Quantitative modeling is poised to redefine the practice of biology. Biological systems are messy and interconnected, filled with feedback loops and indirect action, making it computationally infeasible to model a large system with high fidelity. One strategy to handle this is to carve out small pieces of the system and hope that studying them in exacting detail devoid of context will produce results close enough to the real thing. Another is to treat the system as a black box, collections of inputs and outputs to run statistical regressions over, trying to establish causal relationships with no understanding of the mechanisms inside.
However, there is a third approach: to model mathematically the important parts of the system and their relationships, capturing as much essential complexity as possible while abstracting over aspects that aren't critical. These models are very useful, but biological systems are intricate and highly connected, so they can quickly become intractable as more functional relationships are determined to be relevant. However, that's only because the mathematics underlying these biological simulations is overly cautious and dated. Using modern mathematical perspective from proof theory and computational differential geometry, these equations may be rearranged into equivalent simpler forms, sliced up into pieces and run in parallel, and in some cases contracted down to trivial logical problems. These techniques have already seen common use in astrodynamics, computational fluid mechanics, and computational physics. We're taking the tools that have enabled rapid growth and development in these fields and applying them to biological analysis.
We're building Sylph: a platform for systems biology. Sylph is an integrated tool for biological modeling and analysis, available as a service, with a public web api and easy client integration into common tools and workflows. Researchers and bioengineers today are largely reliant on tools that were designed for general engineering applications and are poorly adapted for their problem domain. They have to trade speed for convenience: either they invest the time and knowledge required to write and maintain a specialized software stack, or else they must use the same tools as a mechanical engineer. Tasks that could have taken minutes instead take hours or days, analyses that could be near-perfect accumulate large error, and the practice of systems biology is restricted to a much smaller scope than it otherwise would be.
Our goal is to eliminate this tradeoff entirely. The Sylph platform is a specialized tool for analysis, simulation, and experiment design driven by the formats and formalisms already present in systems and synthetic biology. Sylph makes the core problems of biological analysis more tractable and wipes away the challenges inherent in setting up and operating a software stack. Sylph can be used from a Jupyter repl, in Matlab, or even run as part of a reinforcement learning model. We believe the core value of Sylph is to drastically reduce the infrastructure complexity faced by a biologist when they're at their desk instead of in the lab, to make systems biology easier to learn and practice, and to enable the exploration of new approaches and methods the existing tooling simply cannot handle.
Sylph is easily applicable in the areas of synthetic biology, drug discovery, and oncology and immunology research. For some research groups and companies, our tools will be drop-in replacements for the ones they already use. For others, we offer a clear value proposition and easy onboarding; often, people avoid the systems approach entirely because of inadequacies in the very tools we intend to replace. We want to be the platform of choice for the systems biology work already being done, while also opening up new areas of research, and new markets, by spurring adoption of systems biology where it may otherwise have taken years to spread. Sylph is a public platform: anyone can sign up, download a client, and begin using it immediately with no upfront cost. Success for us will be reading testimonials three or four years from now about research breakthroughs and successful companies built on our work.
Software for us is just the beginning. We're also in the early stages of designing a methodology and automation tools for microfluidics experiment design and chip layout, making use of the same mathematical advances that drive the Sylph platform. We believe the same formalisms used to describe and simulate a biological system can be used to automate wetlab work. Testing genetic modifications, enzyme-assisted reactions, metabolic processes, and stem cell differentiation could be as easy as submitting a model of the experiment to Sylph and waiting for results. That's how we want to practice biology.
We're presently seeking seed funding to give us the resources we need to finish our mathematics work and development of our software platform. To this end, we're raising just enough to take us from prototype to launch and beyond. You can reach us at firstname.lastname@example.org or email@example.com.
In the coming weeks, we'll have posts from Vivien about her broader vision for the company and systems biology as a whole, and from Sig about the concrete details of our mathematical approaches. Follow us at @sylphbio to keep updated about our progress.