More Like this

1. Adapting modeling and simulation credibility standards to computational systems biology

   https://doi.org/10.1186/s12967-023-04290-5  

   Tatka, Lillian T.; Smith, Lucian P.; Hellerstein, Joseph L.; Sauro, Herbert M. (July 2023, Journal of Translational Medicine)

   Abstract Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as forms of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrower domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models. 

   more » « less
2. Charting a New Frontier Integrating Mathematical Modeling in Complex Biological Systems from Molecules to Ecosystems

   https://doi.org/10.1093/icb/icab165  

   White, Katharine A; McEntire, Kira D; Buan, Nicole R; Robinson, Lecia; Barbar, Elisar (July 2021, Integrative and Comparative Biology)

   Abstract Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative modeling process, and nurture a cultural shift towards interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems. 

   more » « less
3. Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

   https://doi.org/10.3389/fpls.2021.687652  

   Dale, Renee; Oswald, Scott; Jalihal, Amogh; LaPorte, Mary-Francis; Fletcher, Daniel M.; Hubbard, Allen; Shiu, Shin-Han; Nelson, Andrew David; Bucksch, Alexander (July 2021, Frontiers in Plant Science)

   null (Ed.)

   The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research. 

   more » « less
4. A new era of Synthetic Biology - microbial community design

   https://doi.org/10.1093/synbio/ysae011  

   Matuszyńska, Anna; Ebenhöh, Oliver; Zurbriggen, Matias_D; Ducat, Daniel_C; Axmann, Ilka_M (July 2024, Synthetic Biology)

   Abstract Synthetic biology conceptualises biological complexity as a network of biological parts, devices and systems with predetermined functionalities, and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesise and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multi-species communities. We argue for a shift in perspective from single organism-centred approaches to emphasising the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. 

   more » « less
5. Dynamic Flux Balance Analysis Models in SBML

   Watanabe, L.; König, M.; and Myers, C. (January 2018, BioRXiv)

   Motivation: Systems biology models are typically simulated using a single formalism such as ordinary differential equations (ODE) or stochastic methods. However, more complex models require the coupling of multiple formalisms since different biological concepts are better described using different methods, e.g., stationary metabolism is often modeled using flux-balance analysis (FBA) whereas dynamic changes of model components are better described via ODEs. The coupling of FBA and ODE frameworks results in dynamic FBA models. A major challenge is how to describe such hybrid models coupling multiple frameworks in a standardized way, so that they can be exchanged between tools and simulated consistently and in a reproducible manner. Results: This paper presents a scheme and implementation for encoding dynamic FBA models in the Systems Biology Markup Language (SBML), thereby allowing to exchange multi-framework computational models between software tools. The paper shows the feasibility of the approach using various example models and demonstrates that different tools are able to simulate the hybrid models and agree on the results. As part of this work, two independent implementations of a multi-framework simulation method for dynamic FBA have been developed supporting such models: iBioSim and sbmlutils. Availability: All materials and models are available from https://github.com/matthiaskoenig/dfba. The tools used in this project are freely available: iBioSim at http://www.async.ece.utah.edu/ibiosim and sbmlutils at https://github.com/matthiaskoenig/sbmlutils/. 

   more » « less