Key challenges facing data-driven multicellular systems biology

Abstract

Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and nancial) commitment.

Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab

“…Macklin's MathCancer Lab is a computational mathematical biology research group that develops theory-and data-driven computational model systems that can help understand and engineer the behavior of multicellular systems, especially in cancer and tissue engineering. Tackling these goals necessitates both multicellular systems biology and multicellular systems engineering perspectives [7,31]. The development of next-generation cures requires a deeper understanding of the fundamental biology of multicellular systems [32].…”

“…Partnerships with the open source community have combined these modeling frameworks with large-scale investigations on supercomputers [6], machine learning approaches that accelerate the investigations and aid model interpretation [38], and new mathematical model components (e.g., Boolean signaling networks [45]). Efforts towards data standardization [7,46] and a recent focus on creating open educational training materials and shared source code repositories seek to grow these computational projects from a single-lab effort to a community-driven ecosystem for mathematical biology, multicellular systems biology, and computational bioengineering [7,47].…”

“…In particular, computational modeling approaches, which include both mathematical modeling of complex cell and molecular systems and statistical modeling of large datasets, are a powerful vehicle for synthesizing disparate and sometimes conflicting data into an integrated biological understanding. Ideally, computational modeling approaches work recursively with experimental workflows; mathematical models quantitate and formalize the largely qualitative, observation-driven "mental models", and the iterative comparison of model outputs to experimental data informs model refinement and also suggests new experimental directions [6][7][8][9].The use of mathematical models clarifies the biological conditions or parameters under which the "mental model" can explain the experimental and simulation data. Increasingly, statistical modeling approaches including machine learning and bioinformatics are used to complement mathematical modeling of cell and molecular biological systems [7].…”

“…Ideally, computational modeling approaches work recursively with experimental workflows; mathematical models quantitate and formalize the largely qualitative, observation-driven "mental models", and the iterative comparison of model outputs to experimental data informs model refinement and also suggests new experimental directions [6][7][8][9].The use of mathematical models clarifies the biological conditions or parameters under which the "mental model" can explain the experimental and simulation data. Increasingly, statistical modeling approaches including machine learning and bioinformatics are used to complement mathematical modeling of cell and molecular biological systems [7]. Analysis of large clinical or experimental datasets can be used to inform parameterization of mathematical models, or to identify novel relationships between cell states and behaviors which can generate new hypotheses for mathematical modeling.…”

“…Despite the consensus around the need for greater integration of computational and experimental modeling approaches, there remains relatively limited adoption of these methods in the life sciences community. Furthermore, the research groups that employ computational modeling approaches largely work in isolation using their own data sources, building their own models, and performing their own analyses [7]. Biologists in experimental research groups face substantial structural, technical and educational barriers to learning to implement computational modeling approaches [10].…”