arXiv subjects

Steven S. Andrews

Publications and source records attributed to Steven S. Andrews.

4 recordsLinked to original sources

arXiv2026

DigiPhen: a new paradigm for building predictive models of biological systems

Reengineered biological systems have the potential to revolutionize chemical and material production, enhance critical mineral recovery, serve as threat sensors and improve human health. Unfortunately, the extreme complexity of organisms has made it difficult to achieve this potential in all but the simplest cases. Recent technological advances, however, have provided a foundation for solving this problem. Here, we describe a capability for accelerating the reengineering of cells by providing accurate predictions of the impact of genetic or environmental changes on cell phenotype. This digital phenome platform (DigiPhen) consists of integrated experimental, analytical and modeling workflows for building a digital representation of microbial or plant systems. It is designed around an expanding set of interchangeable, interconnecting software and experimental modules that can accurately represent the mechanistic determinants of phenotype. The DigiPhen platform will systematically collect data on cell composition, spatial organization, metabolic pathways and regulatory networks in a semi-autonomous fashion and use this information to build modular, multi-scale models of biological systems. These models will be used to predict molecular and environmental changes needed for producing desired biological outcomes. DigiPhen is intended to be the heart of community research campaigns that will meet the immediate needs of individual researchers while fulfilling long-term goals of the scientific community. Altogether, the DigiPhen platform represents a new paradigm for building predictive models of biological systems.

q-bio.MN↗

arXiv2023

Design Patterns of Biological Cells

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology in order to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.

q-bio.MN↗

arXiv2022

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML, and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

q-bio.QM↗

arXiv2021

Dynamics and Sensitivity of Signaling Pathways

Signaling pathways serve to communicate information about extracellular conditions into the cell, to both the nucleus and cytoplasmic processes to control cell responses. Genetic mutations in signaling network components are frequently associated with cancer and can result in cells acquiring an ability to divide and grow uncontrollably. Because signaling pathways play such a significant role in cancer initiation and advancement, their constituent proteins are attractive therapeutic targets. In this review, we discuss how signaling pathway modeling can assist with identifying effective drugs for treating diseases, such as cancer. An achievement that would facilitate the use of such models is their ability to identify controlling biochemical parameters in signaling pathways, such as molecular abundances and chemical reaction rates, because this would help determine effective points of attack by therapeutics.

q-bio.MN↗