Interactive Science: DNA & Cancer (2D)

Safe, educational visualizations by Rotger Research Foundation — now with deep-dive explainers.

DNA Double Helix — Structure & Base Pairing

Hover/Move: tooltips • Click: pause/play
What you’re seeing
Two sugar–phosphate backbones (curves) with rungs (base pairs): A↔T and C↔G. The “twist” is a 2D projection of the double helix. Hydrogen bonds stabilize pairing.
Adenine (A) Thymine (T) Cytosine (C) Guanine (G)
Why it matters
Base-pairing rules enable faithful copying. Variations (mutations) create diversity and drive evolution. Some changes are neutral, some harmful, some beneficial.
Fast fact: Human cells carry ≈3 billion base pairs per haploid genome.
How to use this section
Move your cursor along the “rungs” to see base-pair tooltips. Click the canvas to pause or resume the twist. Open the Deep Dive below to explore DNA in context.

  • Double helix: two antiparallel strands; sugar–phosphate backbone outside, bases inside.
  • Base pairing: A pairs with T (2 H-bonds); C with G (3 H-bonds). Sequence encodes biological information.
  • Major/minor grooves: protein-binding landscapes enabling regulation.
  • Stability: hydrogen bonding + base stacking interactions.

  1. Replication: copying DNA before cell division; high-fidelity polymerases with proofreading.
  2. Transcription: DNA → RNA by RNA polymerase; promoters & enhancers set when/where genes turn on.
  3. Translation: ribosomes read mRNA codons to build proteins (amino acids).
Note: Alternative splicing and RNA modifications expand protein diversity from a limited gene set.

  • Point mutations: missense, nonsense, silent.
  • Indels: insertions/deletions; frameshifts may alter downstream protein sequence.
  • Structural variants: duplications, inversions, translocations.
  • Somatic vs germline: somatic changes occur after conception; germline are inherited.
Why care? Some variants increase disease risk; others confer protection or are neutral.

  • Chromatin state: DNA wraps around histones; chemical marks open/close access.
  • DNA methylation: often linked to reduced transcription.
  • Enhancers/silencers: noncoding elements that fine-tune gene expression.
  • Noncoding RNAs: miRNA/lncRNA layers of post-transcriptional control.

Base pair: bonded letters (A–T, C–G) that form the rungs of DNA.

Gene: DNA sequence that contributes instructions for a product (often a protein).

Genome: the complete DNA set for an organism.

Mutation/Variant: a change in DNA sequence; “variant” is the neutral term.

Cancer Clone Explorer — Clonal Evolution (Education)

Click canvas: pause/play • Click a cell: pin label
Active drugs reduce targeted clone fitness; try sequences & combos.
What you’re seeing
A simplified tumor ecosystem with three subclones (A/B/C). Cells divide or die based on fitness. Drugs reduce fitness of targeted clones; untargeted clones can expand (resistance).
Clone A Clone B Clone C
Why it matters
Cancer evolves. Treatment choices change the competitive landscape among clones. Concepts here echo real-world ideas like combination therapy and adaptive therapy.
Tip: Try turning on Drug A alone vs A+B together vs switching A→B→C over time.
How to use this section
Toggle drug chips, watch the clone counts change in the top-left HUD. Click a cell to pin its label (clone & age). Click the canvas to pause/resume.

  • Core idea: cancer is a disease of altered cell regulation and unchecked growth.
  • Hallmarks (conceptual): sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling immortality, angiogenesis, invasion/metastasis, reprogrammed metabolism, immune evasion.
  • Genetics + environment: interactions between variants and exposures shape risk.

  • Subclones: groups of tumor cells sharing specific mutations.
  • Selection: therapies and microenvironments favor some clones over others.
  • Heterogeneity: diversity inside the same tumor can drive resistance and relapse.
In the simulation: “fitness” is simplified; real tumors involve many interacting factors.

  • Non-cancer cells: immune cells, fibroblasts, blood vessels, matrix.
  • Signals: cytokines, growth factors, hypoxia alter behavior and therapy response.
  • Immuno-oncology: checkpoint inhibitors release immune brakes on anti-tumor T cells.

  • Therapies: surgery, radiation, chemo, targeted inhibitors, immunotherapy, hormone therapy, supportive care.
  • Resistance: pre-existing resistant clones or new changes after treatment.
  • Strategies: combination therapy, sequential therapy, adaptive dosing to contain resistant clones.
In the simulation: drugs simply reduce clone-specific fitness to illustrate selection ideas (no clinical guidance).

  • Access matters: screening, timely diagnosis, and supportive care aren’t equally available to all communities.
  • Risk reduction: vaccination (e.g., HPV), tobacco cessation, healthy environments, and culturally-tailored outreach help.
  • Trust & transparency: inclusive research/design improves outcomes for everyone.

Clone/Subclone: a group of cells with shared changes (mutations).

Fitness: how likely a cell is to survive and divide in its environment.

Resistance: reduced treatment effect on some cells/clones.

TME (Tumor Microenvironment): non-cancer components around the tumor that influence behavior.