Advantages of Cellnomica Software Suite (CSS)

The advantages of the Cellnomica Software Suite (CSS) can be viewed from several perspectives, including financial, scientific. Cellnomica Research is world leader in developmental systems biology and is at the cutting edge of research and development in the areas below:

Drug Discovery

The majority of genetic diseases are most likely regulatory diseases. This means the cause involves not defective genes or SNIPS, but mutations in the regulatory architecture of the genome. Modification of such architectures or pointed interference with their activity will be the goal of medical interventions. The agents of intervention and their delivery may not be traditional pharmacological drugs. Drug targets will be specific points in the regulatory, genomic network architecture, metabolic and signaling networks. Drugs in the past were systemic in their effects. The future will most likely involve targeted drugs.

To assess the effects of a drug on regulation a gene and more generally its interactions with other regulators and genes, software modeling of regulatory pathways have several advantages:

1. The effect of disturbing a pathway at a particular point can be seen immediately. If the software also models the effect of regulation on cellular interactions, cell growth and division (as CSS does) we also see the wider effects of the modified regulatory networks.
   

2. If we were to try to define or design a regulatory network by mutation on animal tissue in the lab, we would be faced with an enormous time consuming, resource consuming and expensive process that most likely would not be successful. Furthermore, even if we somehow produced a network through mutational guessing that appeared to satisfy our design goals, how would be ever be sure that the network was safe? How would we know it may not directly produce a cancer? With a lab approach, we are basically blind. We test for some cases that the system is safe, but we have no proof. If , however, we design the regulatory network in software and test it in software, we have both a proof of principle, debugging of the network and verification of safety all prior to beginning the actual lab work. Granted there may still be further side effects outside the network. But, at least inside the network we know it is safe and we can limit its output to known safe interactions.
   

3. Cancer is primarily a regulatory disease. It involves complex networks that include cell signaling, genome activation and mutations. A model theoretic approach that simulates such processes is the best and perhaps the only way to understand such processes. It also points the way to designing "drugs" that change the genome in such a way that the cells that contain that genome are no longer cancerous. Furthermore, we can test in silico effect of genomically modified networks in the surrounding tissue to make sure it does not generate unwanted side effects. Hence, a cure for cancer will involve the understanding pathological networks and design of curative agents that modify pathological networks. Such a methodology is very different from designing a drug that usually has unwanted systemic side effects on other tissues and sub cellular processes. Top

Drug Discovery
Designing MCOs
New Paradigm
A Test Bed
Animal Testing
Reduce Wet Lab Time
Predictive Modeling
As if scenarios
Tissue Engineering
Tissue Modeling
Tissue Regeneration
Organ genesis
Organ Modeling
Mutations
Theory Testing
Cancer Modeling
and Cancer Debugging
Drug Delivery
Toxicology
Shorten Drug Development
Speed Up Search
Understand Genomes
Genome Semantics
Reverse Engineer Genomes