arXiv subjects

Animesh Ray

Publications and source records attributed to Animesh Ray.

4 recordsLinked to original sources

arXiv2026

The Evolutionary Dynamics of AI, Politicization, Contestation, and Trust in Science Funding

Economic stability and progress in modern technological societies depend on vigorous and independent public funding of science and engineering research. When peer review or funding decisions are perceived as politically directed, scientists, funding agencies, and the public react in coupled and conflicting ways. We an evolutionary game-theoretic model to analyze how perceived political interference in science funding affects the interrelated behaviors of scientists, funding agencies, and the public. The model simulates scientists choosing to refuse peer reviews and retaliate, agencies responding by adopting AI-assisted review and altering reviewer pay, and the public accepting or rejecting these AI systems. Through numerical simulations, five principal findings are identified: (1) Operational capacity and institutional legitimacy are governed by separate conditions and can fail independently. (2) Legitimacy of the process is bistable, meaning final states are determined by the public's acceptance of AI. (3) Since the career cost for researchers refusing to review is generally low, resistance/retaliation cascades can readily ignite, leading identical institutions to entirely opposite fates. (4) Increasing reviewer pay only stabilizes participation within a strict budget-solvency frontier, and emergency pay can paradoxically erode the legitimacy it aims to protect. (5) Finally, finite-population simulations reveal that baseline scenarios partition into either legitimacy recovery without capacity or joint failure, confirming that the fundamental separation of capacity and legitimacy outcomes is a dominant structural feature driven primarily by initial scientific resistance and politicization levels. This theoretical work quantifies issues for future work in science policy.

physics.soc-ph↗

arXiv2026

AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2

Machine learning-based antibody design is emerging as one of the most promising approaches to combat infectious diseases, due to significant advancements in the field of artificial intelligence and an exponential surge in experimental antibody data (in particular related to COVID-19). The ability of an antibody to bind to an antigens (called binding affinity) is one of the the most critical properties in designing neutralizing antibodies. In this study we introduce Ab-Affinity, a new large language model that can accurately predict the binding affinity of antibodies against a target peptide, e.g., the SARS-CoV-2 spike protein. Code and model are available at https://github.com/ucrbioinfo/AbAffinity.

q-bio.QM↗

arXiv2014

A signature of power law network dynamics

Can one hear the 'sound' of a growing network? We address the problem of recognizing the topology of evolving biological or social networks. Starting from percolation theory, we analytically prove a linear inverse relationship between two simple graph parameters--the logarithm of the average cluster size and logarithm of the ratio of the edges of the graph to the theoretically maximum number of edges for that graph--that holds for all growing power law graphs. The result establishes a novel property of evolving power-law networks in the asymptotic limit of network size. Numerical simulations as well as fitting to real-world citation co-authorship networks demonstrate that the result holds for networks of finite sizes, and provides a convenient measure of the extent to which an evolving family of networks belongs to the same power-law class.

q-bio.QM↗

arXiv2005

A p53 Oscillator Model of DNA Break Repair Control

The transcription factor p53 is an important regulator of cell fate. Mutations in p53 gene are associated with many cancers. In response to signals such as DNA damage, p53 controls the transcription of a series of genes that cause cell cycle arrest during which DNA damage is repaired, or triggers programmed cell death that eliminates possibly cancerous cells wherein DNA damage might have remained unrepaired. Previous experiments showed oscillations in p53 level in response to DNA damage, but the mechanism of oscillation remained unclear. Here we examine a model where the concentrations of p53 isoforms are regulated by Mdm22, Arf, Siah, and beta-catenin. The extent of DNA damage is signalled through the switch-like activity of a DNA damage sensor, the DNA-dependent protein kinase Atm. This switch is responsible for initiating and terminating oscillations in p53 concentration. The strength of the DNA damage signal modulates the number of oscillatory waves of p53 and Mdm22 but not the frequency or amplitude of oscillations{a result that recapitulates experimental findings. A critical fnding was that the phosphorylated form of Nbs11, a member of the DNA break repair complex Mre11-Rad50-Nbs11 (MRN), must augment the activity of Atm kinase. While there is in vitro support for this assumption, this activity appears essential for p53 dynamics. The model provides several predictions concerning, among others, degradation of the phosphorylated form of p53, the rate of DNA repair as a function of DNA damage, the sensitivity of p53 oscillation to transcription rates of SIAH, beta-CATENIN and ARF, and the hysteretic behavior of active Atm kinase levels with respect to the DNA damage signal

q-bio.MN↗