What’s next for computational systems biology?

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