Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the next-generation of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis.

pdf/whats-next-for-computational-systems-biology-axw8e6yjk3.pdf

What’s next for computational systems biology?

The past decades have witnessed an astounding rise of the nascent field of systems biology. By and large unknown or ignored for a long time, the field rapidly moved into the limelight and is now in the process of becoming a widely recognized and respected component of mainstream biology. Of course, much remains to be explored and accomplished in systems biology within its parent domain of biology, but the time seems ripe for expansions beyond this domain. The goal of such an expansion should not be the creation of new strongholds or academic silos outside biology, but the true integration of biological systems thinking into educational programs of other disciplines. The expansion should naturally start with closely related fields like biophysics, biochemistry, bioinformatics, and bioengineering, but should continue further into other areas invested in the study of life, such as medicine, epidemiology, and public health, as well as applied mathematics and computer science. This perspective sketches out how systems biological thinking might enrich the training of a new generation of scientists in different fields of scientific endeavor.

Perspective: Systems biology beyond biology

AbstractThis curated collection covers a selection of PRIMUS articles published over a roughly 12-year period that focus on modeling and applications. The collection includes sections on individual projects, courses with a significant modeling component, and modeling and applications in extracurricular settings and throughout the curriculum.Keywords: Curated collectionmodelingapplicationsprojectscalculusdifferential equationsmodeling coursesliberal artsmathematical biologyservice learning ACKNOWLEDGMENTSThe authors would like to thank the many authors who have submitted articles involving applications and modeling for publication in PRIMUS and made possible this collection. The second author would also like to thank and remember Ben Fusaro, who passed away in July 2022. He was one of the visionaries behind COMAP's ICM/MCM, or the “applied Putnam” as he originally described it, and was involved in the development of Mathworks Mega Math Challenge for high school students. Ben was a lifelong proponent of collaborative problem solving, often encouraging students and colleagues to explore socially meaningful problems. He was forward-thinking, a gracious collaborator, and a generous friend.DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsEthan BerkoveEthan Berkove earned his PhD from the University of Wisconsin, Madison. His budding interest in applications and modeling was reinforced during his three-year stint as a Davies Associate at the United States Military Academy at West Point. He arrived at Lafayette College in 1999, where he is now Professor of Mathematics. He still enjoys incorporating applications into his classes, and teaches throughout the curriculum, including courses in modeling. In his free time, he enjoys reading and cycling around the Lehigh Valley.Ben GalluzzoBen Galluzzo is the Associate Director of Clarkson University's Institute for STEM Education as well as the Head of School for the University's early college program, The Clarkson School. In addition to his institutional roles, Ben regularly teaches math modeling and promotes mathematics as a tool for investigating and developing an understanding of real-world issues through a variety of funded projects that provide him with the opportunity to develop curriculum, run professional development workshops, engage with professional learning communities and provide STEM outreach activities for students at all grade levels. His current research is focused on both the development of an online environment to support student learning of mathematical modeling and investigating how teachers learn to do and teach mathematical modeling. 

Curated Collection: Modeling and Applications

There is widespread misuse of statistics in research, particularly in the life sciences, which is one of the contributing factors to reproducibility concerns in research. However, the formal quantitative training that life sciences research trainees receive is often quite limited. Our survey of statistics requirements in undergraduate life sciences programmes offered by the top research-intensive universities in Canada, the U15 Group of Canadian Research Universities, confirmed that training in statistics tends to be limited and more general in nature. To help raise awareness and address these limitations, this paper shares findings of this statistics requirements survey and describes the evidence-based framework for a second-year undergraduate course at the University of Toronto, which was introduced to integrate statistics instruction with the life sciences research process. We hope these insights will inform future quantitative course offerings, and ultimately, better prepare students to effectively engage with statistics in life sciences research. 

Bridging statistics and life sciences undergraduate education

doi: https://doi.org/10.12669/pjms.40.2(ICON).8952 
How to cite this: Aijaz J. Why medical professionals must learn mathematics and computing?. Pak J Med Sci. 2024;40(2):-S106.  doi: https://doi.org/10.12669/pjms.40.2(ICON).8952

Why medical professionals must learn mathematics and computing?

The integration of biology with mathematics and computer science mandates the training of students capable of comfortably navigating among these fields. We address this formidable pedagogical chall...

Integration of Biology, Mathematics and Computing in the Classroom Through the Creation and Repeated Use of Transdisciplinary Modules

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Integration of Biology, Mathematics and Computing in the Classroom Through the Creation and Repeated Use of Transdisciplinary Modules