ReviewIn silico multicellular systems biology and minimal genomes
Section snippets
Genome semantics or how does a chimpanzee differ from a human?
The genes of a chimpanzee and a human are virtually identical, therefore, it must be the regulatory genome architecture where the genomes and humans and chimpanzees will differ sufficiently to account for the differences in development, morphology and behavior.
The central problem of post-genomic biology and medicine is to understand the meaning of genomes. To understand genomes we need to view them in their biological context. This includes the context of the cell and the dynamic context of a
The systems biology hierarchy
Computational systems biology 8, 9, 10, 11, 12, 13, 14 can be described as a set of areas of research and modeling that fall into a hierarchy (Figure 1). In this hierarchy, all levels have an equal contributing value; for example, while the higher levels tend to be supported by the lower levels, a higher level might also be used to organize and give meaning to a lower level. To illustrate this, mutations in the genome, at a low level, are described in terms of their effects on the morphology at
Properties of systems
Ideally, in an analogous way to the relationship between phenomenological and statistical thermodynamics, systems biology will permit the modeling of properties at different levels of abstraction and ontology. The information required depends on the level of information that we are modeling. Each level of information has its own ontology of objects and relations. Thus, we can have models that are incomplete and yet accurate at a given level. For example, one might model the effects of homeotic
The semantic hierarchy and the meaning of genomes
To the hierarchy of levels modeling in systems biology, there corresponds a semantic hierarchy of levels of information and ontology. Furthermore, there is an information flow between these levels. The specification of the semantics of genomes requires that we understand how the cell interprets the genome in different contexts and levels. At the lowest level, we have the transcription into RNA and then translation of mRNA into proteins. At a higher level, proteins have particular functions
Problems with the bottom-up approach
Although there is a lot of data being generated in laboratories about the expression of genes, RNA and proteins, we can not interpret the data unless we have a semantics of the given genome. In other words, we need to understand how the genome functions in the multicellular system. How do we get the semantics? The dominant approach is bottom-up; we create mutations and observe the effects. However, genomes and their organisms are highly complex and, as a result, using a direct bottom-up search
In silico genomes
One way of reducing the search space for the semantics of genomes is to conduct in silico simulations of genomes that lead to multicellular phenomena that correspond to natural multicellular phenomena. In such an approach, the semantics of genomes is constrained by what we know from research in molecular biology, cell biology and genetics, as well as a century of experimentation in developmental biology. Modeling multicellular or cellular processes is, in essence, a process of theory
Minimal genomes and minimal cells
Complementary to a virtual, in silico approach that models the semantics of single cell and multicellular genomes, is the in vivo construction of genomic networks that regulate cell processes. The work by Venter and others to construct a minimal cell with a minimal genome is part of this effort 34, 35, 36, 37, 38, 39. Whereas Venter's group is trying to construct a minimal cell bottom-up, others 37, 38, 39, 40 are reducing simple bacterial genomes to find a minimal set of essential genes. The
Minimal multicellular genomes
If the construction of a minimal cell can be achieved, what happens next? I believe that the next step is to investigate the regulatory properties of minimal cells. After that it becomes possible to investigate minimal multicellular genomes (mMCG) and their organisms. An mMCG for a multicellular system is the simplest genome that is capable of generating that system. In other words, mMCGs generate mMCSs. An mMCS is a multicellular structure that develops from a single cell using a minimum of
Engineered mMCOs and drug discovery
This forward engineering process will open up new areas of biotechnology as well as multicellular pharmacodynamics (MCPD; see subsequent sections). In particular, networked MCPD might see great advantages in that an in silico model of an mMCO can then be tested and corrected by how the corresponding natural mMCO responds to a drug. One problem with the minimalist approach is that because the cell is minimal it might lack some of the drug responses of the normal cell. However, this is outweighed
Systems biology and drug discovery
As described previously, the genome can and should be interpreted at different levels of information and ontology. In the drug discovery process, there is also a realization by scientists that an ontological view restricted to the molecular level might hinder the drug discovery process. There is a movement to complement the molecular view with a systemic view in drug design 44. Regulatory networks require a systemic understanding. Although it is certainly true that there are powerful and
Multi-cellular pharmacodynamics
Pharmacokinetics is the study of the distribution of drugs in organs and tissue. Pharmacodynamics (PD) goes a step further and attempts to get at the causes and workings of ADMET properties 44. We propose to extend PD one step further.
Multicellular modeling of PD uses multicellular models where cells can be in diversely differentiated states. It uses multicellular tissue to model and dynamically simulate and display the effect of drug distribution and other ADMET properties and is a hybrid
Networked multicellular pharmacodynamics
Furthermore, MCPD models can be extended to model regulatory genomic networks together with signal transduction pathways, as part of a complex of interacting components in the cell; these are known as networked-MCPD (Net-MCPD) models. In this way, drug interactions with the cell, the genome, the cell signaling dynamics and the multicellular system can also become accessible to modeling. In this approach, many levels of the systems biology hierarchy are involved in modeling and simulation to the
In silico cancer modeling and simulation
For example, in cancer the regulatory networks in the genome and cell signaling dynamics can have a key role in the etiology, ontogenesis and dynamics of the disease. In this case, drug ADMET properties must be supplemented by additional properties that influence the dynamics of relevant cellular disease states. MCPD models enhanced with genome and cell signaling components might give us a deeper insight into the dynamics of cancers and their response to drugs in a dynamic multicellular context.
Conclusion
At present, drug discovery is still dominated by a bottom-up approach that mimics the flow of information dictated by the Central Dogma 47. However, there are inherent limitations with this approach because of the NP-complexity of the search space. Here, an alternative, multileveled approach has been proposed that includes in silico multicellular systems biology in tandem with in vivo forward and reverse engineering methods to analyze and design minimal genomes for mMCSs. A test bed for drug
References (47)
New computational approaches for analysis of cis-regulatory networks
Dev. Biol.
(2002)Promoters can contribute to the elucidation of protein function
Trends Biotechnol.
(2003)Modeling transcriptional control in gene networks–methods, recent results, and future directions
Bull. Math. Biol.
(2000)Mathematical modeling of gene networks
Neuron
(2000)Whole-cell simulation: a grand challenge of the 21st century
Trends Biotechnol.
(2001)- et al.
The Virtual Cell: a software environment for computational cell biology
Trends Biotechnol.
(2001) - et al.
Evolution of mutational robustness
Mutat. Res.
(2003) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Adv. Drug Deliv. Rev.
(2001)Comprehensive, comprehensible, distributed and intelligent databases: current status
Bioinformatics
(1998)Online genomics facilities in the new millennium
Pharmacogenomics
(2002)
Modeling transcriptional regulatory networks
Bioessays
Genomic Regulatory Systems: Development and Evolution
Systems biology: a brief overview
Science
Computational systems biology
Nature
Complexity and robustness
Proc. Natl. Acad. Sci. U. S. A.
Reverse engineering of biological complexity
Science
The challenges of in silico biology
Nat. Biotechnol.
What lies beyond bioinformatics?
Nat. Biotechnol.
Cluster analysis and promoter modelling as bioinformatics tools for the identification of target genes from expression array data
Pharmacogenomics
Genetic network modeling
Pharmacogenomics
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