Biology subjects

Mısırlı, G.

Publications and source records attributed to Mısırlı, G..

4 recordsLinked to original sources

bioRxiv2026

Ontology-driven software engineering using LLMs for knowledge graphs in engineering biology

Large language models have transformed software engineering practices. However, generated artefacts are not always developer-friendly and may partially meet complex requirements. As the need to standardise, integrate, and develop tools in engineering biology increases, novel approaches are needed to create and maintain intuitive software sustainably. Here, we present an ontology-driven approach using large language models to create user-facing software libraries for knowledge graphs. We introduce an ontology-to-language framework to systematically map domain terms and graph structures. We then demonstrate this approach by creating an ontology for the latest Synthetic Biology Open Language standard and generating the sbol-script software library, which can be used within browsers or to develop applications with native web support. This ontology-driven software engineering approach and these resources are essential for the community and to facilitate the development of sustainable software projects. The SBOL3 Ontology and the sbol-script library are available from https://github.com/SynBioDex/sbol-owl3 and https://github.com/SynBioDex/sbol-script.

synthetic biology↗

bioRxiv2023

Engineering genetic circuits in receiver cells for diffusion-based molecular data communications

Developments in bioengineering and nanotechnology have ignited the research on biological and molecular communication systems. Despite potential benefits, engineering communication systems to carry data signals using biological messenger molecules is challenging. Diffusing molecules may fall behind their schedule to arrive at a receiver, interfering with symbols of subsequent time slots and distorting the signal. Theoretical molecular communication models often focus solely on the characteristics of the communication channel and fail to provide an end-to-end system response, since they assume a simple thresholding process for a receiver cell and overlook how the receiver can detect the incoming distorted molecular signal. There is a need to develop viable end-to-end communication models. In this paper, we present a model-based framework for designing diffusion-based molecular communication systems coupled with synthetic genetic circuits. We describe a novel approach to encode information as a sequence of bits, each transmitted from a sender as a burst of specific number of molecules, control cellular behavior, and minimize cellular signal interference by employing equalization techniques from communication theory. This approach allows the encoding and de-coding of data bits efficiently using two different types of molecules that act as the data carrier and the antagonist to cancel out the heavy tail of the former. We also present Period Finder, as a tool to optimize communication parameters, including the number of molecules and symbol duration. This tool facilitates automating the choice of communication parameters and identifying the best communication scenarios that can produce efficient cellular signals.

synthetic biology↗

bioRxiv2021

Virtual Parts Repository 2: Model-driven design of genetic regulatory circuits

Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits where manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater for the modular composition of models of synthetic systems that do not yet exist in nature. Here, we present the Virtual Parts Repository 2, a resource to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular and composable models. The repository is service-oriented and can be utilized by design tools in computational workflows. Designs provided in Synthetic Biology Open Language documents are used to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a rule-based modeling abstraction based on reaction networks to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction incorporates design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids and cellular resource dependency. The computational resources and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/439316v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@be045dorg.highwire.dtl.DTLVardef@110856forg.highwire.dtl.DTLVardef@1a861eforg.highwire.dtl.DTLVardef@1b5cf48_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗