By Hidehiko Otake, CEO of Corundum Systems Biology
Traditional biomedical research has excelled by simplifying biology into understandable components, transforming medicine. Increasingly, however, complex chronic diseases require tools that can capture interactions across systems, not just individual components.
A “systems” approach to healthcare can address the most fundamental challenges in human health, redefining how we understand, prevent, and manage disease to improve human wellbeing.
What is Systems Biology?
Systems biology is the integration of multidimensional biological, environmental, and digital information into predictive, mechanistic, and actionable frameworks. Falling costs and improving quality of single cell, spatial and longitudinal data, combined with the advent of AI are now making it practical to shift from the reductionist approach to an integrated and more comprehensive perspective. Uniting diverse datasets and modeling strategies to understand the complexity of interconnected systems across multiple scales can accelerate clinical development.
Foundational Pillars of Systems Biology
We see four fundamental pillars of systems biology that are ripe for investment:
- Multimodal Integration: AI platforms that reason across biomedical data modalities, including omics, microbiome, medical imaging and clinical parameters, resulting in unified, actionable frameworks.
- Mechanistic Modelling of Biological Networks: Platforms capable of modelling immune, metabolic and neurological networks that enable us to move from data description to casual, quantitative prediction.
- Data Enabled Discovery, Diagnostics and Therapeutics: Companies where data is the engine of value creation, including AI-enabled drug discovery platforms, wearables and diagnostic tools.
- Data Creation Platforms: Platforms that collect deep, standardized, longitudinal, biological data, forming an infrastructure layer upon which therapeutics, diagnostic tools and health optimization tools can be developed.
These foundational pillars guide us in identifying appropriate companies to support – those that can generate outsized scientific and commercial impact. Ventures that combine differentiated data assets, advanced computational capabilities and clear market alignment stand out as having the potential to be scalable businesses.
Implementing Systems Biology Into an Investment Approach
We are committed to working with researchers and entrepreneurs at the earliest stages of the venture process. CSB is built to sit in the middle ground between tech and biotech investing, underwriting computational platforms while maintaining the evidence standards and milestone orientation required to build enduring biotech companies. We provide comprehensive support, escorting ventures from ideation to commercialization, offering access to funding, resources and mentorship designed to help navigate the R&D and regulatory processes.
In our first fund, we focused our investments on microbiome and human bio-intelligence platforms, an entry point into the systems biology sector. We’re now expanding our scope for our second fund to include systems immunology and neuroscience, food-as-medicine, synthetic biology and sound applications of modern AI.
We see synergy between each of these disciplines and seek companies that can leverage other technologies and players in the systems biology ecosystem. Over time, we believe that these companies and technologies will redefine how healthcare is measured, forecast and improved, driving the future of therapeutics, diagnostics and healthcare management.
Our expansion from our first to second fund also reflects our investment approach. We see evolution as an asset not a risk. As biology and technology evolves we will adjust our thematic focus to incorporate the latest scientific insights and shifting clinical needs.
Join Us!
If you’re building at the intersection of biology, data, and technology, or if you want to learn more about how systems biology can drive durable impact, we would love to connect (https://www.csb.co.jp/contact).
