Biology subjects

Saglam, A. S.

Publications and source records attributed to Saglam, A. S..

3 recordsLinked to original sources

bioRxiv2022

WEBNG: A templating tool for weighted ensemble sampling of rule-based models

Time scales for biological processes span many orders of magnitude, forcing modelers to tackle coupled processes that have large time scale gaps. This results in rare events, which take longer to occur than the fastest processes in the model. Efficient generation of rare events has been a focus of modelers for a long time and multiple software packages implement various rare event sampling algorithms. However, these packages frequently require expertise to get started with, making it harder for researchers to start using them. WEBNG (short for Weighted Ensemble-BioNetGen) is an open source software framwework that bridges the open source software packages WESTPA, which implements the weighted ensemble method for sampling rare events, and BioNetGen, which facilitates the specification and simulation of biochemical reaction network models following a rule-based approach. WEBNG simplifies rare event sampling in simulations of rule-based models by taking a model specified in the BioNetGen language (BNGL) and generating a WESTPA simulation folder ready to simulate with default parameters selected to match model observables. WEBNG is written in Python with dependencies only on proven, open-source packages that are in active development, which makes WEBNG easy to install and maintain. Here, we describe the architecture and features of WEBNG and demonstrate its capabilities through application to a two-gene model of cell fate transitions.

systems biology↗

bioRxiv2022

MCell4 with BioNetGen: A Monte Carlo Simulator of Rule-Based Reaction-Diffusion Systems with Python Interface

Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4s Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.

biophysics↗

bioRxiv2020

Combination Treatment Optimization Using aPan-Cancer Pathway Model

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisti-cated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify potentially novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the mixtures and dosages used in sequential treatment plans--that is, optimized sequences of potentially different drug combinations--providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be used for other signaling pathway models also, provided that a suitable predictive model is available.

systems biology↗