arXiv subjects

H. Steven Wiley

Publications and source records attributed to H. Steven Wiley.

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.

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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.

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arXiv2022

SBbadger: Biochemical Reaction Networks with Definable Degree Distributions

Motivation: An essential step in developing computational tools for the inference, optimization, and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. Results: In this work we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms, and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide samples outputs, and compare it to currently available biochemical reaction network generation software.

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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.

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