Kernel

Kernel is a software platform developed by Asimov for designing and optimizing genetic constructs in mammalian synthetic biology. It is intended for scientists working in biologics discovery who need to independently design, optimize, and deploy high-performing expression systems. Kernel integrates Asimov's computational models, bio-tools, and domain expertise into a single interface, connecting sequence design with data-driven optimization workflows.

The platform targets biologics discovery teams seeking more predictable expression outcomes, higher first-pass titers, and reduced molecule attrition during screening. It is designed to support the full path from early discovery through cell line development and scale-up.

Model-Guided Expression Optimization

• Performs multi-objective optimization of coding sequences, signal peptides, and vector architectures.
• Aims to drive higher titers and more reliable leads for CMC hand-off and production.
• Supports molecule-aware codon optimization using expression models that account for titer, stability, and developability.
• Helps complex and difficult-to-express molecules survive screening steps, keeping more viable leads in play.
• Produces expression vectors designed to maintain productivity and stability through cell line development and scale-up.

Core Capabilities

• Genetic circuit design: Allows users to compose expression vectors from reusable genetic parts, check design rules, and version genetic circuit layouts for reuse.
• Batch workflows: Supports importing, optimizing, sharing, and ordering many sequences at once.
• Data warehouse: Keeps sequences, metadata, and annotations linked and searchable, providing a shared source of truth across a team.
• Model-guided sequence design: Uses computational models to guide vector designs that maximize titer, stability, and developability.

Key Application Outcomes

• Higher first-pass titers, providing more material for assays and supporting deeper characterization for lead decisions.
• Reduced silent attrition by keeping difficult-to-express molecules viable through screening.
• Expression vectors that are more predictive of performance through cell line development.

Security and Compliance

• Kernel is SOC 2 Type 2 compliant, with validated controls covering Security, Confidentiality, and Availability.
• Customers can access a Trust Center for compliance reports and documentation.
• Authentication is handled via Auth0; passwords are not handled directly by Kernel, and access is enforced at the API layer.
• All customer data, including sequences and experimental data, is encrypted, isolated, and kept confidential.
• Data is not shared between customers, and administrators have fine-grained controls to manage access permissions.

Kernel is deployed as a software-as-a-service (SaaS) product. Support and inquiries can be directed to [email protected], and Asimov offers direct consultations to discuss specific workflows, data systems, and genetic design challenges.