Biology subjects

Ferdowsi, A.

Publications and source records attributed to Ferdowsi, A..

3 recordsLinked to original sources

bioRxiv2026

ProtoNetStack for DNA-Encoded Source Routing and Majority Aggregation in Protocell Molecular Nanonetworks

Protocell communities can support programmable molecular nanonetworks, yet most demonstrations use broadcast diffusion or fixed sender-receiver circuits. We introduce PO_SCPLOWROTOC_SCPLOWNO_SCPLOWETC_SCPLOWSO_SCPLOWTACKC_SCPLOW, a network-layer abstraction in which a logical DNA-encoded packet carries a payload, a processing-address list, and an optional forwarding budget. The list determines where localized molecular services transform the packet, not its bidirectional diffusive trajectory. We formulate a finite-state reaction-transport model whose concentration dynamics and single-copy continuous-time Markov chain use the same generator. Under ideal specificity, positive rates, connected transport, no degradation, and sufficient budget, packet stages advance only in the encoded order and delivery occurs almost surely. All injected concentration is delivered asymptotically. Uniform first-order degradation makes delivery probability the Laplace transform of the lossless delivery-time distribution. A union-bound result separates endpoint delivery from route-faithful delivery under off-target processing. As an application, we develop cancellation-based strict-majority aggregation on rooted protocell trees. Conservation of token imbalance proves asymptotic correctness and yields a finite-time certificate. With one initial token per node, outside-root mass below one guarantees the correct root sign. Direct matrix-exponential calculations show sequential processing, branching addressability, route-length attenuation, and bounded forwarding work. A 16-condition finite-copy benchmark with 20,000 trajectories per condition shows that off-target reactions can increase endpoint arrival while decreasing route-faithful delivery. Adaptive ordinary differential equation simulations on trees up to 511 compartments show decision time increasing approximately with maximum tree depth and quantify bias from asymmetric loss. PO_SCPLOWROTOC_SCPLOWNO_SCPLOWETC_SCPLOWSO_SCPLOWTACKC_SCPLOW is therefore a formally analyzable molecular networking architecture and an experimentally testable blueprint. Sequence-resolved gates and chassis calibration remain future work.

synthetic biology↗

bioRxiv2026

Efficient Stochastic Trace Generation for Transcription

Bursty transcription in single cells typically produces over-dispersed, skewed, and sometimes heavy-tailed expression distributions that are explained by two-state Markov models of the promoters. While the gold standard for simulation is exact stochastic sampling with Gillespies algorithm, obtaining thousands of timed traces is computationally costly. Surrogate models based on stochastic differential equations (SDEs) are widely used to speed up this simulation process. An example is the Chemical Langevin Equation based on Gaussian noise, which, however, does not capture heavy-tailed noise. In this work, we present a unified SDE framework that combines deterministic drift, Gaussian fluctuations, and additive sporadic jumps of arbitrary distributions, and provide an open-source Python implementation, bcrnnoise. The framework subsumes standard surrogate models and allows for vectorized generation of batches of transcription traces. We assess computational speed and accuracy of common surrogate models along with new models, showing that high accuracy can be obtained while reducing computational cost up to two orders of magnitude.

bioinformatics↗

bioRxiv2021

TSCDA: A novel greedy approach for community discovery in networks

In this paper, we introduce a new approach for detecting community structures in networks. The approach is subject to modifying one of the connectivity-based community quality functions based on considering the impact that each communitys most influential node has on the other vertices. Utilizing the proposed quality measure, we devise an algorithm that aims to detect high-quality communities of a given network based on two stages: finding a promising initial solution using greedy methods and then refining the solutions in a local search manner. The performance of our algorithm has been evaluated on some standard real-world networks as well as on some artificial networks. The experimental results of the algorithm are reported and compared with several state-of-the-art algorithms. The experiments show that our approach is competitive with the other well-known techniques in the literature and even outperforms them. This approach can be used as a new community detection method in network analysis.

bioinformatics↗