Institute of Technology and Science-Pilani Hyderabad India Max Delbrück Center for Molecular Medicine Berlin Germany Research Program in Men's Health: Aging and Metabolism Brigham and Women's Hospital Harvard Medical School BostonMA United States USF Health Byrd Alzheimer's Research Institute Morsani College of Medicine University of South Florida TampaFL33612 United States

Protein evolution shapes pathogen adaptation-landscape, particularly in developing drug resistance. The rapid evolution of target proteins under antibiotic pressure leads to escape mutations, resulting in antibiotic resistance. A deep understanding of the evolutionary dynamics of antibiotic target proteins presents a plausible intervention strategy for disrupting the resistance trajectory. Mutations in Dihydropteroate synthase (DHPS), an essential folate pathway protein and sulfonamide antibiotic target, reduce antibiotic binding leading to anti-folate resistance. Deploying statistical analyses on the DHPS sequence-space and integrating deep mutational analysis with structure-based network-topology models, we identified critical DHPS subsequences. Our frustration landscape analysis suggests how conformational and mutational changes redistribute energy within DHPS substructures. We present an epistasis-based fitness prediction model that simulates DHPS adaptive walks, identifying residue positions that shape evolutionary constraints. Our optimality analysis revealed a substructure central to DHPS evolvability, and we assessed its druggability. Combining evolution and structure, this integrated framework identifies a DHPS substructure with significant evolutionary and structural impact. Targeting this region may constrain DHPS evolvability and slow resistance emergence, offering new directions for antibiotic development. © 2025

关键词： Topology

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Multilevel modeling and value of information in clinical trial decision support

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BMC systems biology 2014年第1期8卷 6-6页

作者： Cui, Yuanyuan Murphy, Brendan Gentilcore, Anastasia Sharma, Yugal Minasian, Lori M. Kramer, Barnett S. Coates, Paul M. Gohagan, John K. Klenk, Juergen Tidor, Bruce Computer Science and Artificial Intelligence Laboratory and Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge 02139 MA United States

BACKGROUND: Clinical trials are the main method for evaluating safety and efficacy of medical interventions and have produced many advances in improving human health. The Women's Health Initiative overturned a half-century of harmful practice in hormone therapy, the National Lung Screening Trial identified the first successful lung cancer screening tool and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial overturned decades-long assumptions. While some trials identify unforeseen safety issues or harms, many fail to demonstrate efficacy. Large trials require substantial resources;to ensure reliable outcomes, we must seek ways to improve the predictive information used as the basis of trials.;CONCLUSION: Using machine learning methods, we identified the parameters of the model that are most predictive of trial outcome and found that the top four are directly related to the rates of reactions producing methylselenol and transporting extracellular selenium into the cell as selenide. This modeling process demonstrates how the approach can be used in advance of a large clinical trial to identify the best targets for conducting further research to reduce the uncertainty in the trial outcome.;RESULTS: Here we demonstrate a modeling framework for linking knowledge of underlying biological mechanism to evaluate the expectation of trial outcomes. Key features include the ability to propagate uncertainty in biological mechanism to uncertainty in trial outcome and mechanisms for identifying knowledge gaps most responsible for unexpected outcomes. The framework was used to model the effect of selenium supplementation for prostate cancer prevention and parallels the Selenium and Vitamin E Cancer Prevention Trial that showed no efficacy despite suggestive data from secondary endpoints in the Nutritional Prevention of Cancer trial and found increased incidence of high-grade prostate cancer in certain subgroups.

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BPS2025 - Systematic embedding of biomechanics within dynamical graph grammars with application to actin networks in dendritic spine heads

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Biophysical Journal 2025年第3期124卷 206a-206a页

作者： Eric Mjolsness Matthew Hur Thomas M. Bartol Terrence J. Sejnowski Departments of Computer Science and Mathematics University of California Irvine Irvine CA USA Mathematical Computational and Systems Biology Program University of California Irvine Irvine CA USA Computational Neurobiology Laborartory Salk Institute for Biological Studies San Diego CA USA

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Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches

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computers in biology and Medicine 2025年 190卷 110044-110044页

作者： Saif, Ahmed Islam, Md. Tarikul Raihan, Md. Obayed Yousefi, Niloofar Rahman, Md. Ajijur Faridi, Hafeez Hasan, Al Riyad Hossain, Mirza Mahfuj Saleem, Rasha Mohammed Albadrani, Ghadeer M. Al-Ghadi, Muath Q. Ahasan Setu, Md. Ali Kamel, Mohamed Abdel-Daim, Mohamed M. Aktaruzzaman, Md. Department of Pharmacy Faculty of Science University of Rajshahi Rajshahi6205 Bangladesh Department of Genetic Engineering and Biotechnology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Jashore7408 Bangladesh Department of Pharmaceutical Sciences College of Health Sciences and Pharmacy Chicago State University ChicagoIL United States Department of Industrial Engineering and Management Systems University of Central Florida USA OrlandoFL United States Department of Pharmacy Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Laboratory Medicine Faculty of Applied Medical Sciences Al-Baha University Al-Baha65431 Saudi Arabia Department of Biology College of Science Princess Nourah bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Microbiology Faculty of Biological Science and Technology Jashore University of Science and Technology Jashore7408 Bangladesh Department of Medicine and Infectious Diseases Faculty of Veterinary Medicine Cairo University Giza12211 Egypt Department of Pharmaceutical Sciences Pharmacy Program Batterjee Medical College P.O. Box 6231 Jeddah21442 Saudi Arabia Pharmacology Department Faculty of Veterinary Medicine Suez Canal University Ismailia41522 Egypt

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to its critical role in tumor proliferation, survival, and resistance. However, a comprehensive analysis of CDC7 across multiple cancers is lacking, and existing therapeutic options have come with limited clinical success. The aim of this is to integrate a comprehensive pan-cancer analysis of CDC7 with the identification of novel marine-derived inhibitors, bridging the understanding of CDC7's role as a prognostic biomarker and therapeutic target across diverse cancer types. In this study, we conducted a pan-cancer analysis of CDC7 across 33 tumor types using publicly available datasets to evaluate its expression, genetic alterations, immune interactions, survival, and prognostic significance. Additionally, a marine-derived compound library of 31,492 molecules was screened to identify potential CDC7 inhibitors using chemoinformatics and machine learning. The top candidates underwent rigorous evaluations, including molecular docking, pharmacokinetics, toxicity, Density Functional Theory (DFT) calculations, and Molecular Dynamics (MD) simulations. The findings revealed that CDC7 is overexpressed in several cancers and is associated with poor survival outcomes and unfavorable prognosis. Enrichment analysis linked CDC7 to critical DNA replication pathways, while its role in modulating tumor-immune interactions highlighted its potential as a target for immunotherapy. Among all tested compounds, Tetrahydroaltersolanol D (CMNPD21999) exhibited the strongest binding affinity and stability, along with better drug-likeness and zero toxicity. These attributes highlight its potential as a promising drug candidate for CDC7 inhibition and future cancer treatment development. Furthermore, additional in vitro and in vivo studies are required to confirm the effectiveness of this drug candidate against the CDC7 protein. © 2025

关键词： Gene expression

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