Coleman Scientific Consulting

CSC Capabilities

CSC specializes in identifying, estimating and managing risks caused by microbes in the environment (air, buildings, soil and water) and foods from farm-to-table (production, processing and food service facilities). Our knowledge of dose-response relationships (Figure 1 below) and predictive microbiology (Figures 2 and 3 below) are essential to assessing risk. Analysis by CSC is changing old paradigms that misled the public and caused regulators to overspend resources with little or no benefit to human health protection.

CSC teams offer expert capabilities in the following multi-disciplinary areas:

  • • Microbial ecology and public health
  • • Risk analysis, biothreat assessments and exercises
  • • Biological and chemical engineering
  • • Modeling, mathematical statistics, and operations research
  • • Program development for conferences, training and workshops
  • • DHS, DOD/Army, EPA, FDA, and USDA program support

Dose-Response

Figure 1: Pathogen Dose-Human Response Analysis
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Predicting human response requires knowledge of the pathogen doses that cause illness or death. Ingestion of a single pathogen cell is extremely unlikely to cause illness in healthy humans. As doses of pathogens increase to hundreds, thousands, or millions of pathogens, the likelihood and severity of illness increases.

To predict human responses to pathogens, families of dose-response curves (Figure 1) are needed to account for variability in human resistance, pathogen virulence, and other factors influencing disease. People with healthy microbiomes (blue ovals, rightmost dose-response curve) can ingest significantly more pathogens before becoming ill than those with microbiomes disrupted by antibiotics (red).

Technical details are described in published scientific manuscripts and current work in preparation.

  • Coleman and Marks, 1998. Topics in dose-response modeling. J Food Protection 61:1550-1559
  • Coleman and Marks, 1999. Qualitative and quantitative risk assessment. Food Control 10:289-297
  • Coleman and Marks, 2000. Mechanistic modeling of salmonellosis. Quantitative Microbiology 2: 227-247

Predictive Microbiology

Figure 2: Pathogen Growth Analysis
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Predicting bacterial growth is simple for pure cultures under defined laboratory conditions. The E. coli O157:H7 growth curves in Figure 2 demonstrate the influence of experimental factors, predominantly initial numbers N(0) inoculated into nutrient media. Optimal growth (green text boxes) typically does not occur for foods. Suboptimal growth, particularly with low initial numbers, is more typical of foods. Growth in a food contaminated by low pathogen numbers (blue text boxes) and refrigerated for 5 days could be overpredicted by more than 4 orders of magnitude if the more optimal growth model (red text boxes) was selected for estimating potential risk for consumers.

Figure 3: Yogurt Microbiota Inhibits Pathogens
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Further, many foods have a natural microbiota or an added starter culture that outcompetes pathogens and protects against illness. The lactic acid bacteria in yogurt suppress growth of pathogens, including E. coli O157:H7 (Figure 3). Growth of low numbers of pathogens in non-sterile food (or in the human gut) is extremely unlikely. The conditions limiting pathogen growth, including temperature, initial numbers, and natural competition of the food microbiota, determine the model that best applies to your customers concerned about pathogen growth in foods and in or on the human body.

Technical details are described in published scientific manuscripts below.

  • Coleman et al, 2003. Impact of microbial ecology of meat and poultry products on predictions from exposure assessment scenarios for refrigerated storage. Risk Analysis 23:215-228
  • Coleman et al, 2003. Influence of agitation, inoculums density, pH, and strain on growth parameters for E. coli O157:H7 – Relevance to risk assessment. International J Food Microbiology 83:147-160

Click below for more information on past performance.

Past Performance